Exercise for the management of type 2 diabetes: A review of the evidence

The aim is to critically review the more relevant evidence on the interrelationships between exercise and metabolic outcomes. The research questions addressed in the recent specific literature with the most relevant randomized controlled trials, meta-analysis and cohort studies are presented in three domains: aerobic exercise, resistance exercise, combined aerobic and resistance exercise. From this review appear that the effects of aerobic exercise are well established, and interventions with more vigorous aerobic exercise programs resulted in greater reductions in HbA1c, greater increase in VO2max and greater increase in insulin sensitivity. Considering the available evidence, it appears that resistance training could be an effective intervention to help glycemic control, especially considering that the effects of this form of intervention are comparable with what reported with aerobic exercise. Less studies have investigated whether combined resistance and aerobic training offers a synergistic and incremental effect on glycemic control; however, from the available evidences appear that combined exercise training seems to determine additional change in HbA1c that can be seen significant if compared with aerobic training alone and resistance training alone.

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming

Srinivasan, A., King, R. D. and Bain, M.E. (2003) An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. Journal of Machine Learning Research. 4(Jul):369-383

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Q. Meng and M. H Lee, Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks, Connection Science, 19(1), pp 25-52, 2007.

Biologically inspired automatic construction of cross-modal mapping in robotic eye/hand systems

Q. Meng and M.H. Lee, 'Biologically inspired automatic construction of cross-modal mapping in robotic eye/hand systems', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006,) ,4742-49, Beijing, 2006.

Comparison of the Lactate Pro and Analox GM7 blood lactate analysers

van Someren KA, Howatson G, Nunan D, Thatcher R, Shave R., Comparison of the Lactate Pro and Analox GM7 blood lactate analysers, Int J Sports Med. 2005 Oct;26(8):657-61. RAE2008

A modified TRIMP to quantify the in-season training load of team sport players

Thatcher, Rhys, et al., 'A modified TRIMP to quantify the in-season training load of team sport players', Journal of Sport Sciences, (2007) 25(6) pp.629-634 RAE2008

Constructing Universities as Organizations: University Reforms in Poland in the Light of Institutional Theory

This chapter shows that apart from changes at the systemic and institutional levels, successful reform implementation struggles with a gradual change in academic beliefs, attitudes and behaviours. Currently, visions of the university proposed by the Polish academic community and visions of it proposed by Polish reformers and policymakers (within ongoing reforms) are worlds apart. I shall study recent reforms in the context of specific academic self--protective narratives being produced in the last two decades (at the collective level of the academic profession) and in the context of the Ivory Tower university ideals predominant at the individual level (as studied comparatively through a large--scale European survey of the academic profession). Institutions change both swiftly, radically – and slowly, gradually. Research literature on institutional change until recently was focused almost exclusively on the role of radical changes caused by external shocks, leading to radical institutional reconfigurations. And research literature about the gradual, incremental institutional change have been emergent for about a decade and a half now (Mahoney and Thelen 2010; Streeck and Thelen 2005, 2009; Thelen 2003). Polish higher education provides interesting empirical grounds to test institutional theories. Both types of transformations (radical and gradual) may lead to equally permanent changes in the functioning of institutions, equally deep transformations of their fundamental rules, norms and operating procedures. Questions about institutional change are questions about characteristics of institutions undergoing changes. Endogenous institutional change is as important as exogenous change (Mahoney and Thelen 2010: 3). Moments in which there emerge opportunities of performing deep institutional reforms are short (in Poland these moments occurred in 2009-2012), and between them there are long periods of institutional stasis and stability (Pierson 2004: 134-135). The premises of theories of institutional change can be applied systematically to a system of higher education which shows an unprecedented rate of change and which is exposed to broad, fundamental reform programmes. There are many ways to discuss the Kudrycka reforms - and "constructing Polish universities as organizations" (rather than traditional academic "institutions") is one of more promising. In this account, Polish universities are under construction as organizations, and under siege as institutions. They are being rationalized as organizations, following instrumental rather than institutional logics. Polish academics in their views and attitudes are still following an institutional logic, while Polish reforms are following the new (New Public Management-led) instrumental logics. Both are on a collision course about basic values. Reforms and reformees seem to be worlds apart. I am discussing the the two contrasting visions of the university and describing the Kudrycka reforms as the reistitutionalization of the research mission of Polish universities. The core of reforms is a new level of funding and governance - the intermediary one (and no longer the state one), with four new peer-run institutions, with the KEJN, PKA and NCN in the lead. Poland has been beginning to follow the "global rules of the academic game" since 2009. I am also discussing two academic self-protection modes agains reforms: (Polish) "national academic traditions" and "institutional exceptionalism" (of Polish HE). Both discourses prevailed for two decades, none seems socially (and politically) acceptable any more. Old myths do not seem to fit new realities. In this context I am discussing briefly and through large-scale empirical data the low connectedness to the outside world of Polish HE institutions, low influence of the government on HE policies and the low level of academic entrepreneurialism, as seen through the EUROAC/CAP micro-level data. The conclusion is that the Kudrycka reforms are an imporant first step only - Poland is too slow in reforms, and reforms are both underfunded and inconsistent. Poland is still accumulating disadvantages as public funding and university reforms have not reached a critical point. Ever more efforts lead to ever less results, as macro-level data show. Consequently, it may be useful to construct universities as organizations in Poland to a higher degree than elsewhere in Europe, and especially in Western Europe.

Restauração directa de classes II em pré-molares com compósitos

Tese de Doutoramento apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em em Biotecnologia e Saúde, Epidemiologia e Saúde Pública.

A Pragmatic Approach to DHT Adoption

Despite the peer-to-peer community's obvious wish to have its systems adopted, specific mechanisms to facilitate incremental adoption have not yet received the same level of attention as the many other practical concerns associated with these systems. This paper argues that ease of adoption should be elevated to a first-class concern and accordingly presents HOLD, a front-end to existing DHTs that is optimized for incremental adoption. Specifically, HOLD is backwards-compatible: it leverages DNS to provide a key-based routing service to existing Internet hosts without requiring them to install any software. This paper also presents applications that could benefit from HOLD as well as the trade-offs that accompany HOLD. Early implementation experience suggests that HOLD is practical.

On the Origin of Power Laws in Internet Topologies

Recent empirical studies have shown that Internet topologies exhibit power laws of the form for the following relationships: (P1) outdegree of node (domain or router) versus rank; (P2) number of nodes versus outdegree; (P3) number of node pairs y = x^α within a neighborhood versus neighborhood size (in hops); and (P4) eigenvalues of the adjacency matrix versus rank. However, causes for the appearance of such power laws have not been convincingly given. In this paper, we examine four factors in the formation of Internet topologies. These factors are (F1) preferential connectivity of a new node to existing nodes; (F2) incremental growth of the network; (F3) distribution of nodes in space; and (F4) locality of edge connections. In synthetically generated network topologies, we study the relevance of each factor in causing the aforementioned power laws as well as other properties, namely diameter, average path length and clustering coefficient. Different kinds of network topologies are generated: (T1) topologies generated using our parametrized generator, we call BRITE; (T2) random topologies generated using the well-known Waxman model; (T3) Transit-Stub topologies generated using GT-ITM tool; and (T4) regular grid topologies. We observe that some generated topologies may not obey power laws P1 and P2. Thus, the existence of these power laws can be used to validate the accuracy of a given tool in generating representative Internet topologies. Power laws P3 and P4 were observed in nearly all considered topologies, but different topologies showed different values of the power exponent α. Thus, while the presence of power laws P3 and P4 do not give strong evidence for the representativeness of a generated topology, the value of α in P3 and P4 can be used as a litmus test for the representativeness of a generated topology. We also find that factors F1 and F2 are the key contributors in our study which provide the resemblance of our generated topologies to that of the Internet.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequence to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.

Navite: A Neural Network System For Sensory-Based Robot Navigation

A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.

A Fuzzy ARTMAP Nonparametric Probability Estimator For Nonstationary Pattern Recognition Problem

An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is introduced. In slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In max-nodes mode, the network initially learns a fixed number of categories, and weights are then adjusted gradually.