87value/wos11967 huggingface dataset:
There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of crowd-sourced knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the public's collective interest. Google and Wikipedia have become important knowledge bases for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our inference engine. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index. (c) 2017 Elsevier Ltd. All rights reserved.
In video-surveillance and ambient intelligence applications, head-pose estimation is an important and challenging task. Basically, the problem lies in assessing the pose of the head according to three reference angles, that indicate the head rotation with respect to three orthogonal axes, and are named roll, yaw, and pitch angles. The problem becomes particularly difficult if only 2D video-sequences or still images are available, thus information about the depth of the scene is missing. This makes the computation of the pitch angle very difficult. State-of-the-art methods usually add the information on the pitch angle separately, and this makes them strongly dependent on the hardware used and the scene under surveillance. Moreover, some of them require large training sets with head poses data. Finally, the extraction of several features from the detected face is often necessary. Since head-pose estimation is only a (small) part of a video-surveillance system as a whole, it is necessary to find novel approaches which make the head-pose estimation as simple as possible, in order to allow their use in real-time. In this paper, a novel method for automatic head-pose estimation is presented. This is based on a geometrical model relying on the exploitation of the Vitruvian man's proportions and the related Golden Ratio''. Our approach reduces the number of features extracted, avoiding the need for a training set as well as information on the hardware used or the scene under control. Simple ratios among eyes and nose positions, according to the assumed Golden Ratio'', are used to compute, in particular, the pitch angle. Proposed method performs competitively with respect to state-of-the-art approaches, without requiring their working constraints and assumptions.
BACKGROUND: It is unclear whether allergen immunotherapy (AIT) can be safely initiated during the pollen season (coseasonal initiation [CSI]) because of a potential increased risk of systemic allergic reactions. OBJECTIVE: To systematically review publications reporting the safety of subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) CSI to validate or invalidate the perception of increased safety risk. METHODS: PubMed, EMBASE, Ovid, Literatura Latino Americana em Ciencias da Saude (LILACS), and Cochrane Library databases were searched without limits for studies of any design reporting SCIT or SLIT CSI for pollen allergen. Congress abstracts were included. RESULTS: Nineteen eligible studies were identified: 8 SCIT (n = 947 subjects total; n[340 double-blind placebo-controlled [DBPC]) and 11 SLIT (n = 2668 subjects total; n = 565 DBPC). Study characteristics and safety reporting were heterogeneous. No epinephrine administrations were reported. Discontinuation frequencies were 6% or less and 10% or less with SCIT and SLIT CSI, respectively. In SCIT studies, systemic allergic reaction frequency was 0% to 7% with up to peak season or CSI, 0% to 6% with after peak season or out-of-season initiation, and 0% to 7% with placebo. In SCIT studies, serious treatment-related adverse event (AE) frequency with CSI ranged from 0% to 2%; few severe AEs were reported. In SLIT studies, systemic allergic reaction frequency ranged from 0% to 4% with CSI, 0% with out-of-season initiation, and 0% to 2% with placebo. Overall, 2 serious treatment-related AEs with SLIT CSI were reported. Severe AE frequency in SLIT studies ranged from 0% to 8% with CSI, 0% to 12% with out-of-season initiation, and 0% to 8% with placebo. CONCLUSIONS: No increase in AEs of concern was observed with SCIT or SLIT CSI; however, additional data with standardized regimens and doses are needed. (C) 2016 American Academy of Allergy, Asthma & Immunology
Electricity is the basic tool that drives industrialization, technological advancement, engineering transformation and economic growth all over the world. The situation of Nigeria's electricity power sector is one that has defied virtually every effort made to resurrect it from its state of dilapidation. A major concern is can there ever be stable and cost effective electricity in Nigeria? The authors are positive this can be realised, but not without considering other forms of electricity energy resources available in Nigeria other than the conventional sources of electricity which is currently the mainstay. A deliberate transition from conventional sources of electricity energy to renewable and environmental friendly sources is critical for national development, even though recent development show the Nigerian government is backsliding in adopting renewable energy technologies. This paper will be used to highlight the different sources of renewable energy which is critical to solve the lingering problem of electricity generation in Nigeria. Hence, based on the literature, modeled scenarios and field experience, the authors discovered it is much easier for individuals to drive this transition to 100% renewable energy than to continue to depend on government forever.
ASTM D6496, Standard Test Method for Determining Average Bonding Peel Strength between the Top and Bottom Layers of Needlepunched Geosynthetic Clay Liners, is used as an index test for quality control in the manufacturing of geosynthetic clay liners (GCLs) and as an acceptance test for the delivery of materials to a project. As its title indicates, it is normally used to measure the peel strength between the top and bottom geotextile layers within a GCL. This paper evaluates the use of this test method to measure the peel strength between the thin film/coating of a laminated GCL and the geotextile it is adhered to. This paper also shows how the moisture content and hydrating conditions affect the peel strength in these products.
Medical additive manufacturing requires standard tessellation language (STL) models. Such models are commonly derived from computed tomography (CT) images using thresholding. Threshold selection can be performed manually or automatically. The aim of this study was to assess the impact of manual and default threshold selection on the reliability and accuracy of skull STL models using different CT technologies. One female and one male human cadaver head were imaged using multi-detector row CT, dual-energy CT, and two cone-beam CT scanners. Four medical engineers manually thresholded the bony structures on all CT images. The lowest and highest selected mean threshold values and the default threshold value were used to generate skull STL models. Geometric variations between all manually thresholded STL models were calculated. Furthermore, in order to calculate the accuracy of the manually and default thresholded STL models, all STL models were superimposed on an optical scan of the dry female and male skulls (gold standard). The intra- and inter-observer variability of the manual threshold selection was good (intra-class correlation coefficients >0.9). All engineers selected grey values closer to soft tissue to compensate for bone voids. Geometric variations between the manually thresholded STL models were 0.13 mm (multi-detector row CT), 0.59 mm (dual-energy CT), and 0.55 mm (cone-beam CT). All STL models demonstrated inaccuracies ranging from -0.8 to +1.1 mm (multi-detector row CT), -0.7 to +2.0 mm (dual-energy CT), and -2.3 to +4.8 mm (cone-beam CT). This study demonstrates that manual threshold selection results in better STL models than default thresholding. The use of dual-energy CT and cone-beam CT technology in its present form does not deliver reliable or accurate STL models for medical additive manufacturing. New approaches are required that are based on pattern recognition and machine learning algorithms.
A range of reported methods have been assessed for the synthesis of two coumarin fluorophores containing azides, la and 2, for subsequent click modification. In the case of la reported methods were successfully applied, but the resulting azide proved to be rather unstable and appears more suited to in situ generation and conversion to the click triazole. In the case of 2, reported methods for the synthesis of precursor 5 were ineffective in two cases and resulted in either no bromination a-to the carbonyl or the formation of multiple unwanted side-products, some of which were isolated, 6-13. The use of CuBr2 in excess or Br-2 in 50% HBr in acetic acid did result in the isolation of 5, which could be efficiently converted to 2 using excess NaN3 in THF. (C) 2016 Elsevier Ltd. All rights reserved.
This study aims to explore the potential impairment of Theory of Mind (ToM; i.e., the ability to represent cognitive and affective mental states to both self and others) and the clinical, neuropsychological and Quality of Life (QoL) correlates of these cognitive abnormalities in the early stages of amyotrophic lateral sclerosis (ALS), a multisystem neurodegenerative disease recently recognized as a part of the same clinical and pathological spectrum of frontotemporal lobar degeneration. Twenty-two consecutive, cognitively intact ALS patients, and 15 healthy controls, underwent assessment of executive, verbal comprehension, visuospatial, behavioral, and QoL measures, as well as of the ToM abilities by Emotion Attribution Task (EAT), Advanced Test of ToM (ATT), and Eyes Task (ET). ALS patients obtained significantly lower scores than controls on EAT and ET. No significant difference was found between the two groups on ATT. As regard to type of ALS onset, patients with bulbar onset performed worse than those with spinal onset on ET. Correlation analysis revealed that EAT and ET were positively correlated with education, memory prose, visuo-spatial performances, and Mental Health scores among QoL items. Our results suggest that not only cognitive but also affective subcomponents of ToM may be impaired in the early stages of ALS, with significant linkage to disease onset and dysfunctions of less executively demanding conditions, causing potential impact on patients' Mental Health.
Ambient-assisted living (AAL) is currently one of the important research and development areas, where accessibility, usability, and learning play a major role and where future interfaces are an important concern for applied engineering. The general goal of AAL solutions is to apply ambient intelligence technology to enable people with specific demands, e.g., handicapped or elderly, to live in their preferred environment longer. The term Internet of Things (IoT) is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. In this context, we propose a new methodology based on self organizing maps (SOMs) and fuzzy C-means (FCM) algorithms for profile generation as regards the activities of the user and their correlation with the available sensors. Moreover, we utilize the provided context to assign the generated profiles to more contextually complex activities. Our methodology is being evaluated into an AAL structure equipped with several sensors. More precisely, we assess the proposed method in a data set generated by accelerometers and its performance over a number of everyday activities.
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on Pervasive Intelligent Technologies for Health. Background: Effective nonverbal communication between patients and clinicians fosters both the delivery of empathic patient-centered care and positive patient outcomes. Although nonverbal skill training is a recognized need, few efforts to enhance patient-clinician communication provide visual feedback on nonverbal aspects of the clinical encounter. Objectives: We describe a novel approach that uses social signal processing technology (SSP) to capture nonverbal cues in real time and to display ambient visual feedback on control and affiliation two primary, yet distinct dimensions of interpersonal nonverbal communication. To examine the design and clinician acceptance of ambient visual feedback on nonverbal communication, we 1) formulated a model of relational communication to ground SSP and 2) conducted a formative user study using mixed methods to explore the design of visual feedback. Methods: Based on a model of relational communication, we reviewed interpersonal communication research to map nonverbal cues to signals of affiliation and control evidenced in patient-clinician interaction. Corresponding with our formulation of this theoretical framework, we designed ambient real-time visualizations that reflect variations of affiliation and control. To explore clinicians' acceptance of this visual feedback, we conducted a lab study using the Wizard-of-Oz technique to simulate system use with 16 healthcare professionals. We followed up with seven of those participants through interviews to iterate on the design with a revised visualization that addressed emergent design considerations. Results: Ambient visual feedback on nonverbal communication provides a theoretically grounded and acceptable way to provide clinicians with awareness of their nonverbal communication style. We provide implications for the design of such visual feedback that encourages empathic patient-centered communication and include considerations of metaphor, color, size, position, and timing of feedback. Conclusions: Ambient visual feedback from SSP holds promise as an acceptable means for facilitating empathic patient-centered nonverbal communication.
Machine learning algorithms perform differently in settings with varying levels of training set mislabeling noise. Therefore, the choice of the right algorithm for a particular learning problem is crucial. The contribution of this paper is towards two, dual problems: first, comparing algorithm behavior; and second, choosing learning algorithms for noisy settings. We present the sigmoid rule framework, which can be used to choose the most appropriate learning algorithm depending on the properties of noise in a classification problem. The framework uses an existing model of the expected performance of learning algorithms as a sigmoid function of the signal-to-noise ratio in the training instances. We study the characteristics of the sigmoid function using five representative non-sequential classifiers, namely, Na < ve Bayes, kNN, SVM, a decision tree classifier, and a rule-based classifier, and three widely used sequential classifiers based on hidden Markov models, conditional random fields and recursive neural networks. Based on the sigmoid parameters we define a set of intuitive criteria that are useful for comparing the behavior of learning algorithms in the presence of noise. Furthermore, we show that there is a connection between these parameters and the characteristics of the underlying dataset, showing that we can estimate an expected performance over a dataset regardless of the underlying algorithm. The framework is applicable to concept drift scenarios, including modeling user behavior over time, and mining of noisy time series of evolving nature.
Estuarine and marine ecosystems are subject to different sources of stress including changes in environmental physicochemical variables (nutrients, oxygen availability, temperature, salinity, pH) or exposure to a large cocktail of environmental pollutants. In the estuary of Bilbao, despite the improvement in environmental quality due to replacement of most polluting industries and the progressive implementation of an integrated sewage treatment plan, chronic pollution by metals and hydrocarbons still remains, together with eutrophication and pollution by emerging contaminants. The Unit of Formation and Research Protection of Ecosystem Health was created in 2012 through the strategic aggregation of three consolidated research groups: Cell Biology in Environmental Toxicology (CBET), Phytoplankton of estuaries and coastal areas (FITEAC) and Research on marine and estuarine pelagic environment and planktonic communities (MarEsPlank). The three groups have a long tradition in studying the health of marine and estuarine ecosystems from different perspectives, focusing in the Bay of Biscay and especially in the estuary of Bilbao. In this work we review data on the health status of the Bilbao estuary using early warning tools at the cell and tissue levels, phytoplankton assemblages and zooplankton ecology. In spite of the seasonal and interannual variability recorded for some parameters, a general recovery trend was observed for the health status in the estuary of Bilbao, with sporadic critical events such as the Prestige oil spill. (C) 2016 Elsevier Ltd. All rights reserved.
Wikipedia has become a very common and frequently used tool and information source for students when they are asked for any academic assignment. Aware of this circumstance and also of the limitations and the quality of existing articles in relation to manufacturing processes, technologies and systems, a project was initiated aimed at enhancing and organising learning activities around the creation and improvement of these contents in Wikipedia. The present work will describe the particular experience in two optional subjects: Design for Manufacture and Assembly and  Plastics Technology. Main objectives of this activity were: to encourage understanding, knowledge, skills and values closely bound to the active use of open and free information systems; to promote autonomous learning; to study in depth subject contents with a more active attitude; to give the possibility that work and knowledge generated by students remain on-line and accessible to other people and not just for the lecturer; to raise the ability for being critical and for evaluating different information sources; and to learn how to work in cooperative and collaborative contexts. In addition, manufacturing engineering contents in Wikipedia were significantly improved, with several remarkable created or improved articles in the manufacturing domain. Based on the results of the project, objectives have been fulfilled and outcomes have been very positive. Lecturers are satisfied in terms of both the quantity and quality of the contributions, whose time devoted by students has fit adequately to what was planned and intended skills have been exercised. Furthermore, some of the works have even been awarded a Manufacturing Engineering Society prize. Regarding students, most of them have agreed that the activity was very interesting, would recommend it in the future and even think they will make more contributions in the Wikipedia.
A multiWatt RF power amplifier operating in the frequency range 0.5 to 1.2 GHz is reported, whose output amplitude is determined by digital input words (RF Power DAC operation). The amplifier employs a CMOS IC for digital control, directly connected to a GaN FET operating in common-gate mode. The use of the GaN FET allows power supply voltages in excess of 15 V to be used. A load resistance of 50 ohms can be directly connected to the GaN FET, thereby avoiding bandwidth limitations from output matching networks. Output power in the range 2 to 3 W and drain efficiencies above 50% are obtained over more than an octave of bandwidth.
Early-onset muscle disease includes three major entities that present generally at or before birth: congenital myopathies, congenital muscular dystrophies and congenital myasthenic syndromes. Almost exclusively there is weakness and hypotonia, although cases manifesting hypertonia are increasingly being recognised. These diseases display a wide phenotypic and genetic heterogeneity, with the uptake of next generation sequencing resulting in an unparalleled extension of the phenotype-genotype correlations and diagnosis by sequencing due to unbiased sequencing. Perhaps now more than ever, detailed clinical evaluations are necessary to guide the genetic diagnosis; with arrival at a molecular diagnosis frequently occurring following dialogue between the molecular geneticist, the referring clinician and the pathologist. There is an ever-increasing blurring of the boundaries between the congenital myopathies, dystrophies and myasthenic syndromes. In addition, many novel disease genes have been described and new insights have been gained into skeletal muscle development and function. Despite the advances made, a significant percentage of patients remain without a molecular diagnosis, suggesting that there are many more human disease genes and mechanisms to identify. It is now technically- and clinically-feasible to perform next generation sequencing for severe diseases on a population-widescale, such that preconception-carrier screening can occur. Newborn screening for selected early-onset muscle diseases is also technically and ethically-achievable, with benefits to the patient and family from early management of these diseases and should also be implemented. The need for world-wide Reference Centres to meticulously curate polymorphisms and mutations within a particular gene is becoming increasingly apparent, particularly for interpretation of variants in the large genes which cause early-onset myopathies: NEB, RYR1 and TTN. Functional validation of candidate disease variants is crucial for accurate interpretation of next generation sequencing and appropriate genetic counseling. Many published pathogenic variants are too frequent in control populations and are thus likely rare polymorphisms. Mechanisms need to be put in place to systematically update the classification of variants such that accurate interpretation of variants occurs. In this review, we highlight the recent advances made and the challenges ahead for the molecular diagnosis of early-onset muscle diseases. (C) 2016 Elsevier Ltd. All rights reserved.