Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and performance of the variable-wavelength BonseHart ultra-small-angle neutron scattering diffractometer KOOKABURRA at ANSTOThis article will form part of a virtual special issue on advanced neutron scattering instrumentation, marking the 50th anniversary of the journal

The double-crystal ultra-small-angle neutron scattering (USANS) diffractometer KOOKABURRA at ANSTO was made available for user experiments in 2014. KOOKABURRA allows the characterization of microstructures covering length scales in the range of 0.1–10 mm. Use of the firstand secondorder reflections coming off a doubly curved highly oriented mosaic pyrolytic graphite premonochromator at a fixed Bragg angle, in conjunction with two interchangeable pairs of Si(111) and Si(311) quintuple-reflection channel-cut crystals, permits operation of the instrument at two individual wavelengths, 4.74 and 2.37 Å. This unique feature among reactor-based USANS instruments allows optimal accommodation of a broad range of samples, both weakly and strongly scattering, in one sample setup. The versatility and capabilities of KOOKABURRA have already resulted in a number of research papers, clearly demonstrating that this instrument has a major impact in the field of large-scale structure determination.
Hierarchical Learning of Motor Skills with Information-Theoretic Policy Search

The key idea behind information-theoretic policy search is to bound the ‘distance’ between the new and old trajectory distribution, where the relative entropy is used as ‘distance measure’. The relative entropy bound exhibits many bene-ﬁcial properties, such as a smooth and fast learning process and a closed-form solution for the resulting policy. In this paper we will summarize our work on information theoretic policy search for motor skill learning where we put particular focus on extending the original algorithm to learn several options for a motor task, select an option for the current situation, adapt the option to the situation and sequence options to solve an overall task. Finally, we illustrate the performance of our algorithm with experiments on real robots.
A Developmental Approach to Learning Affordances

Originating in the psychology literature, the concept of affordances has found wide use in the area of autonomous robotics. Viewed as relations between the agent’s action choices and their effects on the state of the environment, affordances allow agents to represent and reason about their interactions with the environment. In designing procedures which allow agents to learn these affordances by directly interacting with objects in the environment, researchers aim to tackle the challenge of uncertainty posed by the occurrence of novel objects and situations encountered in complex, real-world domains. In this paper, we present a new approach to learning affordances. The proposed approach distinguishes itself from existing methods by emphasising an incremental and developmental approach to the learning of affordances tied directly to the task for which affordances are being learned, i.e., for problem solving in the agent’s operating environment. By tightly coupling the learning of affordances with the planning component, and by generally restricting learning to those affordances which are immediately relevant for achieving current goals, the proposed approach aims to allow agents to efﬁciently tackle problems which may be beyond current capabilities while at the same time allowing for continual accumulation of newer and more advanced skills.
Understanding The Role Of IoT Technologies In Supply Ecosystems

This paper introduces the concept of supply ecosystems for exploring the role of the Internet of Things (IoT) and its technologies in value added activities. The paper argues that the IoT has a role in helping to understand the information problems that firms face, and in identifying the way in which IoT technologies can help. To achieve this, the paper examines the vision and scope of the IoT and its perceived potential value to firms. Then, based on the available literature, the paper furthers the existing concept of supply ecosystems and argues that the potential value of the IoT to businesses can only be realized if a holistic, more ecological perspective, is used. The paper hence proposes that supply ecosystems can be used to understand the value of IoT in solving information problems
Trends in Studying Emotions

– The purpose of this introductory study is to briefly sketch the main developmental trends in the psychological investigation of emotions within the past several decades. The topics elaborated include theoretical conceptualizations and research strategies in observation, measurement, and in the content analysis of affective phenomena. In this endeavor, only the topics of human, normal, and adult dynamics of affective phenomena are considered. Discernable changes and stable viewpoints, as well as some perennial problems in the study of human emotions are summarily reviewed in their developmental perspective.
Are donors targeting the greatest health needs ? Evidence from mining sites in the D . R . Congo

I examine the effectiveness of donors in targeting the highest burden of malaria in the Democratic Republic of Congo when health information structure is fragmented. I exploit local variations in the burden of malaria induced by mining activities as well as financial and epidemiological data from health facilities to estimate how local aid is matching local health needs. Using a regression discontinuity design, I find significant but quantitatively small variations in aid to health facilities located within mining areas. Comparing local aid with the additional cost of treatment and prevention associated with the increased risk of malaria transmission, I find suggestive evidence that local populations with the highest burden of the disease receive a proportionately lower share of aid compared to neighbouring areas with reduced exposure to malaria infection. The evidence of disparities in the allocation of aid for malaria supports the view that donors may have inaccurate information about local population needs.
TANTALUM (Ta) AND NIOBIUM (Nb) CONTAINING ALLOY POWDERS FOR APPLICATION IN ADDITIVE MANUFACTURING

INTRODUCTION Additive manufacturing (AM) processes such as laser or electron beam powder-bed-fusion (PBF-LB or PBF-EB), and directed energy deposition (DED), enable production of parts with a high degree in freedom of design allowing e.g., topology optimization, or complex structures that open new opportunities in wide range of applications.1,2 In addition to the fact that the AM process allows the creation of new designs that are not realizable by subtractive manufacturing, the materials applied can contribute further with their intrinsic properties to optimize mechanical, biological, or chemical parameters of AM parts.3,4 While the metal AM material landscape is still mainly dominated by steel or titanium alloys with applications being limited by their intrinsic material properties, new material candidates can further expand the scope of AM technology.5,6 Tantalum and niobium are characterized by high melting points, superior corrosion resistance,7 superconductivity,8 and a high level of biocompatibility.9 Moreover, they are β-phase stabilizers in Ti alloys. These properties make them interesting candidates for applications such as biomedical implants, superconductors, high temperature components (e.g., engine parts), or parts for the chemical processing industry.10–12 While Ta/Nb and their alloys are already applied in many of these industries, the combination of their properties with the freedom of design offered by AM processing technologies is of vital importance for the realization of new and optimized solutions. Moreover, applying AM processes for Ta/Nb materials is also beneficial for cost aspects as, for example, the number of parts for assembly can be reduced by AM, or the performance of parts can be optimized due to the realization of more complex structures and geometries. Accordingly, a major field of research activity to push existing performance beyond current boundaries has been developed
Mechanism and Constitutive Model of a Continuum Robot for Head Stabilization in Cancer Radiation Therapy

We present a parallel robot mechanism and the constitutive laws that govern the deformation of its constituent soft actuators. Our ultimate goal is the real-time motion-correction of a patient’s head deviation from a target pose where the soft actuators control the position of the patient’s cranial region on a treatment machine. We describe the mechanism, derive the stress-strain constitutive laws for the individual actuators and the inverse kinematics that prescribes a given deformation, and then present simulation results that validate our mathematical formulation. Our results demonstrate deformations consistent with our radially symmetric displacement formulation under a finite elastic deformation framework.
A COMPARATIVE STUDY OF DEMAND FOR LOCAL AND FOREIGN WINES IN BULGARIA

We use a semantic differential based instrument to assess the main factors in wine demand attitudes of Bulgarian customers. We aim to find out whether specific marketing techniques could alter the demand for such a traditional product as wine in Bulgaria. Given the fact that Bulgarian customers only recently received the possibility to taste imported wines, we find that foreign wines are evaluated mainly through the prejudice of past knowledge, not linked to the actual product. We discover a rigidity in such a prejudice and adhering to "standard" definitions of "prestige" and quality of wines. Most likely the demand for wine in Bulgaria will continue to be focused on local products. Cross-Cultural Management Journal Volume XV, Issue 3 (29) / 2013
OpenWHEEL i3R-A new architecture for clearance performance

This article describes the development of a sequential geometric model in order to control our allterrain robot OpenWHEEL i3R and refine the robustness of its climbing strategy. This robot is a 4wheel mobile robot based on an articulated chassis that allows to overcome obstacles up to two thirds of the height of its centre of gravity by warping its central joint. The kinematics and a motion strategy in 19 steps have been jointly validated on two small-scale and one full-scale prototype. Permanent static stability is guaranteed during climbing, by actuating only the central joint of the robot, in addition to the wheels. This principle of minimal actuation, and some other particularities of the geometric model, are presented in this work.
STUDY OF CLPG GENE EXPRESSION LEVELS IN LOCAL AWASSI LAMBS IN IRAQ

The present study has been conducted to estimate mRNA expression level of Callipyge (CLPG) gene in Local Awassi Lambs in Iraq. It was divided into two groups (Group1: No. 31, age 4 months and live body weight below 30 kg.Group2: No. 24, age 6 months, and live body weight more 30 kg). The results of the study showed the presence of expression of the CLPG gene at a rate of 8.7065 in favor of the first group (Group1) versus the second group (Group2) according to the weight group and age. We conclude from the study the presence of expression of the CLPG gene according to the weight and age group, and further study may be conducted to ascertain the association of this expression with the growth and carcass meat quality traits.
GASIFICATION PILOT PLANT

In the field of hydrogen production through coal gasification for distributed power generation, Sotacarbo, an Italian limited company which operates in the field of clean coal technologies, is developing different research and development projects for the tuning of a coal-to-hydrogen process configuration. Toward this goal, a flexible pilot platform was built in 2007-2008, and it is currently in operation. In particular, the platform includes demonstrative and pilot air-blown fixed-bed gasifiers, the latter equipped with a flexible syngas treatment line for a combined power generation and CO2free hydrogen production. This chapter presents a detailed description of all the experimental equipment and the main results obtained during the first 700 hours of tests in the pilot unit. In particular, the optimum operating conditions with a low sulphur South African coal have been defined, with a syngas production of about 43 Nm/h (from the gasification of 8 kg/h of coal), characterized by a lower heating value of 4.5 MJ/kg, and a high cold gas efficiency, of about 97%. A series of experimental tests has also been carried out in order to assess the plant performance under different operating conditions and the effects of the main parameters, such as air/fuel and steam/fuel mass ratios and oxidant composition (air, eventually enriched in oxygen, or mixtures of oxygen and carbon dioxide). Moreover, a series of preliminary tests has been carried out in order to evaluate the gasification performance with a portion of high sulphur Sulcis coal or biomass (wood pellets) in the fuel blend. The experimental results obtained in syngas treatment processes show a very efficient removal of pollutants (in particular, a final concentration of sulphur compounds lower than 10 ppm, in volume, can be obtained, even with high sulphur coal in the fuel * Corresponding author: apettinau@sotacarbo.it Alberto Pettinau, Francesca Ferrara and Carlo Amorino 2 blend) and the possibility to reduce carbon dioxide emissions of about 90% by using a single-stage amine-based carbon capture system, which assures a CO2 absorption efficiency of about 99%. Finally, the guidelines of the future research activities are described in this chapter, with particular reference to the further optimization of both gasification and syngas treatment processes in different operating conditions.
TensorForest: Scalable Random Forests on TensorFlow

We present TensorForest, a highly scalable open-sourced system built on top of TensorFlow for the training and evaluation of random forests. TensorForest achieves scalability by combining a variant of the online Hoeffding Tree algorithm with the extremely randomized approach, and by using TensorFlow’s native support for distributed computation. This paper describes TensorForest’s architecture, analyzes several alternatives to the Hoeffding bound for per-node split determination, reports performance on a selection of large and small public datasets, and demonstrates the beneﬁt of tight integration with the larger TensorFlow platform
Deep Reinforcement Learning with Temporal Abstraction and Intrinsic Motivation

Learning goal-directed behavior in environments with sparse feedback is a major challenge for re-inforcement learning algorithms. The primary difﬁculty arises due to insufﬁcient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake, which could eventually help them solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for ﬂexible goal speciﬁcations, such as functions over entities and relations, leading to efﬁcient exploration in complicated environments. We demonstrate the strength of our approach on a problem with very sparse, delayed feedback – the classic ATARI game ‘Montezuma’s Revenge’.
A FOUNDATION FOR INDUCTIVE REASONING IN HARNESSING THE POTENTIAL OF BIG DATA

Training programs for statisticians and data scientists in healthcare should give greater importance to fostering inductive reasoning toward developing a mindset for optimizing Big Data. This can complement the current predominant focus on the hypothetico-deductive reasoning model, and is theoretically supported by the constructivist philosophy and Gestalt theory. Big-Data analytics is primarily exploratory in nature, aimed at discovery and innovation, and this requires fluid or inductive reasoning, which can be facilitated by epidemiological concepts (taxonomic and causal) as intuitive theories. Pedagogical strategies such as problem-based learning (PBL) and cooperative learning can be effective in this regard. Empirical research is required to ascertain instructors’ and practitioners’ perceptions about the role of inductive reasoning in Big-Data analytics, what constitutes effective pedagogy, and how core epidemiological concepts interact with the evidence from Big Data to produce outcomes. Together these can support the development of guidelines for an effective integrated curriculum for the training of statisticians and data scientists First published February 2020 at Statistics Education Research Journal Archives
PORTABLE AND WEARABLE PARTICULATE MATTER (PM) SENSORS FOR CHARACTERIZATION OF AEROSOLS AND CONTROL OF PM USING PHOTOIONIZATION ENHANCED CORONA SYSTEMS

Knowledge of the PM distribution is vital for identifying hotspots, estimating exposure and eventual health impacts, and validating transport models. Different aerosol mechanistic processes result in spatial heterogeneity and temporal variation, important aspects for human exploration and habitation of lunar surfaces. Conventional aerosol instrumentation can accurately measure the PM concentration at fixed locations at a relatively high cost. However, they tend to have poorer spatio-temporal resolution. Due to these reasons and the bulky sizes, these instruments are not the most practical for use in outer space.
Artiﬁcial Intensity Remapping: Learning Multimodal Image Descriptors without Multimodal Image Data

We present a novel, simple, and aggressive data augmentation strategy called Artiﬁcial Intensity Remapping (AIR). AIR artiﬁcially generates challenging but realistic training input to improve generalization of learning, making a trained model more effective even for adversarial input. In building a deep-learning-based image descriptor model, experiments show that our model trained using only non-multimodal data with AIR outperforms state-of-the-art algorithms not only for non-multimodal data but also for multimodal data. This multimodal testing demonstrates the beneﬁts of AIR in addressing the fact that successful performance in deep learning, with its huge number of parameters, may result from overﬁtting to a given dataset.
FireCircle: GRNET’s approach to advanced network security services’ management via bgp flow-spec and NETCONF

Security, and in particular reactive protection against network attacks and anomalies, is a major concern of modern networks. This paper presents “FireCircle”, a novel security provisioning service that allows the administrators of a downstream network to inject firewall-like rules at their upstream level and mitigate attacks there; under certain circumstances these rules can be further propagated so that attacks are mitigated even closer to their sources. FireCircle has been designed and implemented by GRNET Network Operations Center, using hardware that supports RFC5575 (a.k.a BGP flow-spec), open-source tools and platforms and software developed in-house. The BGP nature of the service makes it quick, scalable, manageable, secure and multidomain. FIreCircle adds to this mechanism a web administrative and user interface, a programmatic API, strong and scalable authentication mechanisms (Shibboleth), advanced and flexible authorization schemes and self-protection anti-measures, all packed under an open-source licence. These features allow the service not only meet its primary goals, that is the easy mitigation of attacks by personnel with medium networking expertise and low-end equipment, but also open potentials for integration with network anomaly detection tools and promote synergies between peering domains or cooperating security teams. This paper covers the aforementioned points, providing details about the architecture, the implementation, the deployment on GRNET production network and the future plans of the service. Also, this paper discusses how FireCircle can be extended into a multidomain environment, taking the extended GEANT community as a case study.
CIA_A_239186 133..140

Lisanne Tap 1 Lisanne J Dommershuijsen Andrea Corsonello Fabrizia Lattanzio Silvia Bustacchini 2 Gijsbertus Ziere 1 Jan LCM van Saase Francesco US Mattace-Raso 1 1Section of Geriatric Medicine, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; 2Italian National Research Center on Aging (INRCA), Ancona, Fermo and Cosenza, Italy; 3Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands Purpose: Aortic stiffness (AS) is associated with cardiovascular events and all-cause mortality in the older population. AS might also influence the health-related quality of life (HRQOL) as a result of the negative effects of AS on cognitive and physical morbidity. We aimed to investigate the possible association between AS and HRQOL in people aged 75 years and over. Patients and Methods: This cross-sectional study was part of the SCOPE study, an international multicenter cohort observational study. The indicators for AS were aortic pulse wave velocity (aPWV) and central pulse pressure (cPP). HRQOL was assessed using the EQ-5D index and the EQ-5D visual analog scale (VAS). ANCOVA and multivariate regression models were used to investigate possible associations. Results: We included 280 Dutch participants of the SCOPE study. Median age was 79 years (IQR 76–83) and 42.1% were women. Participants reporting any problem on the EQ-5D index (n=214) had higher values of aPWV (12.6 vs 12.2 m/s, p = 0.024) than participants not experiencing any problem (n=66) and comparable values of cPP (44.4 vs 42.0 mmHg, p = 0.119). Estimates only slightly changed after adjustments. No association was found between indicators of AS and EQ-5D VAS. Conclusion: Aortic stiffness was associated with impaired quality of late life. This association could be mediated by subclinical vascular pathology affecting mental and physical health.
Serbia: A new process for waste rubber and plastic recycling

This paper intends to describe a new technological process for waste rubber and plastic recycling up to the commercial components in safe environmental friendly way. Researches and all relevant technical-technological data related to this process are checked at constructed pilot plant. The future construction of these units for waste rubber and plastic recycling will allow interested parties to achieve the environmental effectiveness and economic efficiency.
The Crisis as a Wake-Up Call: Do Banks Tighten Screening and Monitoring During a Financial Crisis‘

To what extent was the credit contraction during the global financial crisis due to more intense screening and monitoring by banks? We address this question by analyzing changes in the structure of a large number of syndicated loans to private, non-financial corporations. We find an increase in retention rates among syndicate arrangers during the crisis that we cannot explain by borrower risk or interbank liquidity alone. This increased 'skin in the game' is especially pronounced when information asymmetries between the borrower and the lending syndicate - or within the syndicate - are high. This indicates that the reduction in bank lending during the crisis was at least partly caused by stricter bank screening and monitoring: a wake-up call.