Today, nearly everyone accepts that in order to slow the damage we are doing to our planet and environment, humans must transition away from the use of fossil fuels. This has led to many science and business innovations as we search for new sustainable or renewable alternatives to coal, oil, and gas. Although it would be nice to think everyone wants to do their part in order to save the world, there are strong financial incentives too. The value of the renewable energy market is set to grow from $880 billion to nearly $2 trillion by 2030. And the growing awareness of the importance of environmental and social governance (ESG) issues means there are tremendous political incentives, too.

In a video that went viral on Twitter Sunday night, a massive explosion rips through what appears to be a Russian convoy, scoring a direct hit on a surface-to-air missile system. The black-and-white footage, posted to the account of the Ukrainian armed forces, is one of several that have emerged on social media in recent days showing the devastating impact of Ukrainian drone strikes on Russian hardware. As the drone's payload explodes in the video--which appears to be a cellphone recording of a screen in a Ukrainian drone facility--people at the facility can be heard gasping in awe before breaking out in cheers and applause. The video racked up more than 3 million views on Twitter in two days. There will be no peace for you on our earth!" the Ukrainian armed forces wrote in the video's caption. The star of this video and others circulating on Twitter is the Bayraktar TB2 – a type of Turkish drone that the Ukrainian military has increasingly deployed against Russian forces in recent ...

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available at https://github.com/Cybonic/DL_vineyard_segmentation_study.git

You can guarantee that any beach you walk on, you'll find pieces of plastic," said James Comerford, a senior researcher in materials and nanotechnology at SINTEF, an independent research organisation in Oslo, Norway. Plastics are estimated to comprise 85% of marine litter, with 11 million metric tonnes entering the oceans annually and the volume potentially tripling by 2040. Some have predicted that, by weight, there will be more plastics than fish in the seas by 2050. In light of the alarming outlook, innovative approaches are required to tackle the problem. This is exactly what the EU Mission "Restore our Ocean and Waters by 2030" is targeting, with the ambition of reducing plastic litter at sea by at least 50%, cutting microplastics released into the environment by 30%, and halving agricultural nutrient losses as well as the use of chemical pesticides. To reduce pollution, the Mission is launching a'lighthouse' in the Mediterranean Sea that will act as a hub to develop, ...

Whale sharks can carry up to 300 babies at once--at different fetal stages and from different fathers. Zebra sharks experience "virgin birth." These are but a mere sampling of the decade's most fascinating shark discoveries. Some 500 known species of these toothy fish ply our planet's waters, ranging from bite size to bus size, and scientists are still becoming acquainted with most of them. Since 2000, when scientists discovered shark populations were collapsing around the world, research on sharks has ramped up across many fields of study, from paleontology to neuroscience to biomechanics.

You can guarantee that any beach you walk on, you'll find pieces of plastic,' said James Comerford, a senior researcher in materials and nanotechnology at SINTEF, an independent research organisation in Oslo, Norway. Plastics are estimated to comprise 85% of marine litter, with 11 million metric tonnes entering the oceans annually and the volume potentially tripling by 2040. Some have predicted that, by weight, there will be more plastics than fish in the seas by 2050. In light of the alarming outlook, innovative approaches are required to tackle the problem. This is exactly what the EU Mission "Restore our Ocean and Waters by 2030" is targeting, with the ambition of reducing plastic litter at sea by at least 50%, cutting microplastics released into the environment by 30%, and halving agricultural nutrient losses as well as the use of chemical pesticides.

Building on recent research for prediction of hurricane trajectories using recurrent neural networks (RNNs), we have developed improved methods and generalized the approach to predict Bayesian intervals in addition to simple point estimates. Tropical storms are capable of causing severe damage, so accurately predicting their trajectories can bring significant benefits to cities and lives, especially as they grow more intense due to climate change effects. By implementing the Bayesian interval using dropout in an RNN, we improve the actionability of the predictions, for example by estimating the areas to evacuate in the landfall region. We used an RNN to predict the trajectory of the storms at 6-hour intervals. We used latitude, longitude, windspeed, and pressure features from a Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset of about 500 tropical storms in the Atlantic Ocean.

Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input.

Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.

In this paper, a deep convolutional neural network-based symbol detection and demodulation is proposed for generalized frequency division multiplexing with index modulation (GFDM-IM) scheme in order to improve the error performance of the system. The proposed method first pre-processes the received signal by using a zero-forcing (ZF) detector and then uses a neural network consisting of a convolutional neural network (CNN) followed by a fully-connected neural network (FCNN). The FCNN part uses only two fully-connected layers, which can be adapted to yield a trade-off between complexity and bit error rate (BER) performance. This two-stage approach prevents the getting stuck of neural network in a saddle point and enables IM blocks processing independently. It has been demonstrated that the proposed deep convolutional neural network-based detection and demodulation scheme provides better BER performance compared to ZF detector with a reasonable complexity increase. We conclude that non-orthogonal waveforms combined with IM schemes with the help of deep learning is a promising physical layer (PHY) scheme for future wireless networks