An Unsupervised Deep Learning Approach for Scenario Forecasts

In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-driven, producing a set of scenarios that represent possible future behaviors based only on historical observations and point forecasts. It first applies a newly-developed unsupervised deep learning framework, the generative adversarial networks, to learn the intrinsic patterns in historical renewable generation data. Then by solving an optimization problem, we are able to quickly generate large number of realistic future scenarios. The proposed method has been applied to a wind power generation and forecasting dataset from national renewable energy laboratory. Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations. Our code and simulation datasets are freely available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available at https://github.com/chennnnnyize/Scenario-Forecasts-GA

Mechanical properties of in-situ synthesis of Ti-Ti3Al metal composite prepared by selective laser melting

Titanium composite strengthened by Ti3Al precipitations is considered to be one of the excellent materials that is widely used in engineering. In this work, we prepared a kind of Ti-Ti3Al metallic composite by in-situ synthesis technology during the SLM (selective laser melting) process, and analyzed its microstructure, wear resistance, microhardness, and compression properties. The results showed that the Ti-Ti3Al composite, prepared by in-situ synthesis technology based on SLM, had more homogeneous Ti3Al-enhanced phase dispersion strengthening structure. The grain size of the workpiece was about 1 μm, and that of the Ti3Al particle was about 200 nm. Granular Ti3Al was precipitated after the aluminum-containing workpiece formed, with a relatively uniform distribution. Regarding the mechanical properties, the hardness (539 HV) and the wear resistance were significantly improved when compared with the Cp-Ti workpiece. The compressive strength of the workpiece increased from 886.32 MPa to 1568 MPa, and the tensile strength of the workpiece increased from 531 MPa to 567 MPa after adding aluminum. In the future, the combination of in-situ synthesis technology and SLM technology can be used to flexibly adjust the properties of Ti-based materials

Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. However, POT's fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly. To address this issue, we propose the dynamic PlenOctree DOT, which adaptively refines the sample distribution to adjust to changing scene complexity. Specifically, DOT proposes a concise yet novel hierarchical feature fusion strategy during the iterative rendering process. Firstly, it identifies the regions of interest through training signals to ensure adaptive and efficient refinement. Next, rather than directly filtering out valueless nodes, DOT introduces the sampling and pruning operations for octrees to aggregate features, enabling rapid parameter learning. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$ parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$ Temples, respectively. Project homepage:https://vlislab22.github.io/DOT. [1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.Comment: Accepted by ICCV202

Compound identification of Shuangxinfang and its potential mechanisms in the treatment of myocardial infarction with depression: insights from LC-MS/MS and bioinformatic prediction

BackgroundPatients with myocardial infarction (MI) have a high incidence of depression, which deteriorates the cardiac function and increases the risk of cardiovascular events. Shuangxinfang (Psycho-cardiology Formula, PCF) was proved to possess antidepressant and cardioprotective effects post MI. However, the compounds of PCF remain unidentified, and the pertinent mechanism is still not systematic. The purpose of this study is to determine the ingredients of PCF, further to probe the underlying mechanism for MI with depression.MethodsThe compounds of PCF were qualitatively identified by LC-MS/MS. The optimal dosage for lavage with the PCF solution in rats was determined to be 1 mL/100 g/day for a duration of 5 days. We also detected the PCF components migrating to blood in the control and model rats. Then the targets of PCF compounds were searched on Swiss target database, and the targets of depression and MI were predicted on TTD, OMIM, GeneCards, DrugBank and PharmGkb database. All the targets were intersected to construct the Protein-Protein Interaction (PPI) network on Metascape platform and the herb-compound-target (HCT) network on Cytoscape, to identify the hub targets. GO and KEGG pathway enrichment analysis were conducted on DAVID platform. Molecular docking was modeled on AutoDock Vina software.ResultsThere were 142 bioactive compounds from PCF acting on 270 targets in a synergistic way. And a total of seven components migrating to blood were identified, including Miltionone I, Neocryptotanshinone, Danshenxinkun A, Ferulic acid, Valerophenone, Vanillic acid and Senkyunolide D. Then SRC and MAPK3 were obtained as the hub proteins by degree value in PPI network, and P2RY12 was picked out as seed proteins ranked by scores from MCODES. Further analysis of biological process and signaling pathways also revealed the significance of ERK/MAPK. Statistical analyses (e.g., GO and KEGG pathway enrichment, PPI network analysis) demonstrated the significance of the identified targets and pathways (p < 0.05). Molecular docking results showed that the binding energies were all less than −5 kcal/mol. The stability of Neocryptotanshinone possessed the lowest binding energy to MAPK3.ConclusionWe identified PCF’s bioactive compounds and predicted its therapeutic mechanism for MI with depression using LC-MS/MS and bioinformatics. Key targets SRC, MAPK3, and seed protein P2RY12 were crucial for PCF’s cardio-neuroprotective effects. Neocryptotanshinone showed the strongest binding to MAPK3, suggesting it as a pivotal active ingredient. These findings offer new insights and targets for future research on PCF

Deep learning integrates histopathology and proteogenomics at a pan-cancer level

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models