The deployment of access points (APs) imposes a great impact on the performance of wireless local area networks (WLANs). Although there have been plenty of works on the optimal AP deployment (OAD), however, the issue remains open that how to deploy APs energy-savingly in 3-D environments while considering some practical demands. Therefore, this article investigates the OAD with the objective of minimizing the total AP transmission power while guaranteeing the effective coverage by jointly optimizing the transmission power and location of each AP. Different from existing works, our article fully considers not only real 3-D environments with various obstacles but also the differentiated coverage demand and the restrictions on AP locations. To solve this NP-hard optimization problem, this article first proposes a novel hybrid swarm intelligence optimization algorithm, named PSO-Lévy-DFOA. The computational results on 16 benchmark functions and comprehensive comparisons with other algorithms demonstrate the superior global convergence performance of PSO-Lévy-DFOA. The proposed PSO-Lévy-DFOA-based OAD method is then applied to WLAN planning in a two-story building. It has been shown that the total AP transmission power can be considerably reduced compared to the current deployment while still fulfilling the effective coverage.

To develop high-performance microfine cementitious grout (HPMCG), the matrix [microfine Portland cement clinker (MPCC) + flue gas desulfurizing gypsum (MFGDG) + calcium carbonate (MCC)], microfine fly ash (MFA), silica fume (SF), and blast-furnace slag (MBFS) were selected. The viscoelasticity, fresh-state properties, mechanical performance, antipermeability, hydration mineral, and microstructure were investigated systematically. The 0%–3.0% naphthalene-based superplasticizer (N) and 1.0%–2.6% composite activator (CA) were applied, and the ratio of the water to binder (w/b) was selected as 0.8–3.0 by weight. The optimum component of HPMCG was acquired as the amount of MFA was 40% relative to that of the matrix, and the additional contents of SF and MBFS were 10% and 30%. The amounts of N and CA were suggested as 1.5%–2.0% and 2.4%, respectively. After optimization, the maximal flexural strength (FS) and unconfined compressive strength (UCS) of the HPMCG (91-day) can achieve 7.29 and 31.65 MPa; nearly all of the calcium hydroxide reacted, and more gels were generated. More generated gels interconnected with each other to form dense network structures, there were few microvoids and pores, and the entirety of the microstructure was enhanced obviously. The optimized HPMCG surpassed the expectation. The optimized HPMCG has superiorities, such as excellent viscoelasticity, good fluidity, high stability or stone rate, high mechanical strength, good antipermeability, an advantageous mineral component, and a microstructure. In civil engineering, they can satisfy high standard demands of construction and repair grouting practices.

Power safety named entity recognition (NER) is essential for determining the cause of faults, assessing potential risks, and planning maintenance schedules, contributing to the comprehension and analysis of power safety documentation content and structure. Such analysis is crucial for the development of a knowledge graph within the power safety domain and the augmentation of the associated dataset. This paper introduces a power safety NER model using efficiently learning an encoder that classifies token replacements accurately (ELECTRA) model. This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). This model also integrates bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) to bolster performance in NER tasks. Experimental results show that the improved ELECTRA model achieved an F1 value of 93% on the constructed power safety NER dataset. It outperforms the BERT-BiLSTM-CRF model, achieving a 3.3% performance improvement.