Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

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.

Errorless learning and memory performance in schizophrenia

There is evidence that patients with schizophrenia have impaired explicit memory and intact implicit memory. The present study sought to replicate and extend that of O'Carroll et al. [O'Carroll, R.E., Russell, H.H., Lawrie, S.M. and Johnstone, E.C., 1999. Errorless learning and the cognitive rehabilitation of memory-impaired schizophrenic patients. Psychological Medicine 29, 105-112.] which reported that for memory-impaired patients with schizophrenia performance on a (cued) word recall task is enhanced using errorless learning techniques (in which errors are prevented during learning) compared to errorful learning (the traditional trial-and-error approach). Thirty patients with a DSM-IV diagnosis of schizophrenia and fifteen healthy controls (HC) participated. The Rivermead Behavioural Memory Test was administered and from their scores, the schizophrenic patients were classified as either memory-impaired (MIS), or memory-unimpaired (MUS). During the training phase two lists of words were learned separately, one using the errorless learning approach and the other using an errorful approach. Subjects were then tested for their recall of the words using cued recall. After errorful learning training, performance on word recall for the MIS group was impaired compared to the MUS and HC groups. However, after errorless learning training, no significant differences in performance were found between the three groups. Errorless learning may play an important role in remediation of cognitive deficits for patients with schizophrenia. (c) 2007 Elsevier Ireland Ltd. All rights reserved.

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.

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.

Design: Mixed methods systematic review, which follows the Joanna Briggs Institute User guide version 5.

Data Sources: Computerized searches of five databases were undertaken for the period 1995-August 2013.

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.

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.

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.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Care Planning in Children and Young Peoples Nursing

Care Planning in Children and Young People's Nursing addresses a selection of the most common concerns that arise when planning care for infants, children and young people within the hospital and community setting. Clear and detailed, this text reflects both the uniqueness and diversity of contemporary children's nursing and utilizes images and case studies to provide a holistic insight into the practice of care planning through the reporting of best available evidence and current research, policy and education.

Divided into sections for ease of reference, Care Planning in Children and Young People’s Nursing explores both the theory and practice of care planning. Chapters on the principles of care planning include issues such as managing risk, safeguarding children, ethical and legal implications, integrated care pathways, interprofessional assessment, and invaluable parent perspectives. Additional chapters on the application of planning care examine the practical aspects of a wide range of specific conditions including cystic fibrosis, obesity, cardiac/renal failure and HIV/AIDS. Each chapter is interactive, with questions, learning activities and points for discussion creating an engaging and enquiry-based learning approach.

Care Planning in Children and Young People’s Nursing is a definitive resource, reflecting innovative practice which is suitable for undergraduate and postgraduate nurse education.

Sarcasm Detection on Czech and English Twitter

This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language. We created a large Czech Twitter corpus consisting of 7,000 manually-labeled tweets and provide it to the community. We evaluate two classifiers with various combinations of features on both the Czech and English datasets. Furthermore, we tackle the issues of rich Czech morphology by examining different preprocessing techniques. Experiments show that our language-independent approach significantly outperforms adapted state-of-the-art methods in English (F-measure 0.947) and also represents a strong baseline for further research in Czech (F-measure 0.582).

Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier

In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.

Developing and evaluating the integration of Life and Social Sciences teaching to first-year Nursing and Midwifery students

Title: Evaluating the integrating of life and social sciences teaching to first-year nursing and midwifery students

Objectives: To evaluate an integrated teaching and learning approach to first-year nursing students, combining the life, social sciences and public health with a more integrated and clinical focused approach to teaching delivery

Background: Historically within the School of Nursing and Midwifery the life sciences and social sciences had been taught as separate modules with separate teaching teams. This had reflected in a somewhat dis-integrated approach to student learning and understanding without clear clinical focus on application. With focus upon student learning the teaching teams engaged with a stepped, incremental and progressive movement towards developing and delivering a more integrated structure of learning, combining the life sciences, social sciences and public health teaching and learning within the one extended first-year module. The focus was particularly on integrated understanding and clinical relevance. This paper discusses both the approach to developing the integrated model of teaching and the evaluation of that teaching.

Results: The module, combining life, social science and Public health teaching was positively evaluated by the students. Evaluations are compared and contrasted from to nursing student intakes.

iLectures - Exploring the interactive lecture using web-based broadcasting technology

The continued use of traditional lecturing across Higher Education as the main teaching and learning approach in many disciplines must be challenged. An increasing number of studies suggest that this approach, compared to more active learning methods, is the least effective. In counterargument, the use of traditional lectures are often justified as necessary given a large student population. By analysing the implementation of a web based broadcasting approach which replaced the traditional lecture within a programming-based module, and thereby removed the student population rationale, it was hoped that the student learning experience would become more active and ultimately enhance learning on the module. The implemented model replaces the traditional approach of students attending an on-campus lecture theatre with a web-based live broadcast approach that focuses on students being active learners rather than passive recipients. Students ‘attend’ by viewing a live broadcast of the lecturer, presented as a talking head, and the lecturer’s desktop, via a web browser. Video and audio communication is primarily from tutor to students, with text-based comments used to provide communication from students to tutor. This approach promotes active learning by allowing student to perform activities on their own computer rather than the passive viewing and listening common encountered in large lecture classes. By analysing this approach over two years (n = 234 students) results indicate that 89.6% of students rated the approach as offering a highly positive learning experience. Comparing student performance across three academic years also indicates a positive change. A small data analytic analysis was conducted into student participation levels and suggests that the student cohort's willingness to engage with the broadcast lectures material is high.