A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI) value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network

Conductive framework of inverse opal structure for sulfur cathode in lithium-sulfur batteries

As a promising cathode inheritor for lithium-ion batteries, the sulfur cathode exhibits very high theoretical volumetric capacity and energy density. In its practical applications, one has to solve the insulating properties of sulfur and the shuttle effect that deteriorates cycling stability. The state-of-the-art approaches are to confine sulfur in a conductive matrix. In this work, we utilize monodisperse polystyrene nanoparticles as sacrificial templates to build polypyrrole (PPy) framework of an inverse opal structure to accommodate (encapsulate) sulfur through a combined in situ polymerization and melting infiltration approach. In the design, the interconnected conductive PPy provides open channels for sulfur infiltration, improves electrical and ionic conductivity of the embedded sulfur, and reduces polysulfide dissolution in the electrolyte through physical and chemical adsorption. The flexibility of PPy and partial filling of the inverse opal structure endure possible expansion and deformation during long-term cycling. It is found that the long cycling stability of the cells using the prepared material as the cathode can be substantially improved. The result demonstrates the possibility of constructing a pure conductive polymer framework to accommodate insulate sulfur in ion battery applications

Conductive framework of inverse opal structure for sulfur cathode in lithium-sulfur batteries

As a promising cathode inheritor for lithium-ion batteries, the sulfur cathode exhibits very high theoretical volumetric capacity and energy density. In its practical applications, one has to solve the insulating properties of sulfur and the shuttle effect that deteriorates cycling stability. The state-of-the-art approaches are to confine sulfur in a conductive matrix. In this work, we utilize monodisperse polystyrene nanoparticles as sacrificial templates to build polypyrrole (PPy) framework of an inverse opal structure to accommodate (encapsulate) sulfur through a combined in situ polymerization and melting infiltration approach. In the design, the interconnected conductive PPy provides open channels for sulfur infiltration, improves electrical and ionic conductivity of the embedded sulfur, and reduces polysulfide dissolution in the electrolyte through physical and chemical adsorption. The flexibility of PPy and partial filling of the inverse opal structure endure possible expansion and deformation during long-term cycling. It is found that the long cycling stability of the cells using the prepared material as the cathode can be substantially improved. The result demonstrates the possibility of constructing a pure conductive polymer framework to accommodate insulate sulfur in ion battery applications.ISSN:2045-232

Combination effects of graphene and layered double hydroxides on intumescent flame-retardant poly(methyl methacrylate) nanocomposites

A novel intumescent flame-retardant poly(methyl methacrylate) (PMMA) nanocomposite has been prepared via in situ polymerization by incorporating intumescent flame retardants (IFRs), graphene and layered double hydroxides (LDHs). Results from X-ray diffraction (XRD) and transmission electron microscopy (TEM) indicate that a fine dispersion of IFR particles, intercalated LDHs and exfoliated graphene is achieved in the PMMA matrix. Thermal and flammability properties of PMMA nanocomposite were investigated using thermogravimetry, cone calorimetry, limiting oxygen index (LOI) and vertical burning (UL-94). The use of IFRs in combination with graphene and LDHs in the PMMA matrix improves greatly the thermal stability and flame retardant properties of the nanocomposites. The PMMA/IFR/RGO/LDH nanocomposites, filled with 10. wt.% IFRs, 1. wt.% graphene and 5. wt.% LDHs, achieve the LOI value of 28.2% and UL-94 V1 grade. Compared with neat PMMA, the PHRR of PMMA/IFRs/RGO/LDHs is reduced by about 45%, while the mechanical properties of PMMA/IFR/RGO/LDH nanocomposites exhibit almost no deterioration. The results from scanning electronic microscopy (SEM) confirm that the compact and dense intumescent char enhanced with LDHs and graphene nanosheets is formed for the PMMA/IFR/RGO/LDH nanocomposites during combustion, which inhibits the transmission of heat and mass when exposed to flame or heat source, and thus improves the flame retardant properties of the nanocomposites

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%

Two-dimensional grating interferometer with nanometer accuracy

A two-dimensional grating interferometer with the nanometer accuracy is proposed, which employs a transmissive two-dimensional diffraction grating and a corner cube prism in its optical configuration. The distinctive features of this optical path configuration are as follows: (i) Fourfold optical subdivision is achieved based on the principle of secondary diffraction, enhancing optical subdivision capability. (ii) The utilization of a corner cube prism configuration enables parallel retroreflection of diffracted light, facilitating ease of optical alignment. Utilizing a two-dimensional transmissive grating with a period of 4 µm (resulting in an optical signal period of 1 µm after fourfold optical subdivision), the two-dimensional grating interferometer achieves measurement resolutions better than 8 nm along the x and y directions. Within a 100 µm range, the grating displacement errors are superior to ±38 and ±36 nm. The repeatability (standard deviation of error) during a reciprocating motion with a 4 µm stroke is superior to 14 nm. The proposed two-dimensional grating interferometer enables nanoscale resolution measurements, showcasing notable linear measurement capabilities and repeatability. It finds potential applications in ultra-accuracy positioning and machining equipment. The utilization of gratings with smaller periods holds the promise of realizing picometer-level measurement potential

A recombinant spike‐XBB.1.5 protein vaccine induces broad‐spectrum immune responses against XBB.1.5‐included Omicron variants of SARS‐CoV‐2

Abstract The XBB.1.5 subvariant has drawn great attention owing to its exceptionality in immune evasion and transmissibility. Therefore, it is essential to develop a universally protective coronavirus disease 2019 vaccine against various strains of Omicron, especially XBB.1.5. In this study, we evaluated and compared the immune responses induced by six different spike protein vaccines targeting the ancestral or various Omicron strains of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in mice. We found that spike‐wild‐type immunization induced high titers of neutralizing antibodies (NAbs) against ancestral SARS‐CoV‐2. However, its activity in neutralizing Omicron subvariants decreased sharply as the number of mutations in receptor‐binding domain (RBD) of these viruses increased. Spike‐BA.5, spike‐BF.7, and spike‐BQ.1.1 vaccines induced strong NAbs against BA.5, BF.7, BQ.1, and BQ.1.1 viruses but were poor in protecting against XBB and XBB.1.5, which have more RBD mutations. In sharp contrast, spike‐XBB.1.5 vaccination can activate strong and broadly protective immune responses against XBB.1.5 and other common subvariants of Omicron. By performing correlation analysis, we found that the NAbs titers were negatively correlated with the number of RBD mutations in the Omicron subvariants. Vaccines with more RBD mutations can effectively overcome the immune resistance caused by the accumulation of RBD mutations, making spike‐XBB.1.5 the most promising vaccine candidate against universal Omicron variants