Nonlocal atlas-guided multi-channel forest learning for human brain labeling: Nonlocal atlas-guided multi-channel forest learning

It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image)

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value

Firefly Algorithm-Based Semi-Supervised Learning With Transformer Method for Shore Power Load Forecasting

Load forecasting of shore power (SP) plays an important role in the power decision-making of the electrical grid due to docked ships are necessary to plug into the electrical grid. However, obtaining a large amount of labeled data on docked ships is time-consuming, presenting a challenge for Shore Power Load Forecasting. Additionally, multiple raw information entries for docked ships could lead to feature redundancy. To address these issues, we proposed a novel three-stage load forecasting method which includes attributive feature selection, semi-supervised learning (SSL) method for the mean of load distribution prediction, and a transformer-based model for variance prediction. Firstly, Firefly Algorithm (FA) is adopted to extract representative attribute features of docked ships to deal with the feature redundancy. Next, the selected feature set and label set are divided into two parts: a few labeled data and a large amount of labeled data. And we propose a $\Pi$ -model-based SSL method to predict the load distribution. Finally, we propose a transformer-based model to predict the variance of load distribution. Our model takes into account all historical load data of each docked ship for context learning. Further, we consider that the attribute features would also affect the variance prediction, so the latent features of the $\Pi$ -model are served as the initial condition which concatenates historical load data. We evaluated our model using 328 power load data from various ships that berth at Zhenjiang Port with shore power, totaling approximately 21,521 hours. The experiments prove the accuracy and efficiency of our proposed model, producing promising forecasting results

Electrooxidation of Methanol on Pt @Ni Bimetallic Catalyst Supported on Porous Carbon Nanofibers

This paper describes the preparation of Ni/Pt/CNFs via electrospinning technology, carbonization process, and chemical reduction method. The structure and composition of Ni/Pt/CNFs were characterized with X-ray diffraction, Raman spectroscopy, nitrogen adsorption isotherms, and X-ray photoelectron spectroscopy. Meanwhile, the morphology was analyzed with scanning electron microscopy and transmission electron microscopy. The electrochemical performance was evaluated by oxygen reduction reaction (ORR), cyclic voltammetry and chronopotentiometry. The results indicated that Pt and Ni nanoparticles were completely reduced in the experimental process and homogeneously distributed on the nanofibers with the average diameters of 3.8 and 17.8 nm, respectively. In addition, the Ni₅₀/Pt/CNFs catalyst showed excellent electrocatalytic performance for ORR and superior specific and mass activities for methanol oxidation (the maximum current density is ca. 10.9 mA cm^–2) and exhibited a slightly slower current decay over time, better than the reference samples which indicated a higher tolerance to CO-like intermediates