Specially commissioned drones will be used to fly blood donations as part of a new trial. Currently, blood donations are processed in south Wales then transported by road, a journey that can take hours. The ultimate ambition of the Dragon's Heart project is to fly life-saving blood samples to the scenes of accidents using drones weighing about 55lb (25kg) and 5.5ft wide (1.7m). The pilot, which is due to start in early 2026, was described as significant and exciting by the Welsh Blood Service. A hatch in the top means the blood sits in the body of the drone, helping to control the temperature of the blood and minimise vibrations.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper deals with the problem of identifiability in the presence of various generated mechanisms for missing data, using a framework introduced by Mohan and others. Some existing results are improved upon, and some nice examples are given (such as identifiability of causal distributions even when conditional distributions are unavailable). Quality Technical quality seems high, the results are very involved but appear to be correct. Results like Theorem 3 are clear and interpretable.

Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural disparities. We evaluate our hybrid model in an extensive case study predicting cherry tree phenology in Japan, South Korea and Switzerland. Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years. Additionally, the neural network's adaptability facilitates parameter learning for specific tree varieties, enabling robust generalization to new sites without site-specific recalibration. This hybrid model leverages both biophysical constraints and data-driven flexibility, offering a promising avenue for accurate and interpretable phenology modeling.

PS4/5, Xbox, PC, Nintendo Switch Taiwanese studio Red Candle Games broke through in 2019 with the first-person horror game, Devotion. Its follow-up, Nine Sols, is less grungy but no less distinct, a robust 2D action-platformer with an exquisite "taopunk" aesthetic. This vivid sci-fi world feels as if it is constructed as much from bamboo and jade as steel and microchips. Alongside absorbing exploration and blistering combat, you study and grow various strains of alien flora found aboard a labyrinthine spaceship. The ultimate goal is escape, but you may never actually want to leave the strange, bioluminescent garden you come to cultivate.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Long Beach's Backyard Builders Program uses one-time funding that will provide as many as 10 homeowners low-to zero-interest loans of up to $250,000 to build Accessory Dwelling Units, or ADUs, on their lots. Eager to boost the supply of affordable housing, city officials in Long Beach devised a program that could help a limited number of homeowners build an extra unit on their land. But before they could launch it, they had to decide what to call it. "We've been playing with a name for a while," Mayor Rex Richardson said, noting that a news release touting the program had been delayed days because of christening purposes.

You can view art (e.g., at a museum). "To go from photographing the getaway to making the getaway, it's like art becoming reality," the photographer Gray Malin said the other day. He was sitting behind the wheel of a blue Range Rover, dressed in a denim shirt, white jeans, and raffia loafers, cruising up the 101 to his latest work: a house he has renovated in Montecito, California, in order to rent to visitors. "How I vacation is how I want you to vacation," he said. As an artist, Malin specializes in glossy portraits of the good life: beaches, boats, a plane landing in St. Bart's.

We propose a hybrid iterative method based on MIONet for PDEs, which combines the traditional numerical iterative solver and the recent powerful machine learning method of neural operator, and further systematically analyze its theoretical properties, including the convergence condition, the spectral behavior, as well as the convergence rate, in terms of the errors of the discretization and the model inference. We show the theoretical results for the frequently-used smoothers, i.e. Richardson (damped Jacobi) and Gauss-Seidel. We give an upper bound of the convergence rate of the hybrid method w.r.t. the model correction period, which indicates a minimum point to make the hybrid iteration converge fastest. Several numerical examples including the hybrid Richardson (Gauss-Seidel) iteration for the 1-d (2-d) Poisson equation are presented to verify our theoretical results, and also reflect an excellent acceleration effect. As a meshless acceleration method, it is provided with enormous potentials for practice applications.

Causal discovery is an essential part of causal inference (Spirtes et al., 2000; Peters et al., 2017), but estimating causal effects is extremely challenging if the underlying causal graph is unknown. Algorithms for learning causal graphs are many and varied, using different parametric structure, classes of graphical models, and assumptions about whether all relevant variables are measured (Spirtes et al., 2000; Kaltenpoth and Vreeken, 2023; Claassen and Bucur, 2022; Nowzohour et al., 2017; Zhang and Hyvarinen, 2009; Peters et al., 2017). In this paper, we consider only nonparametric assumptions, i.e. conditional independences in distributions that are represented by graphs. The primary graphical model used in causal inference is the directed acyclic graph, also known as a DAG. These offer a clear interpretation and are straightforward to conduct inference with, and are associated with probabilistic distributions by encoding conditional independence constraints.