In Reunion, where the objective is a 100

This paper analyses data related to 62 human-shark interactions in New Caledonia and 60 in Reunion Island from 1980 to 2022. A database on human-shark interactions in New Caledonia and Reunion Island was developed using the same variables and similar time steps for both locations. The discussion focuses on human-shark interactions in New Caledonia, with some comparisons to those on Reunion Island where possible. Environmental, contextual and individual variables related to human-shark interactions were analysed to identify potential influencing factors. The data analysis for New Caledonia revealed some correlations between variables related to the human-shark interactions. The main results showed that there was a concomitant increase in human-shark interactions since 2010 in the bays of Noumea and in the prevalence of human-shark interactions among people practising boardsports since 2007. The year 2007 was a pivotal year with respect to differences in important parameters of human-shark interactions. The results of this comparison between Reunion Island and New Caledonia were as follows: fatal human-shark interactions occurred among individuals practising boardsports and in turbid environments, whereas nonfatal human-shark interactions among individuals engaged in spearfishing and in clear water in New Caledonia.

The present article describes a dataset encompassing model outputs generated by the Weather Research and Forecasting (WRF) regional climate model. A high-resolution (1km) downscaling simulation was performed over two tropical islands, Reunion and Mauritius, situated in the South-West Indian Ocean (SWIO), with initial and boundary conditions provided by the ERA5 reanalysis with a global resolution of 0.25° × 0.25°. The simulation used three nested domains sequentially configured with spatial resolutions of 9, 3, and 1km, respectively, with a downscaling ratio of 3. The physical configurations of this simulation were determined through previous modeling studies and sensitivity tests. The published simulation data currently covers a period of 10 years, starting from 1991 (with the possibility to be extended to 30 years). Over 60 output variables were selected for publication with open access, including those related to the intermittent energy resources (e.g., surface solar radiation and its direct/diffuse components, wind speed/direction at multiple vertical levels, and precipitation, of interest for the run-off-river hydropower), as well as the widely used climatic/meteorological variables (e.g., temperature, pressure, humidity, etc.) at a temporal resolution varying from a day up to 30 minutes. All the data are available through an open-access data server, where an intelligent algorithm is applied to simplify the download process for data users. For the first time, a long-term, high-resolution climate/meteorological dataset covering Reunion and Mauritius has been simulated and published as open-access data, yielding substantial benefits to studies on climate modeling, weather forecasting, and even those related to climate change in the SWIO region. In particular, this dataset will enable a better understanding of the temporal and spatial characteristics of intermittent climate-related energy resources, consequently facilitating their implementation towards a green and low-carbon future.

The observational data described in this article are collected at several locations in the South-West Indian Ocean (SWIO). Platforms equipped with radiometers and a weather transmitter, and located over Comoros, Madagascar, Mauritius, La Réunion and Seychelles islands, are used to measure incident global and diffuse shortwave radiation and incident global UV A + B-band radiation along with air temperature, relative humidity, wind speed and direction, air pressure and rainfall amount with a sampling frequency of 0.1 Hz. The data are stored as 1-min averages and automatically transmitted to the LE2P-ENERGY lab at the University of La Réunion. The dataset is hosted on the website: https://galilee.univ-reunion.fr, and is uploaded to Zenodo repository. Such a dataset will help in providing information related to solar energy forecasting and assessment for solar energy implementation at a regional and national level in the SWIO.

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of "glass ceiling". In order to explore this "glass ceiling" space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the "out-of-the-box" fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.

This paper aims at presenting years of solar irradiation data together with meteorological data acquisition localized in the French region of La Réunion Island (SW Indian Ocean). The publicly available data take the form of multivariate time series data with one-minute sampling rate over eight years – with still ongoing acquisition. We also present typical analytics tasks that are related to solar energy application domain as well as general time series analytics tasks that are suitable for these data. Thus, we aim at drawing the attention of the time series data mining community to these valuable data.

This paper analyses data related to the 57 shark attacks that were recorded on Reunion from 1980 to 2017, against the backdrop of an Indian Ocean island that is particularly vulnerable to shark attacks. To address this issue of vulnerability, the discussion focuses on the respective weight of environmental, contextual and individual variables. The most pertinent parameters to explain the occurrence of attacks on Reunion are as follows: time of day, month and turbidity. Two specific features of Reunion Island can be added to those: first, the high mortality rate of the attacks (46% vs a world average of 11%), and secondly, the average increase in the number of attacks between 2011 and 2017, despite the average drop in the number of ocean users. To understand and explain this rise, three variables are identified: water turbidity, swell height and victim activity. In addition, the multiple correspondence analysis, despite the limited number of attacks, provides correlations between some variables: on the one hand, attack outcome, turbidity, swell height, and, as regards attacks before or after 2011, board sports and swell height.

This paper focuses on the prediction of daily surface solar radiation maps for Reunion Island by a hybrid approach that combines principal component analysis (PCA), wavelet transform analysis, and artificial neural network (ANN). The daily surface solar radiation over 18 years (1999–2016) from CM SAF (SARAH-E with 0.05 deg × 0.05 deg spatial resolution) is first detrended using the clear sky index. Dimensionality reduction of the detrended dataset is secondly performed through PCA, which results in saving computational time by a factor of eight in comparison to not using PCA. A wavelet transform is thirdly applied onto each of the first 28 principal components (PCs) explaining 95% of the variance. The decomposed nine-wavelet components for each PC are fourthly used as input to an ANN model to perform the prediction of day-ahead surface solar radiation. The predicted decomposed components are finally returned to PCs and clear sky indices, irradiation in the end for re-mapping the surface solar radiation's distribution. It is found that the prediction accuracy is quite satisfying: root mean square error (RMSE) is 30.98 W/m2 and the (1 − RMSE_prediction/RMSE_persistence) is 0.409.

An accurate assessment of the amount solar radiation incident at specific locations is highly complex due to the dependence of available solar radiation on many meteorological and topographic parameters. Reunion Island, a small tropical French territory, intends to deploy solar energy technologies rapidly. In this context, the variability and intermittency of solar irradiance in different regions of the island is of immediate interest if the generated energy will be integrated in the existing energy network. This paper identifies different features of spatial and temporal variability of daily global horizontal irradiance (GHI) observed on Reunion Island. For this purpose, trends in the mean daily as well as seasonal variability of GHI were investigated. Furthermore, the intermittency and multifractal behaviors of the spatial daily GHI change were examined. Analyzing this daily variability is crucial to day-ahead forecasting of solar resource for better managing solar integration in the power grid, particularly in small island states with isolated power systems. Results revealed that the difference in cumulative GHI for two successive days ranges between −10 and 10 kW/m2/day while the highest and lowest variability of daily change occurs during summer and winter, respectively. The decorrelation distance, which gives a measure of the distance over which the variability at distinct geographic locations become independent of one another at a given timescale, was also calculated. It was found that the average decorrelation distance for day-to-day GHI change is about 22 km, a smaller value than that calculated by the previous studies using much sparser radiometric networks. The Hurst exponent, fractal co-dimension, and Lévy parameter, which describe solar radiation intermittency, were also evaluated for Reunion Island.

The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations i.e., phosphoglucoisomerase, phosphofructokinase, fructose-bisphosphate-aldolase, triose-phosphate-isomerase. Out of three ANN algorithms, the neuralnet package with two activation functions, “logistic” and “tanh” were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: https://github.com/DSIMB/ANN-Glycolysis-Flux-Prediction.

Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a "flow around" regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability.