Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

The development and evaluation of exercises in meaningful word practice in first grade,

Research chapter for this study will be found in Ash, Dorothea: "Development and evaluation of silent reading exercises in grade one" Thesis (M.A.)--Boston Universit

A comparative study compiled from a survey taken in Middleton, Rhode Island concerning educational experiences for preschool children in the home,

Thesis (M.A.)--Boston Universit

Two Words, One Meaning: Evidence of Automatic Co-Activation of Translation Equivalents

Research on the processing of translations offers important insights on how bilinguals negotiate the representation of words from two languages in one mind and one brain. Evidence so far has shown that translation equivalents effectively activate each other as well as their shared concept even when translations lack of any formal overlap (i.e., non-cognates) and even when one of them is presented subliminally, namely under masked priming conditions. In the lexical decision studies testing masked translation priming effects with unbalanced bilinguals a remarkably stable pattern emerges: larger effects in the dominant (L1) to the non-dominant (L2) translation direction, than vice versa. Interestingly, this asymmetry vanishes when simultaneous and balanced bilinguals are tested, suggesting that the linguistic profile of the bilinguals could be determining the pattern of cross-language lexico-semantic activation across the L2 learning trajectory. The present study aims to detect whether L2 proficiency is the critical variable rendering the otherwise asymmetric cross-language activation of translations obtained in the lexical decision task into symmetric. Non-cognate masked translation priming effects were examined with three groups of Greek (L1)–English (L2) unbalanced bilinguals, differing exclusively at their level of L2 proficiency. Although increased L2 proficiency led to improved overall L2 performance, masked translation priming effects were virtually identical across the three groups, yielding in all cases significant but asymmetric effects (i.e., larger effects in the L1 → L2 than in the L2 → L1 translation direction). These findings show that proficiency does not modulate masked translation priming effects at intermediate levels, and that a native-like level of L2 proficiency is needed for symmetric effects to emerge. They furthermore, pose important constraints on the operation of the mechanisms underlying the development of cross-language lexico-semantic links

Polymer Nanocomposite Materials Based on Carbon Nanotubes

(Invited Book Chapter

Can Clinical and Surgical Parameters Be Combined to Predict How Long It Will Take a Tibia Fracture to Heal? A Prospective Multicentre Observational Study: The FRACTING Study

Healing of tibia fractures occurs over a wide time range of months, with a number of risk factors contributing to prolonged healing. In this prospective, multicentre, observational study, we investigated the capability of FRACTING (tibia FRACTure prediction healING days) score, calculated soon after tibia fracture treatment, to predict healing time. Methods: The study included 363 patients. Information on patient health, fracture morphology, and surgical treatment adopted were combined to calculate the FRACTING score. Fractures were considered healed when the patient was able to fully weight-bear without pain. Results: 319 fractures (88%) healed within 12 months from treatment. Forty-four fractures healed after 12 months or underwent a second surgery. FRACTING score positively correlated with days to healing: r = 0.63 (p < 0.0001). Average score value was 7.3 ± 2.5; ROC analysis showed strong reliability of the score in separating patients healing before versus after 6 months: AUC = 0.823. Conclusions: This study shows that the FRACTING score can be employed both to predict months needed for fracture healing and to identify immediately after treatment patients at risk of prolonged healing. In patients with high score values, new pharmacological and nonpharmacological treatments to enhance osteogenesis could be tested selectively, which may finally result in reduced disability time and health cost savings

Multi-label classification using ensembles of pruned sets

This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods