The influence of a novel simulated learning environment upon student clinical subjective refraction performance: A pilot study

Background: Optometry students are taught the process of subjective refraction through lectures and laboratory based practicals before progressing to supervised clinical practice. Simulated learning environments (SLEs) are an emerging technology that are used in a range of health disciplines, however, there is limited evidence regarding the effectiveness of clinical simulators as an educational tool. Methods: Forty optometry students (20 fourth year and 20 fifth year) were assessed twice by a qualified optometrist (two examinations separated by 4-8 weeks) while completing a monocular non-cycloplegic subjective refraction on the same patient with an unknown refractive error simulated using contact lenses. Half of the students were granted access to an online SLE, The Brien Holden Vision Institute (BHVI®) Virtual Refractor, and the remaining students formed a control group. The primary outcome measures at each visit were; accuracy of the clinical refraction compared to a qualified optometrist and relative to the Optometry Council of Australia and New Zealand (OCANZ) subjective refraction examination criteria. Secondary measures of interest included descriptors of student SLE engagement, student self-reported confidence levels and correlations between performance in the simulated and real world clinical environment. Results: Eighty percent of students in the intervention group interacted with the SLE (for an average of 100 minutes); however, there was no correlation between measures of student engagement with the BHVI® Virtual Refractor and speed or accuracy of clinical subjective refractions. Fifth year students were typically more confident and refracted more accurately and quickly than fourth year students. A year group by experimental group interaction (p = 0.03) was observed for accuracy of the spherical component of refraction, and post hoc analysis revealed that less experienced students exhibited greater gains in clinical accuracy following exposure to the SLE intervention. Conclusions: Short-term exposure to a SLE can positively influence clinical subjective refraction outcomes for less experienced optometry students and may be of benefit in increasing the skills of novice refractionists to levels appropriate for commencing supervised clinical interactions.

Preadolescents and Their Mothers as Oral Health-Promoting Actors : Non-biologic Determinants of Oral Health among Turkish and Finnish Preadolescents

Is oral health becoming a part of the global health culture? Oral health seems to turn out to be part of the global health culture, according to the findings of a thesis-research, Institute of Dentistry, University of Helsinki. The thesis is entitled as “Preadolescents and Their Mothers as Oral Health-Promoting Actors: Non-biologic Determinants of Oral Health among Turkish and Finnish Preadolescents.” The research was supervised by Prof.Murtomaa and led by Dr.A.Basak Cinar. It was conducted as a cross-sectional study of 611 Turkish and 223 Finnish school preadolescents in Istanbul and Helsinki, from the fourth, fifth, and sixth grades, aged 10 to 12, based on self-administered and pre-tested health behavior questionnaires for them and their mothers as well as the youth’s oral health records. Clinically assessed dental status (DMFT) and self-reported oral health of Turkish preadolescents was significantly poorer than the Finns`. A similar association occurred for well-being measures (height and weight, self-esteem), but not for school performance. Turkish preadolescents were more dentally anxious and reported lower mean values of toothbrushing self-efficacy and dietary self-efficacy than did Finns. The Turks less frequently reported recommended oral health behaviors (twice daily or more toothbrushing, sweet consumption on 2 days or less/week, decreased between-meal sweet consumption) than did the Finns. Turkish mothers reported less frequently dental health as being above average and recommended oral health behaviors as well as regular dental visits. Their mean values for dental anxiety was higher and self-efficacy on implementation of twice-daily toothbrushing were lower than those of the Finnish. Despite these differences between the Turks and Finns, the associations found in common for all preadolescents, regardless of cultural differences and different oral health care systems, assessed for the first time in a holistic framework, were as follows: There seems to be interrelation between oral health and general-well being (body height-weight measures, school performance, and self-esteem) among preadolescents: • The body height was an explanatory factor for dental health, underlining the possible common life-course factors for dental health and general well-being. • Better school performance, high levels of self-esteem and self-efficacy were interrelated and they contributed to good oral health. • Good school performance was a common predictor for twice-daily toothbrushing. Self-efficacy and maternal modelling have significant role for maintenance and improvement of both oral- and general health- related behaviors. In addition, there is need for integration of self-efficacy based approaches to promote better oral health. • All preadolescents with high levels of self-efficacy were more likely to report more frequent twice-daily toothbrushing and less frequent sweet consumption. • All preadolescents were likely to imitate toothbrushing and sweet consumption behaviors of their mothers. • High levels of self-efficacy contributed to low dental anxiety in various patterns in both groups. As a conclusion: • Many health-detrimental behaviors arise from the school age years and are unlikely to change later. Schools have powerful influences on children’s development and well-being. Therefore, oral health promotion in schools should be integrated into general health promotion, school curricula, and other activities. • Health promotion messages should be reinforced in schools, enabling children and their families to develop lifelong sustainable positive health-related skills (self-esteem, self-efficacy) and behaviors. • Placing more emphasis on behavioral sciences, preventive approaches, and community-based education during undergraduate studies should encourage social responsibility and health-promoting roles among dentists. Attempts to increase general well-being and to reduce oral health inequalities among preadolescents will remain unsuccessful if the individual factors, as well as maternal and societal influences, are not considered by psycho-social holistic approaches.

Interval Data Classification under Partial Information: A Chance-Constraint Approach

This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.

Distance Relay Co-ordination Using Support Vector Machines in Power Transmission System

In this paper, a new approach to enhance the transmission system distance relay co-ordination is presented. The approach depends on the apparent impedance loci seen by the distance relay during all possible disturbances. In a distance relay, the impedance loci seen at the relay location is obtained by extensive transient stability studies. Support vector machines (SVMs), a class of patterns classifiers are used in discriminating zone settings (zone-1, zone-2 and zone-3) using the signals to be used by the relay. Studies on a sample 9-bus are presented for illustrating the proposed scheme.

Improvement in Intrusion Detection With Advances in Sensor Fusion

Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier. The paper presented here addresses the problem of optimizing the performance of IDSs using sensor fusion with multiple sensors. The trade-off between the detection rate and false alarms with multiple sensors is highlighted. It is illustrated that the performance of the detector is better when the fusion threshold is determined according to the Chebyshev inequality. In the proposed data-dependent decision ( DD) fusion method, the performance optimization of ndividual IDSs is first addressed. A neural network supervised learner has been designed to determine the weights of individual IDSs depending on their reliability in detecting a certain attack. The final stage of this DD fusion architecture is a sensor fusion unit which does the weighted aggregation in order to make an appropriate decision. This paper theoretically models the fusion of IDSs for the purpose of demonstrating the improvement in performance, supplemented with the empirical evaluation.

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.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Joseph A. Rosen Papers 1921-1938

With few exceptions, the bulk of the collection pertains to the work of the Agro-Joint. Records of the Agro-Joint Director General. Agreements of the American Relief Administration (ARA) and the Joint Distribution Committee with the Soviet government, 1922-1923. Agreements between the Agro-Joint and the Soviet government, 1924, 1927, 1928. Agreements of the Agro-Joint and the American Society for Jewish Farm Settlements (ASJFS) with the Soviet government, 1929, 1930, 1933, 1938. Materials relating to relief work of the JDC within the framework of the American Relief Administration, 1922, including the appointment of J. Rosen as the JDC representative at the ARA. Statistics, reports, miscellaneous correspondence relating to JDC activities in Russia. Minutes, memos, reports, legal documents, certificate of incorporation, and general correspondence relating to the ASJFS, its formation, fund-raising activities, 1927-1939. Records of the Agro-Joint Main Office, Moscow. Annual and periodi c reports of the Agro-Joint including statistics, financial estimates, financial reports, analyses of expenditures, relating to Agro-Joint work, 1924-1937. General correspondence files: incoming and outgoing letters, reports, and memoranda. Materials relating to land surveys and allocations in the Crimea: statistics, surveys, memos, correspondence, relating to the Salsk district, Chernomor district, Changar peninsula, Azov, Kuban, Odessa district, Samara district, Povolzhe, Krivoy Rog, Kherson, The Far East, Siberia. Materials relating to contacts with KOMZET. Correspondence, minutes of KOMZET meetings, statistical information, reports. By-laws of the OZET (Obshchestvo po Zemleustroystvu Trudyachtchikhsya Evreev - Association For the Settlement of Toiling Jews On Land) and AGRO-KUSTBANK (Evreysky Agrarno-Kustarny Bank - Jewish Agricultural and House Workers Bank). Register of Agro-Joint assets transferred to KOMZET. Records of the Agro-Joint Agricultural Department. Materials

Statistical and Information-Theoretic Methods for Data-Analysis

In this Thesis, we develop theory and methods for computational data analysis. The problems in data analysis are approached from three perspectives: statistical learning theory, the Bayesian framework, and the information-theoretic minimum description length (MDL) principle. Contributions in statistical learning theory address the possibility of generalization to unseen cases, and regression analysis with partially observed data with an application to mobile device positioning. In the second part of the Thesis, we discuss so called Bayesian network classifiers, and show that they are closely related to logistic regression models. In the final part, we apply the MDL principle to tracing the history of old manuscripts, and to noise reduction in digital signals.

Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Bringing nature to schools to promote children’s physical activity

Physical activity (PA) is essential for human health and wellbeing across all age, socioeconomic and ethnic groups. Engagement with the natural world is a new defining criterion for enhancing the benefits of PA particularly for children and young people. Interacting with nature benefits children’s social and emotional wellbeing, develops resilience and reduces the risk of obesity and type 2 diabetes across all population groups. Governments around the world are now recognising the importance of children spending more active time outdoors. However, children’s outdoor activities, free play and nature-related exploration are often structured and supervised by adults due to safety concerns and risks. In this context schools become more accessible and safe options for children to engage in PA outdoors with the presence of nature features. Research on school designs involving young children has revealed that children prefer nature-related features in school environments. Affordances in nature may increase children’s interest in physically active behaviours. Given that present school campuses are designed for operational efficiency and economic reasons there is a need to re-design schools responding to the positive role of nature on human health. If schools were re-designed to incorporate diverse natural features children’s PA and consequent health and wellbeing would likely improve markedly.

Stalinismin lyhyt kurssi : Suomalaiset Moskovan Lenin-koulussa 1926-1938

This study of the Finns at the International Lenin School (ILS) reflects history of the Soviet Union during Stalin's era, history of the Communist International (Comintern) as well as history of Finnish communism. The life span of the ILS (1926-1938) matches up with creating and establishing the power structures of Stalinism. Both the ILS and Finnish Communism in the USSR became casualties of the Great Terror (1937-1938). After the WW2, however, the Soviet education was appreciated inside the Communist Party of Finland (CPF). If Finland would have become People's Democracy, the former ILS students would have composed the inner circle of the new "democratic" government. The Finnish teachers of the ILS were leaders of the CPF that was headquartered in Moscow. At the ILS studied in total 141 Finnish communists. The purpose of the ILS was to educate the communist parties' leading stratum of functionaries. They were supposed to internalize current values, methods and discipline of the Bolsheviks. This study evaluates the effects of the total school experience on the Finns that often ended in another total institution in Finland: prison. The curricula of the ILS consisted of theory of Marxism-Leninism, party history, political economics and themes of campaigns of Stalinism. The ILS year included participation in Bolshevik party life and practical work. During summer excursions (praktikas) the students could acquaint themselves with building of socialism in the Soviet Republics. At the ILS, intention to ideological moulding was not hidden. The students were supposed to adopt the Stalinist identity of the professional revolutionaries of the era. The ILS was saturated with ideology and propaganda. This study analyzes especially uses of history as vehicle of ideological standardisation and as instrument of power. Stalin contributed personally to shortcomings of history writing of the communist party. Later he supervised writing of the inclusive handbook of communism, "History of the All-Union Communist Party. Short Course". Special attention will be paid to the effects of Stalin's intervention at the ILS and inside the CPF. The life of the Finns at the ILS and outside the school is described at grass roots. The dividing line between personal and political is analyzed by charting emotional, intimate and bodily experiences of the Finns of the ILS. The fates of the ILS Finns after the studying or teaching period in Moscow are explored in detail. The protagonist among the teachers is Yrjö Sirola that was called "father of the CPF cadres". The Finnish ILS teachers and the formed students that had remained in the USSR were most severely hit by the Great Terror. The Soviet education had most importance in Finland of post WW2 period. The training at the ILS, however, did not contribute to revolution in Finland. The main heading of the study, "A Short Course of Stalinism", crystallises interpretation of the ILS as seat of learning of ideological unity of Stalinism. On the other hand, the title includes a statement of incompleteness of the Stalinist education if the schooling at the ILS had remained in one year.

Learning with a mutualistic teacher

The concept of a “mutualistic teacher” is introduced for unsupervised learning of the mean vectors of the components of a mixture of multivariate normal densities, when the number of classes is also unknown. The unsupervised learning problem is formulated here as a multi-stage quasi-supervised problem incorporating a cluster approach. The mutualistic teacher creates a quasi-supervised environment at each stage by picking out “mutual pairs” of samples and assigning identical (but unknown) labels to the individuals of each mutual pair. The number of classes, if not specified, can be determined at an intermediate stage. The risk in assigning identical labels to the individuals of mutual pairs is estimated. Results of some simulation studies are presented.