Assessment of the usability and accuracy of two-diode models for photovoltaic modules

Many diode-based equivalent circuits for simulating the electrical behaviour of photovoltaic (PV) cells and panels are reported in the scientific literature. Two-diode equivalent circuits, which require more complex procedures to calculate the seven model parameters, are less numerous. The model parameters are generally calculated using the data extracted from the datasheets issued by the PV panel manufactures and adopting simplifying hypotheses and numerical solving techniques. A criterion for rating both the usability and accuracy of two-diode models is proposed in this paper with the aim of supporting researchers and designers, working in the area of PV systems, to select and use a model that may be fit for purpose. The criterion adopts a three-level rating scale that considers the ease of finding the data used by the analytical procedure, the simplicity of the mathematical tools needed to perform calculations and the accuracy achieved in calculating the current and power. The analytical procedures, the simplifying hypotheses and the operative steps to calculate the parameters of the most famous two-diode equivalent circuits are exhaustively described in this paper. The accuracy of the models is tested by comparing the characteristics issued by the PV panel manufacturers with the current-voltage (I-V) curves, at constant solar irradiance and/or cell temperature, calculated with the analysed models with. The results of the study show that the two-diode models recently proposed reach accuracies that are comparable with the values derived from the one-diode models

Performance Differences Between the Sexes in the Boston Marathon From 1972 to 2017

Knechtle, B, Di Gangi, S, Rüst, CA, and Nikolaidis, PT. Performance differences between the sexes in the Boston Marathon from 1972 to 2017. J Strength Cond Res XX(X): 000-000, 2018-The differences between the sexes in marathon running have been investigated for athletes competing in world class-level races. However, no information exists about changes in these differences since the first women officially began participating in marathons. We examined trends in participation and performance in the Boston Marathon from 1972 to 2017. A total of 371,250 different finishers (64% men) and 553,890 observations-with 187,998 (34%) being of women and 365,892 (66%) of men-were analyzed using Generalized Additive Mixed Models. The number of finishers increased over the years. Female participation started at 2.81% in 1972 and reached 45.68% in 2016. Considering all finishers, men (03:38:42 ± 00:41:43 h:min:s) were overall faster than women (04:03:28 ± 00:38:32 h:min:s) by 10.7%. Average performance worsened over the years, but the differences between the sexes decreased. For the annual 10 fastest runners, performance improved with a decrease in speed difference (18.3% overall, men: 02:13:30 ± 00:04:08 h:min:s vs. women: 02:37:42 ± 00:17:58 h:min:s). For the annual winners, performance improved with a decrease in speed difference (15.5% overall, men: 02:10:24 ± 00:03:05 h:min:s vs. women: 02:30:43 ± 00:11:05 h:min:s). For the near-elite finishers from the 21st to the 100th place and from the 101st to the 200th place, women's performance improved with a decrease in the difference to men. In summary, the trend in performance over the years depended on the methodological approach (i.e., all vs. annual 10 fastest finishers vs. annual winners), but the difference between the sexes decreased in all instances. Although men were 10.7% faster than women, the fastest men (i.e., top 10 and winners) increased the gap between men and women by an average of 18.3% for the annual 10 fastest and 15.5% for the annual winners

Deep Neural Machine Translation with Weakly-Recurrent Units

Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation. Recently, new architectures have been proposed, which can leverage parallel computation on GPUs better than classical RNNs. Faster training and inference combined with different sequence-to-sequence modeling also lead to performance improvements. While the new models completely depart from the original recurrent architecture, we decided to investigate how to make RNNs more efficient. In this work, we propose a new recurrent NMT architecture, called Simple Recurrent NMT, built on a class of fast and weakly-recurrent units that use layer normalization and multiple attentions. Our experiments on the WMT14 English-to-German and WMT16 English-Romanian benchmarks show that our model represents a valid alternative to LSTMs, as it can achieve better results at a significantly lower computational cost

On the Importance of Word Boundaries in Character-level Neural Machine Translation

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model

Data Augmentation for End-to-End Speech Translation: FBK@IWSLT '19

This paper describes FBK’s submission to the end-to-end speech translation (ST) task at IWSLT 2019. The task consists in the “direct” translation (ie without intermediate discrete representation) of English speech data derived from TED Talks or lectures into German texts. Our participation had a twofold goal: i) testing our latest models, and ii) evaluating the contribution to model training of different data augmentation techniques. On the model side, we deployed our recently proposed S-Transformer with logarithmic distance penalty, an ST-oriented adaptation of the Transformer architecture widely used in machine translation (MT). On the training side, we focused on data augmentation techniques recently proposed for ST and automatic speech recognition (ASR). In particular, we exploited augmented data in different ways and at different stages of the process. We first trained an end-to-end ASR system and used the weights of its encoder to initialize the decoder of our ST model (transfer learning). Then, we used an English-German MT system trained on large data to translate the English side of the English-French training set into German, and used this newly-created data as additional training material. Finally, we trained our models using SpecAugment, an augmentation technique that randomly masks portions of the spectrograms in order to make them different at every training epoch. Our synthetic corpus and SpecAugment resulted in an improvement of 5 BLEU points over our baseline model on the test set of MuST-C En-De, reaching the score of 22.3 with a single end-to-end system

Compositional Source Word Representations for Neural Machine Translation

The requirement for neural machine translation (NMT) models to use fixed-size input and output vocabularies plays an important role for their accuracy and generalization capability. The conventional approach to cope with this limitation is performing translation based on a vocabulary of sub-word units that are predicted using statistical word segmentation methods. However, these methods have recently shown to be prone to morphological errors, which lead to inaccurate translations. In this paper, we extend the source-language embedding layer of the NMT model with a bi-directional recurrent neural network that generates compositional representations of the source words from embeddings of character n-grams. Our model consistently outperforms conventional NMT with sub-word units on four translation directions with varying degrees of morphological complexity and data sparseness on the source side

Risk perceptions about personal Internet-of-Things: Research directions from a multi-panel Delphi study

Internet-of-Things (IoT) research has primarily focused on identifying IoT devices\u27 organizational risks with little attention to consumer perceptions about IoT device risks. The purpose of this study is to understand consumer risk perceptions for personal IoT devices and translate these perceptions into guidance for future research directions. We conduct a sequential, mixed-methods study using multi-panel Delphi and thematic analysis techniques to understand consumer risk perceptions. The results identify four themes focused on data exposure and user experiences within IoT devices. Our thematic analysis also identified several emerging risks associated with the evolution of IoT device functionality and its potential positioning as a resource for malicious actors to conduct security attacks

Network sensitivity of systemic risk

A growing body of studies on systemic risk in financial markets has emphasized the key importance of taking into consideration the complex interconnections among financial institutions. Much effort has been put into modeling the contagion dynamics of financial shocks and into assessing the resilience of specific financial markets, either using real network data, reconstruction techniques or simple toy networks. Here, we address the more general problem of how shock propagation dynamics depend on the topological details of the underlying network. To this end, we consider different realistic network topologies, all consistent with balance sheet information obtained from real data on financial institutions. In particular, we consider networks of varying density and with different block structures. In addition, we diversify in the details of the shock propagation dynamics. We confirm that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can aid in the design of regulatory policies to improve the robustness of financial markets

FBK’s Neural Machine Translation Systems for IWSLT 2016

In this paper, we describe FBK’s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-{de, fr} and {de, fr}-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs

Sensitivity and Specificity of Soluble Triggering Receptor Expressed on Myeloid Cells-1, Midregional Proatrial Natriuretic Peptide and Midregional Proadrenomedullin for Distinguishing Etiology and to Assess Severity in Community-Acquired Pneumonia

This study aimed to evaluate the diagnostic accuracy of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), midregional proatrial natriuretic peptide (MR-proANP) and midregional proadrenomedullin (MR-proADM) to distinguish bacterial from viral community-acquired pneumonia (CAP) and to identify severe cases in children hospitalized for radiologically confirmed CAP. Index test results were compared with those derived from routine diagnostic tests, i.e., white blood cell (WBC) counts, neutrophil percentages, and serum C-reactive protein (CRP) and procalcitonin (PCT) levels.This prospective, multicenter study was carried out in the most important children’s hospitals (n = 11) in Italy and 433 otherwise healthy children hospitalized for radiologically confirmed CAP were enrolled. Among cases for whom etiology could be determined, CAP was ascribed to bacteria in 235 (54.3%) children and to one or more viruses in 111 (25.6%) children. A total of 312 (72.2%) children had severe disease.CRP and PCT had the best performances for both bacterial and viral CAP identification. The cut-off values with the highest combined sensitivity and specificity for the identification of bacterial and viral infections using CRP were ≥7.98 mg/L and ≤7.5 mg/L, respectively. When PCT was considered, the cut-off values with the highest combined sensitivity and specificity were ≥0.188 ng/mL for bacterial CAP and ≤0.07 ng/mL for viral CAP. For the identification of severe cases, the best results were obtained with evaluations of PCT and MR-proANP. However, in both cases, the biomarker cut-off with the highest combined sensitivity and specificity (≥0.093 ng/mL for PCT and ≥33.8 pmol/L for proANP) had a relatively good sensitivity (higher than 70%) but a limited specificity (of approximately 55%).This study indicates that in children with CAP, sTREM-1, MR-proANP, and MR-proADM blood levels have poor abilities to differentiate bacterial from viral diseases or to identify severe cases, highlighting that PCT maintains the main role at this regard