Miracle-dz/UniFAR-FTUs huggingface dataset:
tibiofibular graft still have a role in the treatment f lower-limb non-union ?

Introduction: Tibial non-union is a complication that poses a real challenge for surgeons. Several forms of treatment, depending on the type of non-union, have been described. The present study sought to assess results for treatment of tibial non-union by inter-tibiofibular graft (ITFG). Material and method: An exhaustive cohort study was performed on the files of 33 patients: 25 male, 8 female; mean age, 44 years. Twenty cases involved high-energy trauma. Twenty-four were open fractures. Twenty-two concerned diaphyseal fracture, 10 of which were complex segmental. Eleven concerned distal fracture, including 4 complete articular fractures. There were 17 cases of septic non-union. There were no cases of severe bone defect. ITFG was performed at a mean 8.7 months post-trauma, as first-line treatment in 30 cases and in second line in 3. Results: Thirty-one patients showed bone consolidation, at a mean 7.2 months. The 2 failures resulted from technical error. Trauma kinetics emerged as a risk factor for failure. Discussion: ITFG remains a useful treatment option in tibial non-union, whether infected or not. The present results are comparable with those of the literature. Although the present series comprised only tight non-union, a study of the literature showed that ITFG can treat bone defects up to 4 or 5 cm. Functional results showed tibiotalar joint stiffening, due more to immobilization and non-weight-bearing than to syndesmosis. ITFG thus remains relevant to the treatment of tibial non-union. Level of evidence: IV, retrospective study. © 2016 Published by Elsevier Masson SAS.
Machine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems

We investigate the performance of multi-user multiple-antenna downlinks via joint antenna selection and power control design. In order to fully exploit the spatial diversity while minimizing the energy consumed by active radio frequency (RF) modules, a subset of antennas are selected to serve the users. Firstly, we propose a joint antenna selection and power allocation (JASPA) algorithm to maximize the system sum rate subjected to the total transmit power constraint and quality of service (QoS) requirements. JASPA copes with the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner iteration successively optimizes the transmit power followed by an outer loop that tries all valid antenna combinations. Although approaching the global optimality, JASPA suffers a combinatorial complexity, which might limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and power allocation (L-ASPA) which significantly reduces the high computational time of JASPA while retaining comparative performance. The core idea behind L-ASPA is to exploit the advances in machine learning to establish underlaying relation between the key system parameters and the selected antennas. The effectiveness of the proposed algorithms is demonstrated via numerical results, which show that JASPA could achieve 90% of the optimal performance while reducing more than 93% computation time.
Deep Bayesian Neural Networks for Continual Learning

In the problem of continual learning, tasks are given in sequence and the goal of the machine is to learn new tasks while retaining previously learned tasks’ performances. Even though deep neural networks have become prevalent among machine learning techniques recently, they may fail to deal with continual learning environment. As the model learns new tasks, parameters contributing largely to previous tasks’ performances may change and result in poor performance. This phenomenon is called catastrophic forgetting, and researchers tackled this problem using regularizers and structure optimization techniques. We aim to show that applying Bayesian modelling to deep neural network is beneficial for continual learning. Not only does Bayesian framework provide systematic way of performing online learning, it also provides uncertainty estimates for measuring each parameter’s contribution to previously learned tasks’ performances. By rescaling gradients applied to parameters with large performance contribution, the model can retain performances to previous tasks longer. This thesis shows how deep Bayesian neural networks utilize model uncertainty, which leads to alleviation of catastrophic forgetting in continual learning environment.
Tanshinone IIA attenuates demyelination and promotes remyelination in A. cantonensis-infected BALB/c mice

Background: Angiostrongylus cantonensis infection can cause demyelination in the central nervous system, and there is no effective treatment. Methods: We used dexamethasone, Tanshinone IIA (TSIIA) and Cryptotanshinone(Two traditional Chinese medicine monomers) in combination with albendazole (AB, a standard anti-helminthic compound) to observe their therapeutic effect on demyelination in A. cantonensis-infected mice. Luxol fast blue staining and electron microscope of myelin sheath, Oligodendrocyte (OL) number and myelin basic protein (MBP) expression in brain was detected in above groups. Results: TSIIA+AB facilitated OL proliferation and significantly increased both myelin sheath thickness and the population of small-diameter axons. In addition, TSIIA treatment inhibited the expression of inflammation-related factors (interleukin [IL]-6, IL-1β, tumor necrosis factor [TNF]-α, inducible nitric oxide synthase [iNOS]) rather than inhibiting eosinophil infiltration in brain. TSIIA also decreased microglial activation and shifted their phenotype from M1 to M2. Conclusions: Taken together, these results provide evidence that TSIIA combined with AB may be an effective treatment for demyelination caused by A. cantonensis infection and other demyelinating diseases.
A Comparative Study of GUI Automated Tools for Software Testing

— Nowadays, a main resort for delivering software with good enough quality is to design, create, implement and maintain test cases that are executed automatically. This could be done on many different levels, however graphical user interface (GUI) testing is the closest one to the way the real user interacts with the software under test (SUT). The aim of this paper is to determine the most popular GUI automated tools for software testing among a list of 52 candidates and compare them according to their features, functional and non-functional characteristics.
Converting Class 2 Algorithms to Class 1 Algorithms

While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.
Advances in Geosciences Integration of water balance models in RIVERTWIN

In the project RIVERTWIN climate, hydrologic, groundwater and water quality models are integrated in order to evaluate river basin management plans established for the implementation of the EU Water Framework Directive. In such integrated models, which try to simulate all relevant processes in a river basin realistically, modelling of the water balance plays a key role. Therefore the integration of hydrological and groundwater models requires special attention. In this case study, the hydrological model simulates discharge and daily groundwater recharge in a high spatial resolution. Using the latter as input, the groundwater model calculates groundwater levels and groundwater runoff, which is then returned to the hydrological model. Such integration on the meso-scale brings up new problems such as commensurability, verification and compatibility of internal state variables and fluxes, but also provides the possibility to analyse the underlying assumptions and simplifications. As an example of this modelling approach the simulation of groundwater recharge, groundwater levels and groundwater runoff in the Neckar catchment are discussed and the problems of the current integration concept are described.
F 2 Image processing

F2—1.1 CT image data X-ray computed tomography (CT) has become a standard clinical tool for a wide range of diagnostic and therapeutic purposes. It allows to obtain three-dimensional (3D) structural and functional details of organs. Compared to traditional two-dimensional (2D) X-ray (Figure 1a), CT eliminates the superimposition of structures in the image and thus allows medical experts to visually distinguish and localize tissue differences in 3D space. CT images display the so-called radiodensity of the tissue according to the mean X-ray attenuation in any given voxel (3D pixel). Hence, a voxel may in general represent a mixture of tissues, a phenomenon called the partial volume effect. The severity of this effect is alleviated by increasing the resolution. Current CT scanners in clinical use achieve resolutions of up to about 150 microns. In general, higher resolution (and better contrast) means – unfortunately – higher radiation dose exposure for the patient. Additionally, radiocontrast agents may be in-
Foam Making Sense behavioral additive deposition and stigmergic agency for integrated surface tectonics

This thesis research deals with the architectural project from an interdisciplinary point of view, integrating biomimetics, additive fabrication, computer vision, and robotics. The work focuses on the feedback interaction loop among robotic additive fabrication, a stigmergic agent-based system and the self-organizing properties of the material. The aim is to explore the morphological, constructive and expressive potentials generated by the mutual influence of computational design, construction behavioral rules, and physical material behavior (whose complexity exceeds current simulation capacity). The proposed approach leads to the creation of surface-based tectonics, enhanced with a fiberglass-coated dendritic ridge formation that integrates functional, ornamental and structural performances. The process can be extended to larger architectural scales with the creation of bespoke EPS molds via robotic hot wire cutting; the presented case study leverages the aforementioned process on ruled surfaces for the generation of translucent delimiters, used to create heterogeneous spatial organization.
1-CONSEQUENCES OF UNPREDICTABLE CHRONIC MILD STRESS ( UCMS ) AS A MODEL OF DEPRESSION ON THE PHYSICAL AND BEHAVIOURAL PARAMETERS OF MALE SWISS MOUSE

The current study was carried out to investigate the role of unpredictable chronic mild stress as a model of depression on the physical and behavioral parameters of male Swiss mice. TwentyFive adult male Swiss albino mice were housed in either control group or unpredictable chronic mild stress (UCMS) group that was exposed to different physical stressors for 8 weeks. Body weight and body coat were assessed. Sucrose consumption test, reward maze test, elevated plus maze and forced swim test were conducted. UCMS mice displayed a reduction in body weight, and decreased sucrose consumption, while exhibited an increase in the latency to pass 1 st gate of the reward maze. UCMS mice also showed a tendency of being in the closed arm of the elevated plus maze for a longer duration compared to mice in the control group, a tendency of being immobile in forced swim test. In conclusion, physical changes in conjunction with the significant behavioral changes strongly support the efficacy of UCMS as a model of depression in the human being.
Real-time and high precision 3D shape measurement method

In this paper, a unique 3D shape measurement method for measurement of nearly plain car body parts is proposed. The main advantage of the system is the high accuracy (about 0.1 mm), the high flexibility and the real-time processing. The primary application of the method is the 100% inspection of press formed car parts in the factory since in these cases the high accuracy and the real-time feature are indispensability. In order to achieve suitable measurement accuracy the camera specifications were carefully inspected. As result, a new type of gray-level marker and a new technique was proposed by the authors for compensating the time variability of marker central points in the camera images. In order to achieve real-time processing, the correspondence matching was devised. Furthermore, since the proposed method is based on processing camera images, i.e. the detection of feature-points on the target (such as screw holes) is also supported. The measured feature-points can easily be compared to CAE data.
Position Paper , Tagging , Taxonomy

In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., “tags”) to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems. Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photosharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.
Chapter 2 The Convergence Process and the Volatility of the Money and Capital Markets

This study investigates the degree of economic integration between Cyprus and the ten new accession countries and the European Union by using advanced econometric techniques. More specifically, the analysis looks at the financial and economic convergence between the above mentioned groups of countries. With respect to the financial convergence, we examine convergence by looking at Bond returns and the capitalization-to-GDP ratio. With respect to economic (real) convergence we examine the per capita GDP. Our results show that Cyprus does not show any degree of convergence when taking into account the capitalization-to-GDP ratio. Of course, this is a general phenomenon, in the sense that we find no convergence, even with respect to the old member states. As far as bond returns are concerned, we do find some partial convergence between Cyprus and the other groups of countries examined. Looking at the per-capita GDP, the results are also disappointing in the sense that again we find no proof of any convergence between the countries involved. In the light of the above mentioned results, one should expect the European Union to take additional measures in order to achieve faster integration (financial and real) between member states. 2.
Correlation Between Mentors and Learners Perceptions of Mentors Behaviors in Completing Online

The advent of completing doctoral dissertations in an online environment is a natural extension of the technological process changes in today’s world. This study focused on determining the relationship between e-mentors and learners’ perceptions of competencies utilizing Schichtel’s theory of competency model. The need for the study is shown by low retention rates of 60-70% for doctoral students when completing their dissertations. The results indicate there is no relationship between e-mentors and learners’ perception of competencies. The results help to explain major difficulties in understanding learners’ satisfaction with the online dissertation process and the low retention rates of completing dissertations.
Internal and external predictors of fruit and vegetable consumption in children

This thesis explored internal and external predictors of fruit and vegetable intake in children of different age groups. The first sample were toddlers between 2-3 years old and the second sample were children between 5-9 years old. Intake of fruit was analysed separately from intake of vegetables, and subgroups of fruit and vegetables with strong sensory properties were also analysed separately. The results showed that in the older sample lifetime exposure to variety of different fruit and vegetables was positively associated with quantity and diversity of fruit and vegetables consumed. In toddlers, liking of fruit and vegetables was not directly linked to their intake. In children of less controlling mothers fruit liking was positively associated with fruit intake and children of the more controlling mothers ate fruit independent of fruit liking. Past history of middle ear infections may affect adiposity and fruit and vegetable intake. Finally, individual levels of sensitivity to sweet taste may affect adiposity and intake of cruciferous vegetables and non-astringent fruit in school-age children. Parental intake was the strongest predictor of intake in both age groups. The results of this thesis show that fruit and vegetables have different predictors of intake and need to be considered separately.
IMPLEMENTATION OF CRYPTOGRAPHICALLY ENFORCED RBAC

RBAC (Role-based access control) is an efficient method for sharing objects between different groups of users. If RBAC is implemented using ABE (Attribute Based Encryption), then the resulting system enforces access control indirectly, through cryptography. We propose a new multi-user system based on Cryptographically Enforced RBAC. To improve performance, our system combines existing work on Cryptographically Enforced RBAC with symmetric cryptography. From the best of our knowledge, we are the first to implement and experimentally evaluate the feasibility of such a system, which was previously only analyzed theoretically. We describe the architecture of our system, its implementation and evaluate performance. Our solution can be used to implement secure storage in clouds where the service provider is untrusted.
Coupling issues in monthly forecasting Frédéric Vitart European Centre for Medium-Range Weather Forecasts Reading

The goal of the present paper is to discuss if the current configuration of the OGCM, which is coupled to the atmospheric model after day 10, is suitable for monthly forecasting. This OGCM has the same configuration as the OGCM used in seasonal forecasting system 3 (Anderson et al, 2007). This configuration has been designed mostly for the prediction of ENSO. This means that this oceanic model is capable of resolving ocean large scale dynamics, but the processes in the oceanic mixed-layer are poorly represented, because of the low vertical resolution in the upper ocean.
The IFRS Adoption Index : A Tool for the Measurement of Accounting Harmonisation

The worldwide harmonisation of accounting represented implementation of the International Financial Reporting Standards responds to demand of capital markets for high-quality, comparable and useful accounting information. The benefits of accounting harmonisation are well known. The research evidence an increased usefulness of financial statements prepared in accordance with IFRS in each adopting country. However, the successful adoption of IFRS brings a huge challenge to all sorts of empirical research focusing on the role of IFRS adoption on certain economic variables. When analysing effect of the IFRS on “country-bycountry level”, the researchers have to control somehow for the fact that IFRS are spread almost over all around the world. Except for some cases, all important world economies have already adopted the IFRS, which are required or allowed for the use by listed companies and even by non-listed companies in certain jurisdictions. To make valid comparison among countries, a sophisticated model evaluating different level of the IFRS adoption has to be developed. The aim of paper is to show some theoretical approaches to solving this newly arising important research issue. Key-Words: Accounting harmonisation; IFRS; Adoption of IFRS; Harmonisation de jure; Harmonisation de facto; IFRS Adoption Index
Determining Relevant Variables and Interactions in Credit Scoring Data with Neural Network Pruning and Rule Extraction ( Full Paper Submission )

Credit scoring data contain discrete and continuous variables that are used in building models to differentiate between good and bad credit risks. Neural networks (NN) are known to produce good results when applied to analyze these data. We propose the introduction of interaction terms between the continuous variables to improve the NN accuracy and to simplify the classification rules extracted from the networks. By explicitly adding interaction terms, pruned NN with high accuracy and few connections are obtained from our test data. The few connections in the networks allow us to describe the classification process as concise sets of rules.
Candida restingae and Kodarnaea anthophila , three new related yeast species from ephemeral flowers

Department of Animal Science, University of New England, Armidale, NSW 2351, Australia 3 Department of Biology, Three new yeast species were discovered during studies of yeasts associated with ephemeral flowers in Brazil, Australia and Hawaii. Their physiological and morphological similarity to Kodamaea (Pichis) ohmeri suggested a possible relationship to that species, which was confirmed by rDNA sequencing. Kodamaea nitidulidarum and Candida restingae were found in cactus flowers and associated nitidulid beetles in sand dune ecosystems (restinga) of Southeastern Brazil. Over 350 strains of Kodamaea anthophila were isolated from Hibiscus and morning glory flowers (Ipomoea spp.) in Australia, and from associated nitidulid beetles and Drosophila hibisci. A single isolate came from a beach morning glory in Hawaii. Expansion of the genus Kodamaea to three species modified the existing definition of the genus only slightly. The type and isotype strains are as follows: K. nitidulidarum strains UFMG96-272T (h+; CBS 849IT) and UFMG96-394’ (h-; CBS 8492’); Candida restingae UFMG96-276T (CBS 84933; K. anthophila strains UWO(PS)95-602.IT (h+; CBS 84943, UWO(PS)91-893.2’ (h-; CBS 8495’) and UWO(PS)95-725.1’ (h-; CBS 8496’).