The role of cognitive, social and biological factors in the etiology of chronic periodontitis has been reported. The aim of this study was to evaluate the salivary cortisol level and interleukin-1 B level in patients of Chronic periodontitis in smokers and stress and nonsmokers without stress. The design of study randomized, prospective, double-blinded, and prospective study. The total sample size was comprised of 600 subjects between the ages of 20 and 50 years. The sample size was divided into 300 males and 300 females. Out of 600 subjects, 200 subjects comprised of subjects with chronic periodontitis with positive depression level with a history of smoking (Group I), 200 subjects comprised of subjects with chronic periodontitis without depression and without smoking (Group II), and 200 subjects who were taken as the control group comprised of healthy subjects without chronic periodontitis, without depression level, and no smoking history (Group III). Salivary cortisol levels were determined by enzyme-linked immunosorbent assay (ELISA). The result showed that there was a positive correlation between morning and evening salivary cortisol level in all the groups with correlation coefficient. There was significant higher value of salivary cortisol in Group I patients when compared with Group II and Group III. However, when the comparison of salivary cortisol levels was done between the Group II and Control group, the result showed nonsignificant P value. It is suggested that stress is positively correlated with the salivary cortisol levels in smokers and nonsmokers.

Traditional Underwater Acoustic Communication (UAC) typically assumes static or slowly varying channels over short observation periods and models multipath amplitude fluctuations with single-state statistical distributions. However, field measurements in shallow-water high-speed mobile scenarios reveal that the combined effects of rapid platform motion and dynamic environments induce multi-scale time-varying amplitude characteristics. These include distance-dependent attenuation, fluctuations in average energy, and rapid random variations. This observation directly challenges traditional single-state models and wide-sense stationary assumptions. To address this, we propose a multi-scale time-varying multipath amplitude model. Using singular spectrum analysis, we decompose amplitude sequences into hierarchical components: large-scale components modeled via acoustic propagation physics; medium-scale components characterized by Hidden Markov Models; and small-scale components described by zero-mean Gaussian distributions. Building on this model, we further develop a time-varying impulse response simulation framework validated with experimental data. The results demonstrate superior performance over conventional single-state distribution and autoregressive models in statistical distribution matching, temporal dynamics representation, and communication performance testing. The model effectively characterizes non-stationary time-varying channels, supporting high-precision modeling and simulation for mobile UAC systems.

Underwater acoustical communication (UAC) systems typically presume limited platform mobility, with insufficient attention to challenging high-speed mobile (HSM) scenarios. However, its importance is expected to increase with the rapid development of highly maneuverable autonomousunderwater vehicles. Knowledge of HSM-UAC channels is a prerequisite for ensuring reliable communication links. This study conducts two shallow-water channel measurements with speeds up to 15 knots, involving both variable-speed and constant-speed horizontal motion. Measured time-varying impulse responses clearly reveal time-varying, multiscale, multilag (TV-MSML) phenomena. Based on these findings, we propose a TV-MSML channel model and use geometric acoustics theory and BELLHOP simulation to explain the observed phenomena and validate the model. We further analyze the time-varying behavior of multipath parameters, such as path delay, residual Doppler factor, and amplitude, and evaluate the applicability of conventional statistical distribution models in HSM scenarios. The nonstationary behavior of HSM-UAC channels is examined through frequency-selective fading, multipath birth–death processes, and spatiotemporal correlation. We designed a local stationarity distance metric using the temporal power correlation coefficient and compared various property parameters with static environments. This work alleviates the lack of measured data and characteristic studies on HSM-UAC channels, highlights the TV-MSML phenomena and high dynamism in HSM scenarios, and offers valuable insights for designing and optimizing mobile UAC systems.

The reliable acoustic path (RAP) is one of the crucial channels for deep-sea sound propagation, which is affected weakly by the interface and has lower transmission loss, enabling long-distance communication. However, RAP-based deep-sea acoustic communication may face channel model mismatch issues. In order to analyze the dynamic characteristics of spatial-temporalvariability channels, deep-sea mobile underwater acoustic channel measurement experiments were conducted. This work proposes a deep learning method based on multi-dimensional properties to classify deep-sea channels. Specifically, the sound ray convergence zone leads to a complex multipath structure and severe delay spread in the RAP channel. The fuzzy c-means (FCM) algorithm is used for multipath clustering to extract accurate channel features, and then the Markov chain (MC) is introduced to track the evolution characteristics of multipath clusters. Finally, the coupling features of channel time-variant impulse response (TVIR) and multi-dimensionalstatistical properties are used as the input of convolutional neural networks (CNNs) to obtain the quantitative evaluation index as the channel classification to build a channel feature dataset for underwater mobile platforms. This dataset can effectively assist in identifying deep-sea mobile channels and promote the development of adaptive underwater acoustic communication systems on mobile platforms.