Institute of Space Technology

Tropical Andean glaciers provide an important flux of freshwater to communities living both in high-altitude Cordillera and population centres downstream in countries such as Peru and Bolivia. Glacier recession threatens the sustainability of these water resources, and accurate modelling of future glacier behaviour is required to manage water stress in the region. These models must capture all processes contributing significantly to overall glacier mass budgets. Here we examine supraglacial pond and ice cliff development on three clean-ice glaciers in the Cordillera Vilcanota, Peru and their overall contribution to glacier mass balance. Whilst such features are common and well-studied on debris-covered glaciers, their development on debris-free glaciers has not been examined in detail. We use high-resolution contemporary and historical satellite imagery and repeat drone surveys to examine surface structure and geometry change over three glaciers during 1977–2024. We show how cliff and pond formation is driven by aspect-dependent surface melt of crevasse walls. These features act as ice loss hotspots, which enhance glacier net mass loss by ∼10% despite accounting for

In this article coset diagrams of the action of PSL(2, Z) on a PL(F_p) are obtained, through parametrization, which yields one of the eight finite generalized triangle groups which are homomorphic images or quotients of PSL(2, Z). Other than this we analyzed the coset diagrams for the parameter for three finite generalized triangle groups. One of the most dependable methods for achieving data security has been the block cipher. S-Boxes constructed using algebraic structure have gained popularity recently because of their advantageous cryptographic properties and high non-linearity have been found in these structures, which attract researchers. With the help of these parametrized actions, a novel algebraic method to create 2^8 S-Boxe was established. The S-Box provides strong cryptographic qualities of nonlinearity 112, differential uniformity 6, linear approximation probability of 0.0576 and differential attack probability of 0.0039. To assess the practical applicability of our S-box, we integrate it into an image encryption scheme and present experimental results to showcase its efficacy in real-world scenarios. When used with an image encryption framework, the following results were obtained: NPCR = 0.9959, UACI = 0.3348, and approx. Entropy 7.98. Therefore, GTG based parametrisation has been shown to be an effective and secure alternative to traditional algebraic construction method for S-Boxes.

This paper presents Q-FOX, a novel reinforcement learning (RL) framework that integrates the FOX optimization algorithm into Q-learning to enable adaptive hyperparameter tuning. By dynamically adjusting critical parameters such as the learning rate and discount factor, Q-FOX addresses the limitations of manually tuned Q-learning, which often suffers from suboptimal convergence and unstable policies. The FOX algorithm, inspired by red fox hunting behavior, balances exploration and exploitation in the hyperparameter space. Q-FOX is evaluated on both discrete (GridWorld, Cliff Walking) and continuous (Mountain Car) environments using tabular Q-learning with state discretization. Results demonstrate significant improvements in average reward, success rate, and convergence speed compared to conventional Q-learning, validating the effectiveness of FOX-based tuning. A comparative analysis is conducted to highlight Q-FOX’s robustness under constrained training episodes. The proposed framework shows promise for broader application in robotics, autonomous navigation, and adaptive control systems, where dynamic learning strategies are essential.