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Tb3+-activated LTA zeolite-derived boro-aluminosilicate glass samples with a composition of xTb2O3-68(Na2O-Al2O3-SiO2)–32B2O3 (x = 0.2, 1.0 and 10 extra wt%) were prepared using the melt-quenching method. The emission spectra recorded upon ultraviolet (UV) excitation with two different wavelengths of 193 and 378 nm showed blue light (5D3 to 7FJ=6,5,4 and 5D4 to 7F6 transitions of Tb3+) and green light (5D4 to 7F5 transition of Tb3+) emissions with comparable intensities up to a Tb3+ concentration of 10 extra wt%. Of note, the mean decay times of the green luminescence of the glass samples were relatively fast (<20 μs). The synthesized glass has potential in applications concerning UV imaging, UV detection, and plasma display panels. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity. [ABSTRACT FROM AUTHOR]

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model's ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect R 2 value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting. [ABSTRACT FROM AUTHOR]

Construction projects face various constraints in terms of materials, labor, equipment, and documents, which can interrupt the scheduled work. Package‐based constraint management (PCM) is a state‐of‐the‐art graph‐based approach that follows the lean theory to effectively model, monitor, and remove constraints before the commencement of work, ensuring smooth construction and minimizing delay and waste. PCM relies on exploring and investigating project knowledge bases (KBs), formed by entity‐relation‐entity triples of constraints. However, most PCM KBs are incomplete and suffer from poor semantics, which hinders the PCM functions. Although many KB completion (KBC) methods exist in the field of artificial intelligence, they primarily focus on general knowledge and exclude the features of specific domains. Therefore, they cannot be directly applied to complete PCM KBs. To address the issue, this study proposes a novel deep learning model, referred to as the domain information enhanced graph neural network (D‐GNN). The features of the developed D‐GNN include (1) building a domain ontology to enrich semantics with rule reasoning, (2) applying the GNN to learn and encode embeddings of constraint entities and relations, and (3) employing a convolution neural network (CNN) for decoding and identifying missing triples. D‐GNN improves the existing KBC methods by integrating two types of domain information, namely, the ontological classes and working contexts into GNN and CNN, respectively. The experimental results verified that the D‐GNN reached an accuracy of 0.848–0.951, and the domain information integration increased the performance by up to 0.263. In practical testing, the D‐GNN significantly reduced the KBC time to 1/6–1/35 of the manual approach and reached higher accuracy. Therefore, the proposed D‐GNN can facilitate PCM by providing complete KBs and supporting downstream constraint monitoring and removal. [ABSTRACT FROM AUTHOR]

UHMWPE composites were prepared by hot pressing process with micro-crystal muscovite as reinforced particulates. The mechanical behaviors of composites to impact loading was evaluated by split Hopkinson bar. The results demonstrated that dynamic yield stress and failure stress of UHMWPE composites were gradually increased when the filling amount was less than 20%; when the filling content of muscovite was around 15%, the energy absorption efficiency of the composite reaches maximum value. It was also found that when strain rate within 3200/s, the dynamic yield stress, failure stress and energy absorption efficiency of UHMWPE composites increased with the increase of strain rate and display strain rate enhancement effect.

The absorption characteristic of lithium niobate crystals doped with chromium and copper (Cr and Cu) is investigated. We find that there are two at)parent absorption bands for LiNb[O.sub.3]:Cr:Cu crystal doped with 0.14 wt.%, [Cr.sub.2][O.sub.3] and 0.011 wt. % CuO; one is around 480 nm, and the other is around 660 nm. With a decrease in the doping composition of C r and an increase in the doping composition of Cu, no apparent absorption band in the shorter wavelength range exists. The higher the doping level of Cr, the larger the absorbance around 660 ran. Although a 633 nm red light is located in the absorption band around 660 nm, the absorption at 633 nm does not help the photorefractive process; i.e., unlike other doubly doped crystals, for example. LiNb[O.sub.3]:Fe:Mn crystal, a nonvolatile holographic recording can be realized by a 633 nm red light as the recording light and a 390 nm UV light as the sensitizing light. For LiNb[O.sub.3]:Cr:Cu crystals, by changing the recording light from a 633 nm red light to a 514 nm green light, sensitizing with a 390 nm UV light; and a 488 nm blue light, respectively, a nonvolatile holographic recording can be realized. Doping the appropriate Cr (for example, No, 2.795 x [10.sup.25] [m.sup.-3] and [N.sub.Cr]/[N.sub.Co] = 1) benefits the improvement of holographic recording properties. OCIS codes: 050.7330. 090.2900, 160.5320. 210.2860, 300.1030.