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High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

This paper aims to examine the social engagement of individuals who have recently migrated to cities in India. The objective is to assess the capacity of these migrants to foster social connections in their newfound urban settings, aiming to advocate for urban governance reforms that facilitate inclusive planning for the holistic well-being of all residents, including migrants.

Using quantitative data from the first phase of the longitudinal aging study in India (LASI-2020), this study introduces the term “social engagement” to characterize the interactions of migrants. Incorporating 18 relevant variables, the research uses the Chi-square test to explore correlations between these factors and the social engagement of recent migrants in cities. In addition, multivariate logistic regression is used to identify traits that empower migrants to achieve significant levels of social engagement.

This study reveals that a substantial proportion of recent migrants (over 70%) experience social isolation in their new urban environments. Factors such as increasing age, female gender and lower educational attainment significantly contribute to this isolation. Moreover, wealth, religious affiliations and previous urban experiences play pivotal roles in shaping social engagement among urban migrants. Interestingly, age itself does not directly impact migrants’ social engagement; instead, the intersection of factors such as education, wealth and prior urban experiences assumes a critical role.

Recognizing that social engagement is an integral part of everyday life in contemporary urban societies, it is imperative for urban governance in India to establish structures and systems that facilitate social engagement for the elderly, including migrants. Urban administrators and planners must integrate these dimensions into planning and administrative processes to foster inclusivity for all elderly populations, including migrants, within urban areas.

Addressing a noticeable gap in existing literature, this paper initiates a discourse on the social engagement of recent migrants residing in Indian cities, with specific focus on the intricate interplay of aging, gender, migration status, wealth and education.