A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Isolation and characterization of a novel simazine-degrading bacterium from agricultural soil of central Chile, Pseudomonas sp MHP41

<p>s-Triazine herbicides are used extensively in South America in agriculture and forestry. In this study, a bacterium designated as strain MHP41, capable of degrading simazine and atrazine, was isolated from agricultural soil in the Quillota valley, central Chile. Strain MHP41 is able to grow in minimal medium, using simazine as the sole nitrogen source. In this medium, the bacterium exhibited a growth rate of mu = 0.10 h(-1), yielding a high biomass of 4.2 x 10(8) CFU mL(-1). Resting cells of strain MHP41 degrade more than 80% of simazine within 60 min. The atzA, atzB, atzC, atzD, atzE and atzF genes encoding the enzymes of the simazine upper and lower pathways were detected in strain MHP41. The motile Gram-negative bacterium was identified as a Pseudomonas sp., based on the Biolog microplate system and comparative sequence analyses of the 16S rRNA gene. Amplified ribosomal DNA restriction analysis allowed the differentiation of strain MHP41 from Pseudomonas sp. ADP. The comparative 16S rRNA gene sequence analyses suggested that strain MHP41 is closely related to Pseudomonas nitroreducens and Pseudomonas multiresinovorans. This is the first s-triazine-degrading bacterium isolated in South America. Strain MHP41 is a potential biocatalyst for the remediation of s-triazine-contaminated environments.</p>

A systematic review evaluating the impact of online or blended learning vs. face-to-face learning of clinical skills in undergraduate nurse education.

Aim: To determine whether the use of an online or blended learning paradigm has the potential to enhance the teaching of clinical skills in undergraduate nursing.<br/><br/>Background: The need to adequately support and develop students in clinical skills is now arguably more important than previously considered due to reductions in practice opportunities. Online and blended teaching methods are being developed to try and meet this requirement, but knowledge about their effectiveness in teaching clinical skills is limited.<br/><br/>Design: Mixed methods systematic review, which follows the Joanna Briggs Institute User guide version 5.<br/><br/>Data Sources: Computerized searches of five databases were undertaken for the period 1995-August 2013.<br/><br/>Review Methods: Critical appraisal and data extraction were undertaken using Joanna Briggs Institute tools for experimental/observational studies and interpretative and critical research. A narrative synthesis was used to report results.<br/><br/>Results: Nineteen published papers were identified. Seventeen papers reported on online approaches and only two papers reported on a blended approach. The synthesis of findings focused on the following four areas: performance/clinical skill, knowledge, self-efficacy/clinical confidence and user experience/satisfaction. The e-learning interventions used varied throughout all the studies.<br/><br/>Conclusion: The available evidence suggests that online learning for teaching clinical skills is no less effective than traditional means. Highlighted by this review is the lack of available evidence on the implementation of a blended learning approach to teaching clinical skills in undergraduate nurse education. Further research is required to assess the effectiveness of this teaching methodology.

Efficient Structure Learning of Bayesian Networks using Constraints

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion), Akaike Information Criterion and Bayesian Dirichlet Criterion. Then a branch-and-bound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality. As an example, structural constraints are used to map the problem of structure learning in Dynamic Bayesian networks into a corresponding augmented Bayesian network. Finally, we show empirically the benefits of using the properties with state-of-the-art methods and with the new algorithm, which is able to handle larger data sets than before.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Aging Behavior of YAl2p/Mg-14Li-1Al Composite

YAl2p/Mg-14Li-1Al composite was made by a stir-casting technique. The aging behavior of the composite was investigated using hardness test, differential scanning calorimetry, and X-ray diffraction. The results show that the microhardness variations of both the matrix alloy and its composite are related to the precipitation and decomposition of ��-MgLi2Al phase. The strengthening of the composite is from the addition of YAl2 particulates and the precipitation in matrix alloy. The former contribution is stable, but the latter is unstable and depends on the aging behavior. The addition of YAl2 particulates delays occurring of the aging behavior of composite.

Patience, Persistence and Pragmatism: Experiences and Lessons Learnt from the Implementation of Clinically Integrated Teaching and Learning of Evidence-Based Health Care - A Qualitative Study

<p>Background</p><p>Clinically integrated teaching and learning are regarded as the best options for improving evidence-based healthcare (EBHC) knowledge, skills and attitudes. To inform implementation of such strategies, we assessed experiences and opinions on lessons learnt of those involved in such programmes.</p><p>Methods and Findings</p><p>We conducted semi-structured interviews with 24 EBHC programme coordinators from around the world, selected through purposive sampling. Following data transcription, a multidisciplinary group of investigators carried out analysis and data interpretation, using thematic content analysis. Successful implementation of clinically integrated teaching and learning of EBHC takes much time. Student learning needs to start in pre-clinical years with consolidation, application and assessment following in clinical years. Learning is supported through partnerships between various types of staff including the core EBHC team, clinical lecturers and clinicians working in the clinical setting. While full integration of EBHC learning into all clinical rotations is considered necessary, this was not always achieved. Critical success factors were pragmatism and readiness to use opportunities for engagement and including EBHC learning in the curriculum; patience; and a critical mass of the right teachers who have EBHC knowledge and skills and are confident in facilitating learning. Role modelling of EBHC within the clinical setting emerged as an important facilitator. The institutional context exerts an important influence; with faculty buy-in, endorsement by institutional leaders, and an EBHC-friendly culture, together with a supportive community of practice, all acting as key enablers. The most common challenges identified were lack of teaching time within the clinical curriculum, misconceptions about EBHC, resistance of staff, lack of confidence of tutors, lack of time, and negative role modelling.</p><p>Conclusions</p><p>Implementing clinically integrated EBHC curricula requires institutional support, a critical mass of the right teachers and role models in the clinical setting combined with patience, persistence and pragmatism on the part of teachers.</p>

Effective Teaching and Learning of Science in the Outdoors: A Blended Model

This paper reports on an innovative Continuing Professional Development (CPD) programme which addressed transition issues and issues with conducting outdoor work and attitudes towards science through ���Shared Learning' days between elementary and middle school transition classes. Teachers supported each other to overcome issues with conducting outdoor work and contributed their expertise from their educational stage. The project utilised a blended CPD approach of workshops, coteaching and in-class support and was based upon a wealth earlier successful CPD programmes to result in a sound theoretical framework.<br/>The outcomes were measured using a thorough mixed-methods approach. This paper will report on the achieved outcomes with effective outdoor learning as the vehicle to overcome identified issues and key challenges for policy development.

A comparison of theoretical Mg VI emission line strengths with active-region observations from SERTS

<p>R-matrix calculations of electron impact excitation rates in N-like Mg VI are used to derive theoretical electron-density-sensitive emission line ratios involving 2s²2p³ - 2s2p⁴ transitions in the 269-403 �� wavelength range. A comparison of these with observations of a solar active region, obtained during the 1989 flight of the Solar EUV Rocket Telescope and Spectrograph (SERTS), reveals good agreement between theory and observation for the 2s²2p³ ⁴S - 2s2p ⁴ ⁴p transitions at 399.28, 400.67, and 403.30 ��, and the 2s²2p³ ²p - 2s2p⁴ ²D lines at 387.77 and 387.97 ��. However, intensities for the other lines attributed to Mg VI in this spectrum by various authors do not match the present theoretical predictions. We argue that these discrepancies are not due to errors in the adopted atomic data, as previously suggested, but rather to observational uncertainties or mis-identifications. Some of the features previously identified as Mg VI lines in the SERTS spectrum, such as 291.36 and 293.15 ��, are judged to be noise, while others (including 349.16 ��) appear to be blended.</p>

Perceptual learning of acoustic noise generates memory-evoked potentials

<p>Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.</p>

Can virtual environments enhance the learning of historical chronology?

Historical time and chronological sequence are usually conveyed to pupils via the presentation of semantic information on printed worksheets, events being rote-memorised according to date. We explored the use of virtual environments in which successive historical events were depicted as ���places��� in time���space, encountered sequentially in a fly-through. Testing was via ���Which came first, X or Y?��� questions and picture-ordering. University undergraduates experiencing the history of an imaginary planet performed better after a VE than after viewing a ���washing line��� of sequential images, or captions alone, especially for items in intermediate list positions. However, secondary children 11���14 years remembered no more about successive events in feudal England when they were presented virtually compared with either paper picture or 2-D computer graphic conditions. Primary children 7���9 years learned more about historical sequence after studying a series of paper images, compared with either VE or computer graphic conditions, remembering more in early/intermediate list positions. Reasons for the discrepant results are discussed and future possible uses of VEs in the teaching of chronology assessed. Keywords: timeline, chronographics