A Wasserstein distributionally robust planning model for renewable sources and energy storage systems under multiple uncertainties

Nowadays, electricity markets and carbon trading mechanisms can promote investment in renewable sources but also generate new uncertainties in decision-making. In this paper, a two-stage Wasserstein distributionally robust optimization (WDRO) model is presented to determine the optimal planning strategy for renewable energy generators (REGs) and energy storage systems (ESSs) in the distribution network. This model considers supply-side and demand-side uncertainties in the distribution network and the interaction uncertainty from the main grid which are depicted by the ambiguity sets based on the Wasserstein metric and historical data. Meanwhile, both 1-norm and -norm Wasserstein metric constraints are considered to satisfy the decision-makers different preference. Furthermore, to solve this WDRO model, a systematic solution method with a three-step process is developed. Numerical results from a modified IEEE 33-node system and a 130-node system in the real world demonstrate the advantages of the two-stage WDRO model and the effectiveness of the solution method.</p

Instance Neural Radiance Field

This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as Instance Neural Radiance Field, or Instance NeRF. Taking a NeRF pretrained from multi-view RGB images as input, Instance NeRF can learn 3D instance segmentation of a given scene, represented as an instance field component of the NeRF model. To this end, we adopt a 3D proposal-based mask prediction network on the sampled volumetric features from NeRF, which generates discrete 3D instance masks. The coarse 3D mask prediction is then projected to image space to match 2D segmentation masks from different views generated by existing panoptic segmentation models, which are used to supervise the training of the instance field. Notably, beyond generating consistent 2D segmentation maps from novel views, Instance NeRF can query instance information at any 3D point, which greatly enhances NeRF object segmentation and manipulation. Our method is also one of the first to achieve such results without ground-truth instance information during inference. Experimented on synthetic and real-world NeRF datasets with complex indoor scenes, Instance NeRF surpasses previous NeRF segmentation works and competitive 2D segmentation methods in segmentation performance on unseen views. See the demo video at https://youtu.be/wW9Bme73coI

Controllable thioester-based hydrogen sulfide slow-releasing donors as cardioprotective agents

Hydrogen sulfide (H2S) is an important signaling molecule with promising protective effects in many physiological and pathological processes. However, the study of H2S has been impeded by the lack of appropriate H2S donors that could mimic its slow-releasing process in vivo. Herein, we report the rational design, synthesis, and biological evaluation of a series of thioester-based H2S donors. These cysteine-activated H2S donors release H2S in a slow and controllable manner. Most of the donors comprising an allyl moiety showed significant cytoprotective effects in H9c2 cellular models of oxidative damage. The most potent donor 5e decreased the mitochondrial membrane potential (MMP) loss and lactate dehydrogenase (LDH) release in H2O2-stimulated H9c2 cells. More importantly, donor 5e exhibited a potent cardioprotective effect in an in vivo myocardial infarction (MI) mouse model by reducing myocardial infarct size and cardiomyocyte apoptosis. Taken together, our studies demonstrated that these new allyl thioesters are potential cardioprotective agents by releasing H2S

NeRF-RPN: A general framework for object detection in NeRFs

This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all bounding boxes of objects in a scene. By exploiting a novel voxel representation that incorporates multi-scale 3D neural volumetric features, we demonstrate it is possible to regress the 3D bounding boxes of objects in NeRF directly without rendering the NeRF at any viewpoint. NeRF-RPN is a general framework and can be applied to detect objects without class labels. We experimented the NeRF-RPN with various backbone architectures, RPN head designs and loss functions. All of them can be trained in an end-to-end manner to estimate high quality 3D bounding boxes. To facilitate future research in object detection for NeRF, we built a new benchmark dataset which consists of both synthetic and real-world data with careful labeling and clean up. Please click https://youtu.be/M8_4Ih1CJjE for visualizing the 3D region proposals by our NeRF-RPN. Code and dataset will be made available

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability

The Effect of Reverse Strain on Microstructure and Strengthening of Copper Fabricated by Severe Plastic Deformation of Torsion Process

The unidirectional single torsion process of commercially pure copper was followed by different number of reverse turns of torsion deformation. The effect of reverse strain on the material refinement and hardening was investigated. It is found that the grain refinement is significantly blocked in the reverse torsion strain in comparison with that only suffered in monotonic torsion strain. The strengthening slightly decreases with the torsional direction change. This phenomenon is interpreted in terms of the average dislocation density. A qualitative assumption is proposed to explain the retarded phenomenon of material refinement and hardening in the reverse torsion process. The reverse strain maybe improves the uniformity and stress-strain equilibrium of severe plastic deformation induced material.DOI: http://dx.doi.org/10.5755/j01.ms.24.3.18414</p