Shanghai releases white list to expedite overseas data transfers for companies – South China Morning Post
Shanghai releases white list to expedite overseas data transfers for companies South China Morning Post
Month: May 2024
EU, Japan to negotiate including cross-border data flow rules in EPA – International Association of Privacy Professionals
EU, Japan to negotiate including cross-border data flow rules in EPA International Association of Privacy Professionals
EU, Japan to negotiate including cross-border data flow rules in EPA – International Association of Privacy Professionals
EU, Japan to negotiate including cross-border data flow rules in EPA International Association of Privacy Professionals
Modelling the potential of land use change to mitigate the impacts of climate change on future drought in the Western Cape, South Africa
Abstract
Several studies have shown that climate change may enhance the severity of droughts over the Western Cape (South Africa) in the future, but there is a dearth of information on how to reduce the impacts of climate change on water yields. This study investigates the extent to which land-use changes can reduce the projected impacts of climate change on hydrological droughts in the Western Cape catchments. For the study, the Soil Water Assessment Tool (SWAT +) model was calibrated and evaluated over several river catchments, and the climate simulation dataset from the COordinated Regional Downscaling EXperiment (CORDEX) was bias-corrected. Using the bias-corrected climate data as a forcing, the SWAT + was used to project the impacts of future climate change on water yield in the catchments and to quantify the sensitivity of the projection to four feasible land-use change scenarios in the catchments. The land-use scenarios are the spread of forest (FOMI), the restoration of shrubland (SHRB), the expansion of cropland (CRDY), and the restoration of grassland (GRSL).
The model evaluation shows a good agreement between the simulated and observed monthly streamflows at four stations, and the bias correction of the CORDEX dataset improved the hydrological simulations. The climate change projection features an increase in temperature and potential evaporation, but a decrease in precipitation and all the hydrological variables. The drying occurs across the Western Cape, with the magnitude increasing with higher global warming levels (GWLs). The land-use changes alter these climate change impacts through changes in the hydrological water balance. FOMI increases streamflow and decreases runoff, while SHRB decreases streamflow and runoff. The influence of CRDY and GRSL are more complex. However, all the impacts of land-use changes are negligible compared to the impacts of climate change. Hence, land-use changes in the Western Cape may not be the most efficient strategies for mitigating the impacts of climate change on hydrological droughts over the region. The results of the study have application towards improving water security in the Western Cape river catchments.
Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living
Abstract
Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics.