A series of previously conducted experiments pertaining to various chemicals and drugs uncover a natural linkage between the molecular structures and the bio-medical and pharmacological characteristics. The forgotten topological index computed for the molecular structures of various chemical compounds and drugs has proven significant in medical and pharmaceutical fields by predicting biological features of new chemical compounds and drugs. A topological index can be considered as the transformation of chemical structure into a real number. Dendrimers are highly-branched star-shaped macromolecules with nanometer-scale dimensions. Dendrimers are defined by three components: a central core, an interior dendritic structure (the branches), and an exterior surface with functional surface groups. In this paper, we determine forgotten topological indices of poly(propyl) ether imine, porphyrin, and zinc–porphyrin dendrimers.

In recent years, chaos has been extensively used in cryptographic systems. In this regard, one dimensional chaotic maps gained increased attention because of their intrinsic simplicity and ease in application. Many image encryption algorithms that are based on chaotic substitution boxes (S-boxes) have been studied in the last few years but some of them appear to be vulnerable to robustness. We, in this paper, propose an efficient scheme for image encryption that utilizes a composition of chaotic substitution based on tent map with the scrambling effect of the Arnold transform. The proposed construction algorithm for substitution box is, on one hand, straightforward and saves omputational labour, while on the other, it provides highly efficient performance outcomes. The understudy scheme uses an S-box, that is based on 1-D chaotic tent map. We partially encrypt the image using this S-box and then apply certain number of iterations of the Arnold transform to attain the fully encrypted image. For decryption we apply the reverse process. The strength of the proposed method is determined through the most significant techniques used for the statistical analysis and it is proved that the anticipated algorithm shows coherent results.

While AI-powered mental health tools have gained momentum as accessible and user-centered alternatives to traditional services, the psychological processes through which they exert influence remain insufficiently theorized. This gap is especially relevant in contexts such as China, where cultural norms surrounding mental health stigma and reluctance toward emotional disclosure intersect with shifting generational attitudes toward autonomy and self-management. This study aimed to examine the relationship between AI-based mental health tools and psychological well-being among Chinese university students, specifically focusing on the mediating roles of emotional self-efficacy and perceived autonomy in mental health management within a parallel mediation framework. A cross-sectional survey was administered to 3,859 university students across Jiangxi Province, China. Participants completed validated measures assessing use of AI-based mental health tools, emotional self-efficacy, perceived autonomy, and psychological well-being. Structural equation modeling was employed to test a parallel mediation model, controlling for age, gender, academic level, and prior use of mental health apps. A post hoc sensitivity analysis was also conducted to examine the robustness of mediation effects to potential unmeasured confounding. The results showed that using AI-based mental health tools was positively associated with psychological well-being (β = 0.229, p < 0.001). Both emotional self-efficacy (β = 0.121, 95% CI: 0.101,0.159) and perceived autonomy in mental health management (β = 0.132, 95% CI: 0.112,0.176) significantly and partially mediated this association, indicating that the psychological benefits of tool use were transmitted through enhanced emotional regulation and volitional engagement. Bootstrapped indirect effects were statistically significant, with confidence intervals that did not include zero. The overall model explained 38% of the variance in psychological well-being. Sensitivity analysis further demonstrated that the mediation effects were robust to moderate levels of unmeasured confounding. The findings suggest that AI-based mental health tools may enhance student well-being by facilitating symptom management and strengthening emotional self-regulation and volitional engagement in care. These results underscore the importance of autonomy-supportive and empowerment-focused design in digital mental health interventions. Future longitudinal and cross-cultural research is recommended to validate these pathways and inform scalable, context-sensitive applications.

The article focuses on Rayleigh wave propagation in a homogeneous isotropic semi-conductor thermoelastic medium rotating with fixed angular frequency with temperature-dependent properties. The elastic constants depend upon the temperature function. The effects of temperature dependency parameter, time derivative, and fractional order are illustrated. By the theory of thermo-elasticity, waves result in the generation of thermal signals that propagate through the medium. A heat conduction model of three-phase lag (3PL) along with fractional order time derivative is used to analyze the thermal signals. The secular equations of Rayleigh waves are derived mathematically at the stress-free, carrier density and thermally insulated boundaries. Some specific properties like velocity, attenuation coefficient, specific heat loss and penetration depth for Rayleigh waves have been evaluated and presented graphically. The secular equations are computed numerically and depicted graphically using Matlab.