The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems.

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high.

Digitalization and population aging are profoundly reshaping contemporary Chinese society, with 14.3% of internet users now aged 60 and above. This study examines elderly Chinese individuals' adaptation to the digital environment through a survey of 205 participants aged 60 and above. Findings reveal widespread basic digital usage (88.78% use smartphones) but limited advanced engagement. While intergenerational support is the primary learning channel (87.32%), significant barriers exist, including communication difficulties and psychological concerns. Digital services' age-friendliness (satisfaction 2.92/5) requires improvement, particularly in accessibility features. The study recommends enhancing age-friendly design and optimizing support systems to promote digital inclusion among the elderly.

In conventional passive seismic exploration, it is often necessary to make a long-period seismic record. On the one hand, the passive seismic records with long period allowed us to screen several good passive seismic records with long period for seismic interferometry reconstruction and perform piecewise stacking on them. On the other hand, a sufficiently long recording time can help us avoid noise interference generated by nonpassive sources during the recording process, such as animal activities, construction operations, industrial electrical interference, etc. Compared with the passive seismic records with short period, the passive seismic records with long period can obtain higher signal-to-noise ratio after seismic interferometry reconstruction. However, they also cause huge consumptions of manpower, material resources, and time. Based on this, this paper proposes a seismic interferometry reconstruction method using passive signals of short-period recordings. Based on deep learning technology, the effective information is extracted and enhanced, the strong coherent noise after reconstruction is suppressed and weakened, the SNR of reconstructed recording is improved, and the effective information is mined. It can effectively reduce the time of passive seismic recording required for acquisition and improve acquisition efficiency. In addition, it also has a certain monitoring effect on real-time changes in underground structures.