Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Unmet Health Needs in Primary Health Care Services in a Representative Sample of the Greek Population †

Unmet health care needs are determined as the difference between the services judged necessary and the services actually received, and stem from barriers related to accessibility, availability and acceptability. This study aims to examine the prevalence of unmet needs and to identify the socioeconomic and health status factors that are associated with unmet needs. A cross-sectional study was conducted in Greece in 2010 and involved data from 1,000 consenting subjects (>18 years old). Multiple binary logistic regression analysis was applied to investigate the predictors of unmet needs and to determine the relation between the socio-demographic characteristics and the accessibility, availability and acceptability barriers. Ninety nine participants (9.9%) reported unmet health needs during the 12 months prior to the research. The most frequently self-reported reasons were cost and lack of time. Youth, parenthood, physician consultations, and poor mental health increased the likelihood of unmet needs. Women were less likely to report accessibility and availability than acceptability barriers. Educational differences were evident and individuals with primary and secondary education were associated with significantly more accessibility and availability barriers compared with those with tertiary education. Unmet health needs pose a significant challenge to the health care system, especially given the difficult current financial situation in Greece. It is believed that unmet health needs will continue to increase, which will widen inequalities in health and health care access.
Spatial Heterogeneity in Tree Diversity and Forest Structure of Evergreen Broadleaf Forests in Southern China along an Altitudinal Gradient

We studied the influence of altitude on the spatial heterogeneity of tree diversity and forest structure in a subtropical evergreen broadleaf forest in southern China. Significant positive correlation was found between tree species diversity, basal area and altitude, but negative correlation between slenderness of trunks and altitude. According to topography, tree species diversity, diameter at breast height, height and basal area increased from ridges to valleys, while slenderness and stem density did not differ significantly with topography. Redundancy analysis (RDA) was employed to clarify relationships between tree species diversity and environmental factors (topography and soil water holding capacity). Topography and water conditions jointly explained 57.8% of the diversity variation. Tree species diversity was significantly correlated with altitude, slope and bulk density (Monte Carlo permutation test with 999 permutations, p < 0.05). A positive relationship existed between altitude, soil non-capillary porosity and diversity, while slope, aspect and soil water content were the opposite. Our results show that soil water holding capacity has a positive effect on maintaining species diversity. When comparing with topographic factors—the main driving forces affecting the pattern of tree species diversity—the effect of soil water holding capacity was weak.
Influence of Phonon Dispersion on Transient Thermal Response of Silicon-on-Insulator Transistors Under Self-Heating Conditions

Lattice Boltzmann method (LBM) simulations of phonon transport are performed in one-dimensional (1D) and 2D computational models of a silicon-on-insulator transistor, in order to investigate its transient thermal response under Joule heating conditions, which cause a nonequilibrium region of high temperature known as a hotspot. Predictions from Fourier diffusion are compared to those from a gray LBM based on the Debye assumption, and from a dispersion LBM which incorporates nonlinear dispersion for all phonon branches, including explicit treatment of optical phonons without simplifying assumptions. The simulations cover the effects of hotspot size and heat pulse duration, considering a frequency-dependent heat source term. Results indicate that, for both models, a transition from a Fourier diffusion regime to a ballistic phonon transport regime occurs as the hotspot size is decreased to tens of nanometers. The transition is characterized by the appearance of boundary effects, as well as by the propagation of thermal energy in the form of multiple, superimposed phonon waves. Additionally, hotspot peak temperature levels predicted by the dispersion LBM are found to be higher than those from Fourier diffusion predictions, displaying a nonlinear relation to hotspot size, for a given, fixed, domain size.
Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized  $\varepsilon$-Nash Equilibria

We consider linear quadratic Gaussian (LQG) games in large population systems where the agents evolve according to nonuniform dynamics and are coupled via their individual costs. A state aggregation technique is developed to obtain a set of decentralized control laws for the individuals which possesses an epsiv-Nash equilibrium property. A stability property of the mass behavior is established, and the effect of inaccurate population statistics on an isolated agent is also analyzed by variational techniques.
Kalman smoothing based channel estimation for space-time block coding

This paper addresses the problem of channel estimation and tracking for space-time block coding. The coding/decoding technique allows using Kalman smoothing in estimating time-selective fading channels. In the simulations it is shown that this technique can preserve the orthogonality property of the decoding matrix even for time-selective channels. A realistic channel model described in COST 207 project is used in our examples.
Clouds On-Off Klimate Intercomparison Experiment (COOKIE)

1. Synopsis Cloud-radiative eects are expected to control many aspects of the current and future climates, ranging from the large-scale circulation of the atmosphere and intra-seasonal variability to climate sensitivity and precipitation projections. However, investigations so far have been carried out using individual models and various methodologies. To identify robust eects and facilitate physical understanding, the role of clouds in climate and climate change needs to be investigated in a more coordinated way and through a wider range of model congurations and complexity. This provides the scientic motivation for the Clouds On O Klima Intercomparison Experiment (or COOKIE for short) proposed here. This COOKIE proposal, which is based on parameter sensitivities and analysis frameworks explored and developed as part of the EUCLIPSE project, uses simple atmosphere-only simulations, where is some experiments clouds are made transparent to radiation. The experimental protocol is centered around six additional experiments to the CFMIP contribution to CMIP5, three thirty-year AMIP and three ve-year Aqua planet Experiments, thus comprising a total of 105 years of atmosphere only simulations. Many of the experiments, and the aordability of the proposed computations, lend them
Remodeling of Tight Junctions and Enhancement of Barrier Integrity of the CACO-2 Intestinal Epithelial Cell Layer by Micronutrients

The micronutrients zinc, quercetin, butyrate, indole and berberine were evaluated for their ability to induce remodeling of epithelial tight junctions (TJs) and enhance barrier integrity in the CACO-2 gastrointestinal epithelial cell culture model. All five of these chemically very diverse micronutrients increased transepithelial electrical resistance (Rt) significantly, but only berberine also improved barrier integrity to the non-electrolyte D-mannitol. Increases of Rt as much as 200% of untreated controls were observed. Each of the five micronutrients also induced unique, signature-like changes in TJ protein composition, suggesting multiple pathways (and TJ arrangements) by which TJ barrier function can be enhanced. Decreases in abundance by as much as 90% were observed for claudin-2, and increases of over 300% could be seen for claudins -5 and -7. The exact effects of the micronutrients on barrier integrity and TJ protein composition were found to be highly dependent on the degree of differentiation of the cell layer at the time it was exposed to the micronutrient. The substratum to which the epithelial layer adheres was also found to regulate the response of the cell layer to the micronutrient. The implications of these findings for therapeutically decreasing morbidity in Inflammatory Bowel Disease are discussed.
Statistical and logical reasoning in disambiguation

While recent statistical methods for disambiguation in natural language processing have been very successful, earlier arguments still hold for the position that fully successful disambiguation requires reasoning to new conclusions from old facts. We explore ways of complementing statistical approaches with the use of ‘domain theories’: collections of facts and axioms that characterize the typical structure of some task domain. In particular, we hypothesize that disambiguation decisions can supply tacit information about such theories, and that the theories can, in part, be automatically induced from such data. We describe a pilot experiment in which a partial domain theory for the domain of air–travel information was induced from a corpus of disambiguated example sentences, the resulting theory then being used successfully in disambiguating other sentences from the same domain.
A Heuristic for the Short-Term Planning of Multi-Product Continuous Plants

We consider the short-term production planning of multi-product continuous plants. In the literature this problem is generally modeled as a large-size monolithic mixed-integer linear or nonlinear program. In this paper we follow a decomposition approach which partitions the problem into an operations planning and an operations scheduling problem. The operations planning problem consists in assigning the production tasks to the processing units, fixing the operating conditions of the tasks, and computing the number of operations executed for each task. This problem can be formulated as a nonlinear program of moderate size containing only one integer variable. The solution to the operations planning problem provides a set of operations, which have to be scheduled on the processing units. For this operations scheduling problem we present a novel mixedinteger linear programming formulation as well as a fast priority-rule based scheduling method. In contrast to the classical monolithic approaches, our new method is able to find good feasible schedules for large-scale instances within less than five seconds.
An Analysis of Implicit Social Networks in Multiplayer Online Games

—For many networked games, such as the Defense of the Ancients and StarCraft series, the unofﬁcial leagues created by players themselves greatly enhance user-experience, and extend the success of each game. Understanding the social structure that players of these games implicitly form helps to create innovative gaming services to the beneﬁt of both players and game operators. But how to extract and analyse the implicit social structure? We address this question by ﬁrst proposing a formalism consisting of various ways to map interaction to social structure, and apply this to real-world data collected from three different game genres. We analyse the implications of these mappings for in-game and gaming-related services, ranging from network and socially-aware matchmaking of players, to an investigation of social network robustness against player departure.
Institutional perspective on emerging industry development

Purpose – The purpose of this paper is to elucidate the dynamic interactions and co‐evolution of institutions with the technology and organization fields in emerging industry development. Insights and inspirations from comparison of the triangle relationship among government, market and local community in different institutional contexts could contribute to possible institutional innovation in the context of large‐scale institutional transition. In this way, this paper is expected to offer insights to the development of emerging industries in China.Design/methodology/approach – The paper reviews the focal literature focusing on institutional change and the co‐evolution of institution, industry and technology. A multi‐level conceptual framework is put forward to explain the mechanism for the co‐evolution of technology, organization and institution. A multi‐case comparison method was applied to compare and disclose the process of co‐evolution of institutions, and the technology and organizational fields, as.
APPROACHES IN INVESTIGATING ROMANIA’S IMAGE AS A TOURIST DESTINATION AMONG THE TURKISH STUDENTS

The image of the tourist destination plays an essential role in the decision making process regarding the choice of a holiday. Image has a crucial role in the success of the destination because it influences the consumers’ satisfaction and helps in drawing up a promotion strategy for the positioning/repositioning on a certain market, branding/rebranding the tourist destination. Information from different sources contribute to the formation of the image of a certain destination, information which can be distributed into: the promotion performed by the destination; the other’s opinions (direct or indirect); mass-media and the popular culture. We intended to investigate Romania’s image a a tourist destination among the Turkish students. The results showed that the students had very poor knowledge of our country.
Biologically Active and Antimicrobial Peptides from Plants

Bioactive peptides are part of an innate response elicited by most living forms. In plants, they are produced ubiquitously in roots, seeds, flowers, stems, and leaves, highlighting their physiological importance. While most of the bioactive peptides produced in plants possess microbicide properties, there is evidence that they are also involved in cellular signaling. Structurally, there is an overall similarity when comparing them with those derived from animal or insect sources. The biological action of bioactive peptides initiates with the binding to the target membrane followed in most cases by membrane permeabilization and rupture. Here we present an overview of what is currently known about bioactive peptides from plants, focusing on their antimicrobial activity and their role in the plant signaling network and offering perspectives on their potential application.
Soft/hard focalization in the EEG inverse problem

We present in this paper a novel statistical based focalized reconstruction method for the underdetermined EEG (electroencephalogram) inverse problem. The algorithm is based on the representation of non-Gaussian distributions as an infinite mixture of Gaussians (IMG) and relies on an iterative procedure consisting out of alternated variance estimation/linear inversion operations. By taking into account noise statistics, it performs implicit spurious data rejection and produces robust focalized solutions allowing for straightforward discrimination of active/non-active brain regions. We apply the proposed reconstruction procedure to average evoked potentials EEG data and compare the reconstruction results with the corresponding known physiological responses
Towards Blended Reactive Planning and Acting using Behavior Trees

In this paper, we show how a planning algorithm can be used to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment. The planning part of the algorithm is based on the idea of back chaining. Starting from a goal condition we iteratively select actions to achieve that goal, and if those actions have unmet preconditions, they are extended with actions to achieve them in the same way. The fact that BTs are inherently modular and reactive makes the proposed solution blend acting and planning in a way that enables the robot to effectively react to external disturbances. If an external agent undoes an action the robot reexecutes it without re-planning, and if an external agent helps the robot, it skips the corresponding actions, again without replanning. We illustrate our approach in two different robotics scenarios.
A block-based approach to adaptively bias the weights of adaptive filters

Adaptive filters are crucial in many signal processing applications. Recently, a simple configuration was presented to introduce a bias in the estimation of adaptive filters using a multiplicative factor, showing important gains in terms of mean square error with respect to standard adaptive filter operation, mainly for low signal to noise ratios. In this paper, we modify that scheme to obtain further advantages by splitting the adaptive filter coefficients into non-overlapping blocks, and employing a different multiplicative factor for the coefficients in each block. In this way, bias vs variance compromise is managed independently in each block, allowing an enhancement if the energy of the unknown system is non-uniformly distributed. In order to give some insight on the behavior of the scheme, a theoretical analysis of the optimal scaling factors is developed. In addition, several sets of experiments are included to widely study the new scheme performance.
PREDICTION MARKETS AS A MEDICAL FORECASTING TOOL: DEMAND FOR HOSPITAL SERVICES

This paper presents the outcome of a study conducted at the Royal Devon and Exeter Hospital in which a prediction market was established in order to forecast demand for services. To the researcher's knowledge, it does not appear that prediction markets have been previously utilized in a healthcare environment. The purpose of this study is to provide evidence for the effective use of prediction markets in a healthcare environment. The study was conducted over a period of one week, and involved sixty-five participants. Each was asked to provide an estimate for demand for services at the Royal Devon and Exeter Hospital. Characteristics gathered for each participant included level of education, occupation, directorate, number of years worked for the hospital, and number of years worked for the National Health Service. The study confirms the effectiveness of prediction markets to forecast future events as overall hospital demand was forecasted with an error of only 0.3%. The prediction market was less successful in predicting demand for services for each department, which the researcher attributes to the small sample size and lack of diversity of participants. Additionally, only a very small percentage of the characteristics captured registered a statistically significant correlation with the accuracy of the estimate. Further studies should focus on different characteristics and/or use a larger sample size to either confirm or refute the existence of such characteristics. The findings of this work could potentially be used as an innovative way to augment the forecasting function for a wide range of healthcare facilities. With the preliminary success of this study to forecast demand, further research in the field is warrantedClassification-JEL: L83
Managing Break-The-Glass using Situation-oriented authorizations

The patient's life is a redline in Healthcare environments. Whenever it comes to danger, such environments reject static authorizations . A common problem "Break The Glass" is known as the act of breaking the static authorization in order to reach the required permission. Healthcare environment is full of different contexts and situations that require the authorizations to be dynamic. Dynamic Authorization is a concept of giving the choice to E-Health authorization system to choose the most suitable permission by considering one's situation. This paper aims at preventing the matter of modifying the policy to make authorizations dynamic. It introduces a simple solution to provide Dynamic Authorization by orienting the authorization system decision using situations. Situations, which are calculated using Complex Event Processing, are integrated to XACML architecture. A Healthcare example proves the efficiency of our approach.
Introducing the GLMSELECT PROCEDURE for Model Selection

This paper describes the GLMSELECT procedure, a new procedure in SAS/STAT software that performs model selection in the framework of general linear models. This procedure supports a variety of model selection methods, including the LASSO method of Tibshirani (1996) and the related LAR method of Efron et al. (2004). The procedure enables selection from a very large number of effects (tens of thousands) and offers extensive capabilities for customizing the selection with a wide variety of selection and stopping criteria. INTRODUCTION When faced with a predictive modeling problem that has many possible predictor effects—dozens, hundreds, or even thousands—a natural question is “What subset of the effects provides the best model for the data? ” Statistical model selection seeks to answer this question, employing a variety of definitions of the “best” model as well as a variety of heuristic procedures for approximating the true but computationally infeasible solution. The GLMSELECT procedure implements statistical model selection in the framework of general linear models. Methods include not only extensions to GLM-type models of methods long familiar in the REG procedure (forward, backward, and stepwise) but also the newer LASSO and LAR methods of Tibshirani (1996) and Efron et al. (2004), respectively. Note that while the model selection question seems reasonable, trying to answer it for real data can lead to problematic pitfalls, including • Only one model is selected, and even that is not guaranteed to be the “best”; there may be other, more parsimonious or more intuitively reasonable models that may provide nearly as good or even better models, but which the particular heuristic method employed does not find. • Model selection may be unduly affected by outliers. • There is a “selection bias” because a parameter is more likely to be selected if it is above its expected value than if it is below its expected value. • Standard methods of inference for the final model are invalid in the model selection context. To some degree, these pitfalls are intrinsic, and they have even led some experts to stridently denounce model selection. However, certain features of GLMSELECT, in particular the procedure’s extensive capabilities for customizing the selection and its flexibility and power in specifying complex potential effects, can partially mitigate these problems. The main features of the GLMSELECT procedure are as follows: • Model Specification – offers different parameterizations for classification effects – supports any degree of interaction (crossed effects) and nested effects – supports hierarchy among effects – provides for internal partitioning of data into training, validation, and testing roles • Selection Control – provides multiple effect selection methods 1 Statistics and Data Analysis SUGI 31