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Background and Objectives: Addictive behaviors are serious factors for mental health and usually increase during public crises. We identified the vulnerable characteristics for bad prognosis of addictive internet use across different periods of the coronavirus disease 2019 (COVID‐19) pandemic. Methods: Self‐reported questionnaires were delivered in three waves through jdh.com during the outbreak (n = 17,960), remission (n = 15,666), and dynamic zero (n = 12,158) periods of COVID‐19 pandemic in China. Internet addiction degree was assessed using the Internet Addiction Test. The different progression groups were divided using a latent class growth model among 1679 longitudinal participants. Risk factors for bad progression were identified by two‐step logistic regression. Results: A total of 40.16% of participants reported an increase in the addictive degree of internet use compared with prepandemic. Across different COVID‐19 periods, the overall trend of addictive internet use was downward among general Chinese study participants (Mslope = −1.56). Childhood traumatic experiences, deterioration of physical health, depression, and anxiety during remission and dynamic periods were the main risk factors for the bad progression of pandemic‐induced addictive internet use. Discussion and Conclusions: Addictive internet use was remitted following relaxed control policies during the COVID‐19 pandemic. Negative childhood experiences and bad mental status during the recovery period were harmful to coping with pandemic‐related addictive internet use. Scientific Significance: Our findings profiled the general trend of addictive internet use and the vulnerable characteristics of its bad progression across different periods of the first wave of COVID‐19 pandemic in China. Our findings provide valuable insights for preventing the long‐term adverse effects of negative public events on Internet addiction. [ABSTRACT FROM AUTHOR]

Fiber Bragg grating (FBG) sensors have attracted a considerable amount of interest for their superior characteristics. However, the FBG sensors made on bare fibers are easily damaged. For their safe use in engineering, the glass core of optical fibers is coated with softer low modulus protective coatings. A portion of the host material strain is absorbed by the protective coatings when the strain transfers from the host material to the fiber core, and hence only a segment of structural strain is sensed. By introducing the shear modulus of the host material, a novel analytical model is developed for evaluating the sensing strain of the embedded FBG sensors in composite structures based on the strain in a host material. The average strain transfer ratio is deduced to describe the percentage transferred to the optical fiber core from the host material. It is concluded that the shear modulus of the host material influences strain transmission, especially when it is much lower than the modulus of the fiber core. Then, the strain transfer ratio of an optical fiber sensor embedded in a multilayered structure is developed in a similar way. The factors that affect the efficiency of strain transfer on the optical fiber sensor are deduced and discussed in detail based on the theoretical analysis. Finally, the theoretical results are verified through laboratory experimentation with the FBG sensors. DOI: 10.1061/(ASCE)0733-9399(2009)135:12(1343) CE Database subject headings: Strain distribution; Probe instruments; Experimentation; Young's modulus; Parameters.

Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed. [ABSTRACT FROM AUTHOR]