Miracle-dz/UniFAR-FTUs huggingface dataset:
Prescription pain reliever misuse and levels of pain impairment: 3-year course in a nationally representative outpatient sample of US adults

Background The primary aim of this work was to present the prevalence data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a representative 3-year longitudinal survey (ages 18+ years) that captured information on patterns of self-reported pain interference and prescription pain reliever misuse. A second aim was to assess the degree to which the risk of various types of opioid misuse (onset, desistance, and incidence of dependence) was related to the longitudinal course of self-reported pain interference over the 3-year period. Methods We used a two-wave, nationally representative sample of adults (aged 18+ years) in which the baseline data were collected during 2001–2002 and a single follow-up was obtained ~3 years later (2004–2005 with 34,332 respondents with complete data on study variables for both waves). Results Our findings indicated that ~10% reported high pain interference in the past month at each wave. There was tremendous stability in levels of pain, with ~5% reporting consistent levels of high impairment over the 3-year study, a proxy for chronic pain. Levels of pain were more strongly associated with prescription pain reliever misuse concurrently rather than prospectively, and the association was largely linear, with the likelihood of misuse increasing with levels of pain. Finally, health service factors were also prominent predictors of onset, but not the outcomes, of desistance or transitions to problem use. Conclusion This study is the first to use a nationally representative sample with measures of pain and drug use history collected over an extended period. These results may help provide clinicians with an understanding that the risk of misuse is greatest when pain is active and may help guide the selection of appropriate intervention materials and monitor strategies for those at greatest risk.
Advances in 3D Model Identification from Stereo Data

Volumetric (CAD) models of objects are converted to a wire frame representation which is compiled into a data base and represented in vectors and matrices that characterise both local geometrical relationships and the structure of the models. This characterisation is independent of position and orientation and supports variable size. Using a stereo vision system, instances of these objects are then identified from pairs of images containing single objects or more than one object.
Development of methods for accurate and efficient analysis of periodic structures

Periodic structures model a wide variety of problems in engineering, including periodic microwave structures such as phased arrays, frequency selective surfaces (FSS), and filters. While the fundamental theory of periodic structures is well-established, modeling demands require increased attention to calculation efficiency, accuracy and error control to model novel periodic structures such as metamaterials, electromagnetic bandgap (EBG) structures, and artificial micro- and nano-structured materials. The paper summarizes recent developments in computing periodic layered media Green's functions that expand the applicability and improve the computation speed, accuracy, and error control of these calculations.
Functional Outcomes and Complications After Primary Spinal Surgery for Scoliosis in Adults Aged Forty Years or Older: A Prospective Study With Minimum Two-Year Follow-up

Study Design. A prospective study. Objective. Our purpose was to evaluate prospectively the complications, clinical outcomes, and self-reported quality of life in a relatively homogenous group of adults aged ≥40 years undergoing primary surgical treatment for scoliosis. Summary of Background Data. Relatively few reports have examined surgical outcomes in adult patients with scoliosis, especially adults aged ≥40 years, whose outcomes may differ because of more rigid curves and more frequent and severe comorbidities. Although most studies have shown patient benefits despite high complication rates after such surgery, most were retrospective and conducted before the introduction of third-generation instrumentation techniques. Methods. We prospectively studied a consecutive series of 35 patients of age ≥40 years (average age, 56.3 years) undergoing primary surgery for scoliosis by one surgeon. Most of our patients (86%) had at least one comorbidity. We collected complete radiographic measurements and Oswestry Disability Index, Short Form 36, and Scoliosis Research Society 22 questionnaires before surgery and at each follow-up, and recorded the number and type of complications. Outcomes were assessed in the context of complications, degree of correction, and procedure characteristics to detect significant (P < 0.05) correlations. Results. The overall complications rate was 49%; 26% of the patients had a major complication and 31% had a minor one. There were no deaths. Coronal curve correction was 30.8° (61%) on average. There were statistically significant postoperative improvements in Oswestry Disability Index, Short Form 36, and Scoliosis Research Society 22 scores. Patients whose fusions ended at L4 or L5 showed greater improvements in some of the Short Form 36 component scores than patients whose fusions involved the sacrum (P = 0.041). There were no significant differences in outcomes related to presence of complications or operative staging. Conclusion. Adults ≥40 years with symptomatic scoliosis benefit from surgical treatment, despite the high complication rate.
Cloudets: Cloud-based cognition for large streaming data

Big data cognition has become a dominant problem in interactive visual analytics for event detection and response, metereology, cosmology, and large smart city applications including traffic monitoring and management, search and rescue operations, crowd management and logistics. The main problems are mainly due to big data volume and velocity and, in some cases, variety in both dimension and type. A practical approach to understanding and viewing big data features is through streaming operations. Streaming allows for both volume and velocity characteristics of big data, and often, for variety as well. However, performing analytics at interactive rates is currently an open challenge in most big data applications. Cloud computing platforms provide practical support and leverage to solving some of the big data and visual analytics problems, especially when dealing with the volume and velocity characteristics of current data generation. In order to interact with streaming data patterns in an elastic cloud environment, we present a new elastic framework for big data visual analytics in the cloud, the Cloudet. The Cloudet is a self-adaptive cloud-based platform that treats both data and compute nodes as elastic objects. The main objective is to readily achieve the scalability and elasticity of cloud computing platforms in order to process large streaming data and adapt to potential interactions between data stream features. Our main contributions include a robust cloud-based framework, the Cloudet, which can flexibly process the streaming data and applications to illustrate the setup and operations of this framework. The framework includes a cloud profile manager that attempts to optimize the cloudet parameters in order to achieve expressivity, scalability, reliability, and the proper aggregation of the data streams into several density maps for the purpose of dynamic visualization of data features.
Petri Nets Formalization of Map/Reduce Paradigm to Optimise the Performance-Cost Tradeoff

Nowadays, the world around us is built up on more and more unstructured data every day. However, performing a longitudinal analysis of these data becomes a Big-Data problem that cannot be tackled with traditional tools, storage or processing infrastructures. One of the main contributions to address this matter has been the Hadoop framework (which implements the Map/Reduce paradigm), especially when used in conjunction with Cloud computing environments. This paper presents a formalization of the Map/Reduce paradigm which is used to evaluate performance parameters and make a trade-off analysis of the number of workers versus processing time and resource cost. We have used Prioritised -- Timed Coloured Petri Nets to obtain complete and unambiguous models of the system behaviour as well as CPNTools to evaluate the correctness of the system using state space exploration and for performance evaluation. The resulting formal model is evaluated with a real social media data Hadoop-based application and it is validated by carrying out experiments on a real private Cloud environment. Results show that the proposed model enables to determine in advance both the performance of a Map/Reduce-based application within Cloud environments and the best performance-cost agreement.
Visualization tool for cyber-physical maintenance systems

Intelligent Maintenance Systems (IMS) are data acquisition/analysis systems for predictive maintenance. The IMS provide autonomously (or semi-autonomously) diagnoses, prognostics and health assessment of components. In these systems, factors such as data acquisition and visualization from equipments are very important. In this context, this study has used a Cyber-Physical Systems (or CPS) approach to consider the various aspects present into a maintenance environment. The CPS is a new paradigm that seeks to combine and coordinate physical and computational elements. In this paper, we propose a 2D/3D visualization tool for cyber-physical maintenance environments. This tool works under HTTP protocol and makes possible the real-time visualization and remotely access, allowing users to view, to edit and to access the maintenance data via web browser.
Atmospheric GCM Response to Extratropical SST Anomalies: Synthesis and Evaluation*

The advances in our understanding of extratropical atmosphere‐ocean interaction over the past decade and a half are examined, focusing on the atmospheric response to sea surface temperature anomalies. The main goal of the paper is to assess what was learned from general circulation model (GCM) experiments over the recent two decades or so. Observational evidence regarding the nature of the interaction and dynamical theory of atmospheric anomalies forced by surface thermal anomalies is reviewed. Three types of GCM experiments used to address this problem are then examined: models with fixed climatological conditions and idealized, stationary SST anomalies; models with seasonally evolving climatology forced with realistic, time-varying SST anomalies; and models coupled to an interactive ocean. From representative recent studies, it is argued that the extratropical atmosphere does respond to changes in underlying SST although the response is small compared to internal (unforced) variability. Two types of interactions govern the response. One is an eddy-mediated process, in which a baroclinic response to thermal forcing induces and combines with changes in the position or strength of the storm tracks. This process can lead to an equivalent barotropic response that feeds back positively on the ocean mixed layer temperature. The other is a linear, thermodynamic interaction in which an equivalent-barotropic low-frequency atmospheric anomaly forces a change in SST and then experiences reduced surface thermal damping due to the SST adjustment. Both processes contribute to an increase in variance and persistence of low-frequency atmospheric anomalies and, in fact, may act together in the natural system.
REDD+ in dryland forests: issues and prospects for pro-poor REDD in the miombo woodlands of southern Africa.

International exploration is underway for ways to achieve Reduced Emissions from Deforestation and forest Degradation, and enhanced forest carbon stocks, in developing countries (REDD+). Implementing REDD+ will involve providing sufficient incentives to land users and a supportive policy, legal and institutional environment. Community-based natural resources management (CBNRM) in the miombo ecoregion of east and southern Africa has been grappling with these issues for many years, with considerable success. The lessons from this experience provide a basis on which REDD+ in dry-land forests can build.
Movement control algorithms for realization of fault-tolerant ad hoc robot networks

Autonomous and semi-autonomous mobile multirobot systems require a wireless communication network in order to communicate with each other and collaboratively accomplish a given task. A multihop communications network that is self-forming, self-healing, and self-organizing is ideally suited for such mobile robot systems that exist in unpredictable and constantly changing environments. However, since every node in a multihop (or ad hoc) network is responsible for forwarding packets to other nodes, the failure of a critical node can result in a network partition. Hence, it is ideal to have an ad hoc network configuration that can tolerate temporary failures while allowing recovery. Since movement of the robot nodes is controllable, it is possible to achieve such fault-tolerant configurations by moving a subset of robots to new locations. In this article we propose a few simple algorithms for achieving the baseline graph theoretic metric of tolerance to node failures, namely, biconnectivity. We formulate an optimization problem for the creation of a movement plan while minimizing the total distance moved by the robots. For one-dimensional networks, we show that the problem of achieving a biconnected network topology can be formulated as a linear program; the latter lends itself to an optimal polynomial time solution. For two-dimensional networks the problem is much harder, and we propose efficient heuristic approaches for achieving biconnectivity. We compare the performance of the proposed algorithms with each other with respect to the total distance moved metric using simulations.
Physical Explanation of Archie's Porosity Exponent in Granular Materials: A Process‐Based, Pore‐Scale Numerical Study

The empirical Archie's law has been widely used in geosciences and engineering to explain the measured electrical resistivity of many geological materials, but its physical basis has not been fully understood yet. In this study, we use a pore‐scale numerical approach combining discrete element‐finite difference methods to study Archie's porosity exponent m of granular materials over a wide porosity range. Numerical results reveal that at dilute states (e.g., porosity ϕ > ~65%), m is exclusively related to the particle shape and orientation. As the porosity decreases, the electric flow in pore space concentrates progressively near particle contacts and m increases continuously in response to the intensified nonuniformity of the local electrical field. It is also found that the increase in m is universally correlated with the volume fraction of pore throats for all the samples regardless of their particle shapes, particle size range, and porosities.
Comparative Physico-Chemical Characterization of the Mucilages of Two Cactus Pears (Opuntia Spp.) Obtained from Mekelle, Northern Ethiopia

Cactus is a plant that naturally grows in tropical and semi-tropical regions of the world. The composition of this mucilage is believed to differ from species to species. Hence, comparative study was carried out on mucilages of Opuntia ficus-indica (OFI) and Opuntia stricta (OS) as regard to their physico-chemical properties and acute toxicity levels. The study indicated that solubility of the dry mucilages were comparable and increased as raise in temperature. However, at all temperature levels the swelling powers were significantly higher in mucilage of OS than that of OFI. At 100% RH the moisture sorption property of OFI (95.4%) was higher than that of OS (76.9%). The pH values of both mucilages at 12% dispersions were found to be 5.57 and 5.87 for OFI and OS, respectively. The conductivity at the same concentration, 12% (w/v), of OFI was 13.12 mS/cm while that of OS was 9.31 mS/cm. The apparent viscosities at 12% (w/v) were 9,017 mPas and 10,060 mPas for OFI and OS, respectively. The apparent viscosities of the dispersions decreased with increase in shear rates which rendered the dispersions a pseudoplastic flow. The surface tension of the aqueous dispersions of OFI (28.71 mN/M) decreased significantly as compared to that of OS (39.7 mN/M). The results of the study proved that the mucilage of OS was superior to OFI mucilage for use as food and pharmaceutical excipients. Moreover, both mucilages exhibited low acute toxicity levels.
Efficient and adaptive epidemic-style protocols for reliable and scalable multicast

Epidemic-style (gossip-based) techniques have recently emerged as a class of scalable and reliable protocols for peer-to-peer multicast dissemination in large process groups. However, popular implementations of epidemic-style dissemination suffer from two major drawbacks: 1) Network overhead: when deployed on a WAN-wide or VPN-wide scale, they generate a large number of packets that transit across the boundaries of multiple network domains (e.g., LANs, subnets, ASs), causing an overload on core network elements such as bridges, routers, and associated links. 2) Lack of adaptivity: they impose the same load on process group members and the network even under reduced failure rates (viz., packet losses, process failures). In this paper, we describe two protocols to address these problems: 1) a hierarchical gossiping protocol and 2) an adaptive dissemination framework (for multicasts) that allows use of any gossiping primitive within it. These protocols work within a virtual peer-to-peer hierarchy called the leaf box hierarchy. Processes can be allocated in a topologically aware manner to the leaf boxes of this structure, so that protocols 1 and 2 produce low traffic across domain boundaries in the network and induce minimal overhead when there are no failures
Efficient central nervous system remyelination requires T cells

We demonstrate a role for immune functions in the spontaneous remyelination of central nervous system (CNS) axons after lysolecithin‐induced demyelination in the spinal cord. Rag‐1–deficient mice lack both B cells and T cells and show significantly reduced spontaneous remyelination compared with control mice of matching genetic background. Mice lacking or depleted of either CD4+ T cells or CD8+ T cells also exhibit reduced remyelination. These data indicate that T cells are necessary for efficient CNS remyelination. Thus, general nonspecific immunosuppression as a therapeutic approach for the treatment of CNS injury and demyelinating disease may have undesirable effects on subsequent tissue repair. Ann Neurol 2003;53:680–684
Extracting Core Knowledge from Linked Data

Recent research has shown the Linked Data cloud to be a potentially ideal basis for improving user experience when interacting with Web content across different applications and domains. Using the explicit knowledge of datasets, however, is neither sufficient nor straightforward. Dataset knowledge is often not uniformly organized, thus it is generally unknown how to query for it. To deal with these issues, we propose a dataset analysis approach based on knowledge patterns, and show how the recognition of patterns can support querying datasets even if their vocabularies are previously unknown. Finally, we discuss results from experimenting on three multimedia-related datasets.
A multilevel framework of firm boundaries: Firm characteristics, dyadic differences, and network attributes

Extending prior firm boundary research that tends to focus on economic explanations and rely on atomistic assumptions, we propose a multilevel framework by bridging the resource-based view and the social network perspective, with their respective emphases on the importance of firms' internal resource endowments and external resource opportunities. Specifically, we argue that firms' boundary choices can be better understood by considering the tension between the need for external resources and the need for risk controls, affected by internal and external resource factors at three important levels: firm characteristics, dyadic differences, and network attributes. We also explore firms' boundary choices under two conditions: whether to initiate external relationships (non-partnering vs. partnering) and whether to pursue either alliances or acquisitions if external relationships are needed. Our analyses of the United States computer industry over a nine-year span largely support our theoretical framework and demonstrate the importance of unique factors at and across individual, dyadic, and network levels in understanding firms' boundary choices. Copyright © 2009 John Wiley & Sons, Ltd.
On the air permeability of Populus pit

Sap hydrodynamics in vascular cells of trees seems to be controlled by small membranes called pits. Understanding how the pit junctions regulate the sap flow and stop embolism by cavitation is today a challenging issue. The hypothesis that the pit porosity adjusts the flow under negative pressure and stops the air bubble diffusion need to be validated. In this talk, we will present the experimental results on Populus trees that support the idea that pits operate "passively" in a biological point of view. This work is based on atomic force microscope (AFM) experiments, which have been realised to measure quantitatively the mechanical properties of pits at the nanoscale.
Principle components analysis and Support Vector Machine based Intrusion Detection System

Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility of the proposed IDS system by several experiments on NSL-KDD dataset. A reduction process has been used to reduce the number of features in order to decrease the complexity of the system. The experimental results show that the proposed system is able to speed up the process of intrusion detection and to minimize the memory space and CPU time cost.
VScaler: Autonomic Virtual Machine Scaling

Recent research results in cloud community found that cloud users increasingly force providers to shift from fixed bundle instance types(e.g. Amazon instances) to flexible bundles and shrinked billing cycles. This means that cloud applications can dynamically provision the used amount of resources in a more fine-grained fashion. This observation calls for approaches which are able to automatically implement fine granular VM resource allocation with respect to user-provided SLAs. In this work we propose VScaler, a framework which implements autonomic resource allocation using a novel approach to reinforcement learning.