Bond performance between NSM FRP rods and concrete using ECC as bonding materials

The pull-out test of near-surface-mounted (NSM) FRP (fiber-reinforced plastics) rod from concrete was performed using engineered cementitious composites (ECC) as bonding materials. The feasibility of cementitious materials in NSM FRP strengthened concrete was then analyzed. Carbon FRP (CFRP) rods and Basalt FRP (BFRP) rods with spiral surfaces and diameters of 8 mm were used in the test. The bonding lengths are 5 times and 10 times of the FRP diameter, respectively. Results show that the failure modes of all the specimens using ECC as bonding materials are pull-out of FRP rods with ductile behavior. Moreover, specimens with NSM FRP rods using epoxy are prepared as control specimens to evaluate the feasibility of ECC. For CFRP rods, the pull-out load-bearing capacity of specimens using ECC is 70% and 50% of that in specimens using epoxy for 5 times and 10 times of the FRP diameter, respectively. For BFRP rods, the load-bearing capacity of specimens using ECC is 75% and 55% of that in specimens using epoxy for 5 times and 10 times of the FRP diameter, respectively. Thus, ECC can be applied in NSM FRP strengthened concrete structures as the bonding materials

Coating titania nanoparticles with epoxy-containing catechol polymers via Cu(0)-living radical polymerization as intelligent enzyme carriers

Immobilization of enzyme could offer the biocatalyst with increased stability and important recoverability, which plays a vital role in the enzyme’s industrial applications. In this study, we present a new strategy to build an intelligent enzyme carrier by coating titania nanoparticles with thermoresponsive epoxy-functionalized polymers. Zero-valent copper-mediated living radical polymerization (Cu(0)-LRP) was utilized herein to copolymerize N-isopropylacrylamide (NIPAM) and glycidyl acrylate (GA) directly from an unprotected dopamine-functionalized initiator to obtain an epoxy-containing polymer with terminal anchor for the “grafting to” or “one-pot” modification of titania nanoparticles. A rhodamine B-labeled laccase has been subsequently used as a model enzyme for successful immobilization to yield an intelligent titania/laccase hybrid bifunctional catalyst. The immobilized laccase has shown excellent thermal stability under ambient or even relatively high temperature above the lower critical solution temperature (LCST) at which temperature the hybrid particles could be facilely recovered for reuse. The enzyme activity could be maintained during the repeated use after recovery and enzymatic degradation of bisphenol A was proven to be efficient. The photocatalytic ability of titania was also investigated by fast degradation of rhodamine B under the excitation of simulated sunlight. Therefore, this study has provided a facile strategy for the immobilization of metal oxide catalysts with enzymes, which constructs a novel bifunctional catalyst that will be promising for the “one-pot” degradation of different organic pollutants

DiffusionPhase: Motion Diffusion in Frequency Domain

In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences, due to limited text-to-motion datasets and the pose representations used that often lack expressiveness or compactness. To address these issues, we propose the first method for text-conditioned human motion generation in the frequency domain of motions. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space with high-frequency details encoded, capturing the local periodicity of motions in time and space with high accuracy. We also introduce a conditional diffusion model for predicting periodic motion parameters based on text descriptions and a start pose, efficiently achieving smooth transitions between motion sequences associated with different text descriptions. Experiments demonstrate that our approach outperforms current methods in generating a broader variety of high-quality motions, and synthesizing long sequences with natural transitions

Short-term PV power prediction based on the 24 traditional Chinese solar terms and adaboost-GA-BP model

High-precision, short-term power forecasting for photovoltaic systems not only reduces unnecessary energy consumption but also provides power grid security. To this end, in this paper we propose a photovoltaic short-term power forecasting model based on the division of data of the 24 traditional Chinese solar terms and the Adaboost-GA-BP model. The 24 solar terms were condensed from the laws of meteorology, phenology, and seasonal changes to adapt to agricultural times in ancient China and have become intangible cultural heritage. This article first analyzes the numerical characteristics of meteorological factors and demonstrates their close correlation with the turning points of the 24 solar terms. Second, using Standardized Euclidean Distance and Spearman’s Correlation Coefficients to analyze data similarity between the Gregorian half-months and the 24 solar terms divisions for comparative analysis purposes, it is shown that the intragroup data under the division of the 24 solar terms have a higher similarity, leading to an average decrease of 15.68%, 40.57%, 14.68%, and 14.64% in the MAE, MSE, RMSE, and WMAPE of the predicted results, respectively. Finally, based on the data derived from the 24 solar terms, the combined algorithm was compared with the Adaboost-GA-BP model and then was verified. The genetic algorithm and Adaboost were used to optimize the BP neural network algorithm in initial value assignment and neural network structure, resulting in a 23.42%, 18.12%, and 22.28% reduction in the mean values of the MAE, RMSE, and WMAPE of the predicted results, respectively. Analysis of the results show that using the Adaboost-GA-BP model based on the 24 solar terms for short-term photovoltaic power forecasting can improve the accuracy of photovoltaic power forecasting and significantly improve the predictive performance of the model

Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture

We propose a novel neural speaker diarization system using memory-aware multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S), which integrates the strengths of memory-aware multi-speaker embedding (MA-MSE) and sequence-to-sequence (Seq2Seq) architecture, leading to improvement in both efficiency and performance. Next, we further decrease the memory occupation of decoding by incorporating input features fusion and then employ a multi-head attention mechanism to capture features at different levels. NSD-MS2S achieved a macro diarization error rate (DER) of 15.9% on the CHiME-7 EVAL set, which signifies a relative improvement of 49% over the official baseline system, and is the key technique for us to achieve the best performance for the main track of CHiME-7 DASR Challenge. Additionally, we introduce a deep interactive module (DIM) in MA-MSE module to better retrieve a cleaner and more discriminative multi-speaker embedding, enabling the current model to outperform the system we used in the CHiME-7 DASR Challenge. Our code will be available at https://github.com/liyunlongaaa/NSD-MS2S.Comment: Submitted to ICASSP 202

Investigating the driving forces of NOx generation from energy consumption in China

In China, nitrogen oxide (NOx) emissions have been declining in recent years, whereas NOx generation continues to increase. This has prompted a growing focus of policy design to inspect the driving mechanisms of NOx generation. In this study, a decomposition model of NOx generation in China from 1995 to 2014 was built using the Logarithmic Mean Divisia Index (LMDI) method. According to the decomposition results, technological effects (e.g., energy intensity and the sector generation factor) inhibited NOx generation in China, while gross domestic product (GDP) per capita was found to have the most positive effect on increasing NOx generation, accounting for 151.00% of the total change and showing an increasing trend in recent years. The sector structure of energy consumption always increased NOx generation, which contradicts the results of previous studies. All population effects considered in this study contributed to the growth in NOx generation. The population scale effect was increasingly impactful on the growth of NOx generation; the population spatial structure was active but less impactful. In general, technological impact cannot offset the increases caused by economic, structural, and population effects. Considering NOx reduction policy in China, more attention should be given to emission reduction policies, energy consumption, and socio-economic effects; together, these approaches will improve initiatives to reduce NOx

Morphology and elemental behavior of borosilicate glass after γ-irradiation and corrosion

Borosilicate glass is currently the best comprehensive solidified material for deep disposal of high-level radioactive waste. The stability of borosilicate glass under irradiation is an important factor affecting the leakage of radioactive isotopes into the biosphere. In this study, three-component borosilicate glass (NBS) was irradiated by γ-rays at doses of 8 kGy and 800 kGy, respectively. The leaching behaviors of the irradiated samples at different absorbed doses were studied. The surface morphologies of the samples were analyzed using optical microscopy and scanning electron microscopy (SEM). The distributions of elements were analyzed using energy dispersive X-ray spectroscopy (EDS), and the elements in solution were characterized using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The results indicate that the irradiation effects on the leaching behavior of borosilicate glass occurs at certain thresholds. When the corrosion time is prolonged, the corrosion effect gradually covers the irradiation effect of the glass. Compared with 8 kGy irradiation, NBS glass samples exhibit more significant changes in elemental behavior under 800 kGy irradiation. After high-dose irradiation, the effect of corrosion on surface morphology is more significant than at low doses, and the element leaching rate of NBS glass is higher after 7 days of corrosion