Assessing the effectiveness of High Intensity Interval Training (HIIT) for smoking cessation in women: HIIT to quit study protocol

Background: Smoking and physical inactivity are major risk factors for heart disease. Linking strategies that promote improvements in fitness and assist quitting smoking has potential to address both these risk factors simultaneously. The objective of this study is to compare the effects of two exercise interventions (high intensity interval training (HIIT) and lifestyle physical activity) on smoking cessation in female smokers. Method/design: This study will use a randomised controlled trial design. Participants: Women aged 18–55 years who smoke ≥ 5 cigarettes/day, and want to quit smoking. Intervention: all participants will receive usual care for quitting smoking. Group 1 - will complete two gym-based supervised HIIT sessions/week and one home-based HIIT session/week. At each training session participants will be asked to complete four 4-min (4 × 4 min) intervals at approximately 90 % of maximum heart rate interspersed with 3- min recovery periods. Group 2 - participants will receive a resource pack and pedometer, and will be asked to use the 10,000 steps log book to record steps and other physical activities. The aim will be to increase daily steps to 10,000 steps/day. Analysis will be intention to treat and measures will include smoking cessation, withdrawal and cravings, fitness, physical activity, and well-being. Discussion: The study builds on previous research suggesting that exercise intensity may influence the efficacy of exercise as a smoking cessation intervention. The hypothesis is that HIIT will improve fitness and assist women to quit smoking.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Assessing clinical reasoning: A method to monitor its development in a PBL curriculum

The aim of this study was to develop and trial a method to monitor the evolution of clinical reasoning in a PBL curriculum that is suitable for use in a large medical school. Termed Clinical Reasoning Problems (CRPs), it is based on the notion that clinical reasoning is dependent on the identification and correct interpretation of certain critical clinical features. Each problem consists of a clinical scenario comprising presentation, history and physical examination. Based on this information, subjects are asked to nominate the two most likely diagnoses and to list the clinical features that they considered in formulating their diagnoses, indicating whether these features supported or opposed the nominated diagnoses. Students at different levels of medical training completed a set of 10 CRPs as well as the Diagnostic Thinking Inventory, a self-reporting questionnaire designed to assess reasoning style. Responses were scored against those of a reference group of general practitioners. Results indicate that the CRPs are an easily administered, reliable and valid assessment of clinical reasoning, able to successfully monitor its development throughout medical training. Consequently, they can be employed to assess clinical reasoning skill in individual students and to evaluate the success of undergraduate medical schools in providing effective tuition in clinical reasoning.

Workplace training and generic and technical skill development in the Australian construction industry

- Purpose The purpose of this paper is to investigate the current skills gap in both generic and skill areas within the construction industry in Queensland, Australia. - Design/methodology/approach An internet-based survey was administered to collect the opinions of construction employees about the workplace-training environment and their perceptions towards training. The survey intended to address the following research questions, specifically in relation to the construction industry. - Findings The survey results reveal that whilst overall participation in workplace training is high, the current workplace training environments do not foster balanced skill development. The study reveals that in the current absence of a formal and well-balanced training mechanism, construction workers generally resort to their own informal self-development initiatives to develop the needed role-specific theoretical knowledge. - Research limitations/implications The findings of the research are based on the data primarily collected in the construction industry in Queensland, Australia. The data are limited to a single Tier 2 construction company. - Practical implications The findings of this study can be utilised to suggest improvements in the current (or develop new) workplace training initiatives. - Social implications The research suggests that workplace training has positive relationship with career growth. The results suggest that in the construction industry, employees are generally well aware of the importance of workplace training in their career development and they largely appreciate training as being a critical factor for developing their capacity to perform their roles successfully, and to maintain their employability. - Originality/value This paper is unique as it investigates the current skills gap in both generic and skill areas within the construction industry in Queensland, Australia. So far no work has been undertaken to identify and discusses the main method of workplace learning within the Tier 2 industry in the context of Queensland Australia.

Subject Teacher Training and Teaching in English

The purpose of this Master s thesis is on one hand to find out how CLIL (Content and Language Integrated Learning) teachers and English teachers perceive English and its use in teaching, and on the other hand, what they consider important in subject teacher education in English that is being planned and piloted in STEP Project at the University of Helsinki Department of Teacher Education. One research question is also what kind of language requirements teachers think CLIL teachers should have. The research results are viewed in light of previous research and literature on CLIL education. Six teachers participate in this study. Two of them are English teachers in the comprehensive school, two are class teachers in bilingual elementary education, and two are subject teachers in bilingual education, one of whom teaches in a lower secondary school and the other in an upper secondary school. One English teacher and one bilingual class teacher have graduated from a pilot class teacher program in English that started at the University of Helsinki in the middle of the 1990 s. The bilingual subject teachers are not trained in English but they have learned English elsewhere, which is a particular focus of interest in this study because it is expected that a great number of CLIL teachers in Finland do not have actual studies in English philology. The research method is interview and this is a qualitative case study. The interviews are recorded and transcribed for the ease of analysis. The English teachers do not always use English in their lessons and they would not feel confident in teaching another subject completely in English. All of the CLIL teachers trust their English skills in teaching, but the bilingual class teachers also use Finnish during lessons either because some teaching material is in Finnish, or they feel that rules and instructions are understood better in mother tongue or students English skills are not strong enough. One of the bilingual subject teachers is the only one who consciously uses only English in teaching and in discussions with students. Although teachers good English skills are generally considered important, only the teachers who have graduated from the class teacher education in English consider it important that CLIL teachers would have studies in English philology. Regarding the subject teacher education program in English, the respondents hope that its teachers will have strong enough English skills and that it will deliver what it promises. Having student teachers of different subjects studying together is considered beneficial. The results of the study show that acquiring teaching material in English continues to be the teachers own responsibility and a huge burden for the teachers, and there has, in fact, not been much progress in the matter since the beginning of CLIL education. The bilingual subject teachers think, however, that using one s own material can give new inspiration to teaching and enable the use of various pedagogical methods. Although it is questionable if the language competence requirements set for CLIL teachers by the Finnish Ministry of Education are not adhered to, it becomes apparent in the study that studies in English philology do not necessarily guarantee strong enough language skills for CLIL teaching, but teachers own personality and self-confidence have significance. Keywords: CLIL, bilingual education, English, subject teacher training, subject teacher education in English, STEP

On Training and Training Voids for Receive Antenna Subset Selection in Time-Varying Channels

Antenna selection (AS) provides most of the benefits of multiple-antenna systems at drastically reduced hardware costs. In receive AS, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. The "best" subset to be used for data reception is determined by means of channel estimates acquired using training sequences. Due to the nature of AS, the channel estimates at different antennas are obtained from different transmissions of the pilot sequence, and are, thus, outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of "training voids", in which the last pilot transmission is not fully exploited by the receiver. We present a "void-filling" method for fully exploiting these voids, which essentially provides more accurate training for some antennas, and derive the optimal subset selection rule for any void-filling method. We also derive new closed-form equations for the performance of receive AS with optimal subset selection.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Training for Antenna Selection in Time-Varying Channels: Optimal Selection, Energy Allocation, and Energy Efficiency Evaluation

Single receive antenna selection (AS) is a popular method for obtaining diversity benefits without the additional costs of multiple radio receiver chains. Since only one antenna receives at any time, the transmitter sends a pilot multiple times to enable the receiver to estimate the channel gains of its N antennas to the transmitter and select an antenna. In time-varying channels, the channel estimates of different antennas are outdated to different extents. We analyze the symbol error probability (SEP) in time-varying channels of the N-pilot and (N+1)-pilot AS training schemes. In the former, the transmitter sends one pilot for each receive antenna. In the latter, the transmitter sends one additional pilot that helps sample the channel fading process of the selected antenna twice. We present several new results about the SEP, optimal energy allocation across pilots and data, and optimal selection rule in time-varying channels for the two schemes. We show that due to the unique nature of AS, the (N+1)-pilot scheme, despite its longer training duration, is much more energy-efficient than the conventional N-pilot scheme. An extension to a practical scenario where all data symbols of a packet are received by the same antenna is also investigated.

Initiatives for the improvement of continuous management training

[EN] Purpose. This work aims to present, from the company viewpoint, a structured account of management proposals and practices directed toward improving the intensity and effectiveness of continuous management training (CMT). Design/methodology/approach. The article takes as its main theoretical referents the Theory of Human Capital, the Resource-Based Vision and the contributions made via the new institutional economy with regard to the problems of information asymmetry between companies, employees and training providers and completes the proposals that derive from this theoretical approach. To do this, experience-based contributions are collected from a selection of company training and HR managers from twelve Basque companies characterised by their strong investment in management training. The methodology used was qualitative and obtained by different qualitative techniques: Focus Groups, Nominal Groups and the Delphi Method, which make up the so-called Hybrid Delphi. Findings and implications. The proposals are aimed at the main agents in training activity: training providers, associations and public agents engaged in management training and, particularly, companies themselves. The initiatives seek above all to increase training market transparency, to improve mutual commitments between companies and managers, and to link training and development with culture and strategic management, so that firms make optimal investment in management training. Originality/value. The methodology used is original, and the contributions are consistent with the theory, have a proven practical utility, and are presented in a hierarchy, which facilitates decision making.

A Reliable Method for Rhythm Analysis during Cardiopulmonary Resuscitation

nterruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Competitive training in hidden Markov models

The use of hidden Markov models is placed in a connectionist framework, and an alternative approach to improving their ability to discriminate between classes is described. Using a network style of training, a measure of discrimination based on the a posteriori probability of state occupation is proposed, and the theory for its optimization using error back-propagation and gradient ascent is presented. The method is shown to be numerically well behaved, and results are presented which demonstrate that when using a simple threshold test on the probability of state occupation, the proposed optimization scheme leads to improved recognition performance.

Report of the essential EAFM training course, Chittagong, Bangladesh, 12-20 November, 2014

Participants were exposed to concepts and information about Ecosystem Approach to Fisheries Management (EAFM) using a structured, participatory method of delivery. The learning strategy involved specifically designed exercises, using real examples, to consolidate learning. Daily monitoring and reviews were conducted together with pre-and post-course assessment.

Report of the training programme on Essential Ecosystem Approach to Fisheries Management (EAFM): developing capacity in the EAFM, Chennai, India, 11-16 August, 2014

Participants were exposed to concepts and information about EAFM using a structured, participatory method of delivery. The learning strategy involved specifically designed exercises, using real examples, to consolidate learning. Daily monitoring and reviews were conducted together with pre-and post-course assessment.