Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacological Treatment of Adult Attention-Deficit/Hyperactivity Disorder (ADHD) in a Longitudinal Observational Study: Estimated Treatment Effect Strengthened by Improved Covariate Balance

An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic [1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.
Towards Massively Parallel Large Eddy Simulation of Turbine Stages

The context of integrated numerical simulations of gas turbine engines by use of high-fidelity Computational Fluid Dynamic (CFD) tools recently emerged as a promising path to improve engines design and understanding. Relying on massively parallel super-computing such propositions still have to prove feasibility to efficiently take advantage of the ever increasing computing power made available worldwide. Although Large Eddy Simulation (LES) has recently proven its superiority in the context of the combustion chamber of gas turbine, methodologies need to be developed and start addressing the problem of the turbomachinery stages, if integrated simulations based on LES are to be foreseen. In the proposed work an in-house code and strategy, called TurboAVBP, is developed for turbomachinery LES thanks to the coupling of multi-copies of the unstructured compressible reacting LES solver AVBP, designed to run efficiently on high performance massively parallel architectures. Aside from the specificity of such wall bounded flows, rotor/stator LES type simulations require specific attention and the interface should not interfere with the numeric scheme to preserve proper representation of the unsteady physics crossing this interface. A tentative LES compliant solution based on moving overset grids method is proposed and evaluated in this work for high-fidelity simulation of the rotor/stator interactions. Simple test cases of increasing difficulty with reference numerical are detailed and prove the solution in handling acoustics, vortices and turbulence. The approach is then applied to the QinetiQ MT1 high-pressure transonic turbine for comparison with experimental data. Two configurations are computed: the first one is composed of 1 scaled stator section and 2 rotors while the second computation considers the geometrically accurate periodic quarter of the machine, i.e. 8 stators and 15 rotors to test scalability issues of such applications. Although under-resolved, the LES pressure profiles on the stator and rotor blades appear to be in good agreement with experimental data and are quite competitive compared to the traditional (Unsteady) Reynolds-Averaged Navier-Stokes (RANS or URANS) modeling approach. Unsteady features inherently present in these LES underline the complexity of the flow in a turbine stage and clearly demand additional diagnostics to be properly validated.Copyright © 2013 by ASME
Fractal Configurations of the Two- and Three-Dimensional Ising Models at the Critical Point

The fractal structure of the Ising model at the critical point Tc is studied in the present paper. The fractal dimensionality D of the total magnetization at Tc was estimated numerically as D = 1.86 ±O.Ol for the two-dimensional square lattice and D=2.46±O.Ol for the three-dimensional simple cubic lattice by Monte Carlo simulations. These values agree very well with the value D=1.875 obtained from the exact critical exponents and D=2.48 obtained from the known critical exponents, re­ ~pectively, through the relation D=d-fJ/v=(d+r/v)/2. This fractalness yields the hyperscaling relation dv=2fJ+r. It was also observed how the fractal nature of the relevant system disappears as the system deviates from the critical point. The dimensionality d of the relevant lattice is observed at temperatures lower than Tc and the random percolation value d/2 at higher temperatures.
A model with a unified kinematic hardening law for cyclic behavior of stiff clay

This paper presents a kinematic hardening model for describing some important features of natural stiff clays under cyclic loading conditions, such as closed hysteretic loops, smooth transition from the elastic behavior to the elastoplastic one and changes of the compression slope with loading/unloading loops. The model includes two yield surfaces, an inner surface and a bounding surface. A non-associated flow rule and a unified kinematic hardening law are proposed for the inner surface. The adopted hardening law enables the plastic modulus to vary smoothly when the kinematic yield surface approaches the bounding surface and ensures at the same time the non-intersection of the two yield surfaces. Furthermore, the first loading, unloading, and reloading stages are treated differently by applying distinct hardening parameters. The main feature of the model is that its constitutive equations can be simply formulated based on the consistency condition for the inner yield surface based on the proposed unified kinematic hardening law; thereby, this model can be easily implemented in a finite element code using a classic stress integration scheme as for the modified Cam Clay model. The simulation results on the Boom Clay, natural stiff clay, have revealed the relevance of the model: a good agreement has been obtained between simulations and the experimental results from the tests with different stress paths under cyclic loading conditions. In particular, the model can satisfactorily describe the complex case of oedometric conditions where the deviator stress is positive upon loading (compression) but can become negative upon unloading (extension).
Sustainable Tourism on Semau Island: Ready or not?

While community development is globalized highly through small island opportunities worldwide, Semau Island is a natural Island that has great potentials for the development of tourism. Related to sustainability, moving from those early studies about sustainable tourism, it can be stated that, sustainable tourism seeks to minimize negative impacts on the local culture and natural environment while generating benefits for local residents. The community and visitor attraction is closely related. It means that tourism is ready to be developed and sustained on the Island. Besides, the methods provide primary and discussion data about whether or not there is a relation between tourism development and its impacts on local community readiness in a wide range of projects and sectors. The report examines the readiness responses from visitor attractions, community response and linkage on tourism to create number of visitors through their satisfaction value for sustainable tourism in Semau Island.
NEAR-DUPLICATE REAL-LIFE FACE IMAGE a

Content-based image retrieval (CBIR) is an image retrieval method based on the analysis of numerical characteristics of the image at the absence of text information. An efficient similarity search in ultra-high dimensional space has been proposed using two-tier inverted file and Local Derivative Patterns (LDP) as feature extraction method with high accuracy and high performance on experimental face image data sets. However, real-life images have different size, resolution and a potential noise. It is unknown whether LDP would show the same satisfactory result given real-life image data sets. This research designed and developed a face search engine to find near-duplicate face in real life images using LDP method to extract image features and two-tier inverted file for multidimensional indexing process. A state expansion method was also introduced to capture more detailed description of image histogram by considering neighbor information. The experiment was performed on 8,083 reallife face images of various sizes between 20x20 to 80x80. The data set contained duplicate copies of face images with some transformation processes. The search result returned top 20 images which had the most similarity with the query images and had an average precision rate of 0.75 or 75%.
SURVEY ON SECURE AND TIME CONFINED IMAGE SHARING ON WEBSITES

: With the increasing volume of images users share through social sites, maintaining privacy has become a major problem. In light of these incidents, the need of tools to help users control access to their shared content is highly essential. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content, and metadata as possible indicators of users’ privacy preferences. We propose a two-level framework which according to the user’s available history on the site determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for image categories which may be associated with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features.
ADVANCED DOCUMENT MANAGEMENT SOLUTIONS FOR THE CONSTRUCTION INDUSTRY : THE CONDOR APPROACH

the paper gives a comprehensive overview of the European Esprit Condor project. The project provides a migration path from document-based to model-based approaches to information representation and structuring. The paper first presents the approach that was adopted to describe the business processes and document management practices of the Condor project end-users, and also to capture the requirements of the Condor system. The chosen approach made use of the IDEF0 and UML (Unified Modelling Language) methodologies. A Strategy towards the integration of the electronic document management systems used within the Condor project is then given. The paper also includes a comprehensive description of the overall Condor models that support the proposed system. Finally, a brief description of the business process re-engineering activity within Condor is given. The project is ongoing and supported by a user interest group, which involves representatives from a variety of non-construction industry companies all over Europe.
Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback

In many anomaly mining scenarios, a human expert veriﬁes the anomaly at-the-top (as ranked by an anomaly detector) before they move on to the next. This veriﬁcation produces a label—true positive (TP) or false positive (FP). In this work, we show how to leverage this label feedback for the top-1 instance to quickly re-rank the anomalies in an online fashion. In contrast to a detector that ranks once and goes ofﬂine, we propose a detector called OJR ANK that works alongside the human and continues to learn (how to rank) on-the-job , i.e., from every feedback. The beneﬁts OJR ANK provides are two-fold; it reduces ( i ) the false positive rate by ‘muting’ the anomalies similar to FP instances; as well as ( ii ) the expert effort by elevating to the top the anomalies similar to a TP instance. We show that OJR ANK achieves statistically signiﬁcant improvement on both detection precision and human effort over the ofﬂine detector as well as existing state-of-the-art ranking strategies, while keeping the per feedback response time (to re-rank) well below a second.
Covid-19 vaccination priorities defined on machine learning

ABSTRACT OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers
Replicative Competence of the T 131 I , K 141 E , and G 145 R Surface Variants of Hepatitis B Virus

Variants of hepatitis B surface antigen have been described in different clinical settings, but their replicative capacity in vitro has remained unexplored. Point mutations leading to sT131I, sK141E, and sG145R amino-acid substitutions were engineered by site-directed mutagenesis into an infectious plasmid clone of the virus. The mutated constructs were transfected into Huh7 cells, and their replication capacity was documented by LightCycler (Roche Diagnostics) measurements of virion-associated hepatitis B virus (HBV) DNA, intracellular relaxed circular double-stranded DNA, and pregenomic RNA. The sT131I and sG145R variants replicated with efficiency equal to that of the wild type, whereas the sK141E variant was replication impaired.
Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Causal Information as a Constraint on Similarity Permalink

Considerable evidence indicates that causal information provides a vital constraint on conceptual representation and coherence. We investigated the role of causal information as a constraint on similarity, exploiting an asymmetry between predictive causal reasoning (given the cause, predict the effect) and diagnostic causal reasoning (given the effect, diagnose the cause). This asymmetry allowed us to isolate the effects of causal understanding from the effects of sharing non-causal features. We found that judged similarity between two objects that are identical except for one feature was affected by whether that feature was a competing potential cause of an effect or an effect of a common cause. Causation Constrains Similarity Any two objects have indefinitely many features in common. For example, Murphy and Medin (1985) pointed out that the number of features that plums and lawn mowers have in common is, in principle, infinite. Both weigh less than 1000 kg, and both are found on earth, in the solar system. Both cannot hear well, have an odor, are used by people, not by elephants, and so on. But despite these shared features people do not generally consider plums and lawnmowers to be similar. Intuitively, the features plums and lawnmowers have in common are not considered important. But what features are important? Why are some features important and not others? There must be criteria for constraining the sheer number of these features (Goodman, 1972; Medin, Goldstone, & Gentner, 1993). Previous researchers (e.g., Murphy & Medin, 1985) have suggested that features will be considered more important when they are diagnostic of causal function and part of a larger explanatory framework. Whether or not a feature is causal serves as a criterion by which people select important features and separate them from unimportant ones (Sloman, Love, & Ahn, 1998). We propose a new paradigm to investigate the influence of causal knowledge on similarity and categorization. This paradigm provides an optimal way of equating the number of common and distinctive features between two objects across conditions: Equality is ensured because the stimuli were in fact identical across conditions. We use this paradigm to investigate the influence of causal knowledge as a constraint on similarity.
Language modality influences risk perception: Innovations read well but sound even better

Psychological theories implicitly assume that the modality in which information is conveyed—spoken or written—leaves judgment and choice unaltered. Modality is rarely considered in textbooks on judgment and decision making, and the selection of modality in research is often based on convenience. We challenge this theoretical assumption. Three experiments (N = 984) show that the modality in which novel technologies are described systematically influences their perceived risk and benefit. Participants either read or heard advantages and disadvantages of novel technologies and then assessed their risk and benefit. In Study 1, spoken descriptions prompted more positive evaluations toward the technologies in terms of overall risks and benefits than written descriptions. Studies 2 and 3 replicated this modality effect and demonstrated that affect partially explains it, as spoken descriptions induced more positive feelings toward the new technologies than written descriptions. Study 3 (preregistered) showed that the influence of modality is unique to novel technologies and does not extend to familiar ones. These findings contribute theoretically to the understanding of the relationship between language and thought, and carry implications for survey research and the use of voice assistant technology.
A Micro-Longitudinal Study of Naps, Sleep Disturbance, and Headache Severity in Women with Chronic Migraine

ABSTRACT Objective To examine the relationship between headaches, naps, and nocturnal sleep in women with chronic migraine (CM) using micro-longitudinal data from diaries and actigraphy. Methods 20 women with CM and 20 age and sex-matched healthy controls (HC) completed self-report questionnaires, electronic diaries, and wrist actigraphy over a 4-week period. Between-group comparisons were conducted with naps (frequency and duration) as the primary variable of interest. Within-group analyses were conducted on the CM group using hierarchical linear mixed models to examine the temporal relationships between headache severity, sleep behaviors, and sleep parameters. The primary variables of interest were naps (number and duration) and nocturnal sleep efficiency (diary and actigraphy). Results The CM group reported significantly more days with naps (25.85%) compared to the HC group (9.03%) during the study period (p = .0025). Within-group analyses in CM revealed that greater headache severity was associated with longer nap duration (p = .0037) and longer nap duration was associated with lower sleep efficiency measured using diaries (p = .0014) and actigraphy (p < .0001). Conclusions Napping is more frequent in CM than HC and nap duration in CM is associated with headache severity and nocturnal sleep disturbance. These findings provide initial support for the hypothesis that daytime napping is a behavioral coping strategy used in CM that could contribute to insomnia.
Shedding light on attitudes towards pregnancy among Inuit adolescents from Nunavik

ABSTRACT Better understanding attitudes toward pregnancy – a potent predictor of adolescent pregnancy – could help explain the high adolescent pregnancy rate in Nunavik, Canada. The objective of this study was to assess the distribution of different attitudes toward pregnancy and the factors associated with high pregnancy likelihood attitudes (HPLA; favourable, indifferent, and ambivalent), focusing on the perceived benefits of childbearing (BOC). T-tests, chi-square tests, and logistics regressions were performed based the answers of 159 Inuit women aged 16 to 20 years from the Qanuilirpitaa? survey. About 43% were ambivalent, 16% favourable, 5% indifferent, and 35% unfavourable toward pregnancy. Bivariate analysis indicate that the HPLA group was more likely to work, to report less frequent positive interactions, and to show a higher BOC score compared to others. Multivariate analysis show that an increased BOC score was associated with HPLA (OR = 1.09, 95% CI = 1.01 − 1.18). Perceiving that a baby would strengthen the relationship with the other parent (OR = 1.65, 95% CI = 1.15 − 2.37) and that it would help to access housing were individually associated with HPLA (OR = 1.45, 95% CI = 1.02 − 2.10). Findings provide evidence to support Inuit adolescents’ reproductive choices.
An assessment of present and future competitiveness of electric commercial vans

As electric vehicles appear as a potential solution for cleaner deliveries, several constraints affect the attractiveness of electric light commercial vehicles (eLCVs). Our research aims at identifying these constraints as well as quantifying their respective weight. We investigate two types of constraints: operational and economic. Operational constraints determine if an electric vehicle is suitable for a given use; for example, the limited range of operation due to the necessity to recharge the battery. Economic performance, which we examine through Total Cost of Ownership (TCO) computations for electric and conventional vehicles, sheds light upon the trade-offs faced by business users when they have to choose between several technologies. We then present the results of a disaggregated constraints analysis made on a French database about light commercial vehicles, which assesses the proportion of vehicles that could be replaced by electric ones, and at what costs. This study shows that, today, eLCVs are competitive for some specific uses, but do not cover the needs of every freight transport operator. Our analysis also shows that even if fuel prices remain low and financial incentives decline, the competitiveness of electric vehicles could grow in the future.
Sweet Impossible Movement Illusions

Past research has used the phi phenomenon to create the illusion of one object moving through another. This article presents three optical illusions that are conceptually similar, yet little known within academic psychology. Two of the illusions have been developed within the magic community and involve the performer appearing to make a finger jump from one hand to another and a cup penetrate through another cup. The article explores the factors underpinning these illusions and describes how these factors were used to enhance a similar illusion developed outside of magic (the penetration of one hand through another).