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Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows. [ABSTRACT FROM AUTHOR]

Mitigating the harmful effects of flooding is essential in the current climate change and urban growth scenario, where these natural phenomena are expected to occur more often. Addressing this problem, we present a novel graph-based forecasting model for predicting urban flooding and showcase its application in the Bow River in the City of Calgary, Alberta, Canada. The proposed model is based on graph theory and deep learning paradigms and was used to forecast flooding occurrences up to 24 h ahead. Our new approach developed the SAGE algorithm, a hybrid learning and planning approach that accurately predicted floodings for various analyzed forecasting horizons, increasing the forecasting performance by up to 44% compared to the persistence model. The SAGE model also returned superior or competitive results compared to other models in the literature. The superiority of the SAGE model was highlighted for longer forecast horizons of 24 h ahead, where it reached a maximum improvement of 79%, suggesting its superiority in capturing spatiotemporal information in the dataset. These results indicate that the SAGE approach is a cutting-edge tool for flooding forecasting, and it could be used to develop early flood warning systems to reduce potential flooding impacts. [ABSTRACT FROM AUTHOR]

Knowledge of lexical items is arguably the most essential aspect of being able to communicate in a foreign language (Richards, 2000). Many studies have examined effective strategies for retaining the meaning of foreign words, but studies investigating the effectiveness of different methods for the retention of essential grammatical features of those words are unfortunately fewer. This study investigates whether memorizing the gender of German nouns concomitantly to their meaning results in a decreased ability by undergraduate native speakers of English to remember noun meaning, and whether a mnemonic approach to retaining the grammatical gender of German nouns may prove effective. In possibly the largest study of its kind (n = 283), the effectiveness of several mnemonic devices (colors, voices, images, and a combination of these) is also investigated for the retention of noun gender in German. The sole use of images simultaneously coding for noun meaning and gender led to the best observed results, as indicated by scores on two post-tests. The use of images seems to be a promising mnemonic technique for helping learners remember the gender of German nouns, while at the same time not significantly compromising their ability to recall noun meaning. The results of the present study have possible implications for German materials development and teaching, and for computer assisted language learning (CALL) in general.

Knowledge of lexical items is arguably the most essential aspect of being able to communicate in a foreign language (Richards, 2000). Many studies have examined effective strategies for retaining the meaning of foreign words, but studies investigating the effectiveness of different methods for the retention of essential grammatical features of those words are unfortunately fewer. This study investigates whether memorizing the gender of German nouns concomitantly to their meaning results in a decreased ability by undergraduate native speakers of English to remember noun meaning, and whether a mnemonic approach to retaining the grammatical gender of German nouns may prove effective. In possibly the largest study of its kind (n = 283), the effectiveness of several mnemonic devices (colors, voices, images, and a combination of these) is also investigated for the retention of noun gender in German. The sole use of images simultaneously coding for noun meaning and gender led to the best observed results, as indicated by scores on two post-tests. The use of images seems to be a promising mnemonic technique for helping learners remember the gender of German nouns, while at the same time not significantly compromising their ability to recall noun meaning. The results of the present study have possible implications for German materials development and teaching, and for computer assisted language learning (CALL) in general. [ABSTRACT FROM PUBLISHER]

The article evaluates the Fluenz™ Mandarin 1+2 software from Fluenz™.