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Composite plates are susceptible to various damages in complex conditions and working environments, which may reduce the reliability of the structure and threaten equipment and personal safety. Thus, the implementation of a robust online Structural health monitoring (SHM) system for these composite structures becomes imperative. To enhance reliability and safety, we introduce a robust online SHM system anchored by our newly developed damage detection Bayesian neural network (DD-BNN). The main contribution of this study lies in the DD-BNN to perform precise and reliable damage detection and localization in composite plates using only one actuator-receiver pair without any signal/feature pre-processing and human intervention. The proposed DD-BNN model innovatively combines probabilistic modeling with deep learning to address uncertainty in Lamb wave-based damage detection and model performance for composite plates, featuring a specialized probabilistic layer trained through Bayesian inference to efficiently encapsulate and manage uncertainty in model weights and activation. Notably, our method significantly simplifies the SHM system design and manual operation requirements. In addition, this approach not only reduces overfitting but also enhances robustness to noise, as confirmed by experiments on perturbation analysis of Gaussian and Poisson noise. [ABSTRACT FROM AUTHOR]

Pa0665 in Pseudomonas aeruginosa shares homologous sequences with that of the essential A-type iron–sulfur (Fe-S) cluster insertion protein ErpA in Escherichia coli. However, its essentiality in P. aeruginosa and its complementation with E. coli erpA has not been experimentally examined. To fulfill this task, we constructed plasmid-based ts-mutant Δpa0665/pTS-pa0665 using a three-step protocol. The mutant displayed growth defects at 42 °C, which were complemented by expressing ec.erpA. Microscopic observations indicated a petite cell phenotype for Δpa0665/pTS-pa0665 at 42 °C, correlated with the downregulation of the oprG gene. RNA sequencing revealed significant transcriptional changes in genes associated with the oxidative phosphorylation (OXPHOS) system, aligning with reduced ATP levels in Δpa0665/pTS-pa0665 under 42 °C. Additionally, the ts-mutant showed heightened sensitivity to H2O2 at 42 °C. Overall, our study demonstrates the essential role of pa0665 for OXPHOS function and is complemented by ec.erpA. We propose that the plasmid-based ts-allele is useful for genetic analysis of essential genes of interest in P. aeruginosa. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment. [ABSTRACT FROM AUTHOR]

• Exploring cooling behaviors beyond air conditioning in residential demand response. • Testing the effect of cooling behaviors on thermal comfort during demand response. • Identifying inaccuracy of prevalent thermal comfort models for demand response. • Developing more precise thermal comfort models for residential demand response. During periods of electricity demand response (DR) in residential buildings, inhabitants frequently engage in supplementary cooling behaviors, extending beyond the utilization of air conditioning (AC). However, the prevalent thermal comfort models, namely the Predicted Mean Vote (PMV) and Temperature Range Method (TRM), which are typically employed during DR periods, are designed for stable environments and do not consider the impact of these additional cooling behaviors. This study aims to develop novel thermal comfort models that more accurately reflect these behaviors during residential DR periods. The initial stage of the study involved a questionnaire survey conducted among residents in Zhengzhou, China, to investigate the AC usage patterns and additional cooling behaviors. Subsequently, typical AC DR strategies, which include increasing indoor temperature set points and turning off AC, along with the three most comment additional cooling behaviors, namely utilizing an electric fan, reducing clothing and consuming cold beverages, were examined through multi-scenario experiments. The findings from these experiments facilitated the development of two novel models: the PMV-DR model, indicative of the thermal comfort level, and the TRM-DR model, representative of the thermal comfort acceptance rate (AR) during the DR period. When compared with the traditional PMV and TRM models, these newly proposed models demonstrated greater consistency with the experimental results. As a result, these models could serve as the basis for thermal comfort evaluations during residential DR periods in the Zhengzhou region. Furthermore, they could provide a methodological benchmark for other geographical locales. [ABSTRACT FROM AUTHOR]