Chaos-driven design of highly nonlinear S-boxes for efficient image encryption

A new study has proposed a novel method for constructing highly nonlinear S-boxes (substitution boxes), crucial elements in symmetric encryption algorithms, using chaotic systems. This approach aims to enhance the security and efficiency of image encryption, a field with increasing demands due to the volume of visual data transmitted and stored digitally. The research focuses on generating S-boxes with optimal cryptographic properties, such as high nonlinearity and low correlation, which are fundamental to resisting cryptanalytic attacks.

Programmable Stiffness Module Optimizes Robotic Aquatic Locomotion

Researchers have developed an online programmable stiffness module that significantly enhances the efficiency of aquatic locomotion in robots. This breakthrough allows robots to dynamically adjust the stiffness of their fins or propulsors to adapt to different aquatic environments and tasks, optimizing their propulsion performance. The ability to modify stiffness in real-time is crucial for mimicking the adaptability observed in natural marine organisms, which adjust the flexibility of their bodies and appendages to swim efficiently under various conditions. The system is based on a mechanism that can alter the stiffness of the robot's propulsive components. This is achieved through the integration of materials and actuators that allow precise control over the deformation and resistance of the fins. Through experiments and simulations, it was demonstrated that stiffness modulation can reduce energy consumption and increase the robot's speed or maneuverability. This approach contrasts with traditional aquatic robotic designs, which often employ fixed-stiffness structures, limiting their versatility and efficiency in variable conditions. The research findings indicate that online stiffness optimization can lead to substantial improvements in propulsive efficiency, with performance approaching that of biological swimmers. This work has significant implications for the development of future generations of autonomous underwater robots, which could be used in ocean exploration, environmental monitoring, or even search and rescue operations. The ability to adapt to changing water conditions, such as currents or turbulence, without sacrificing efficiency is a fundamental step towards more robust and intelligent aquatic robots.

Electro-thermal benchmarking of low-order lithium-ion battery models

A recent study has conducted a comprehensive evaluation of low-order equivalent circuit models for lithium-ion batteries, considering both their electrical and thermal behavior. The research focused on how these models represent the internal dynamics of batteries under different loading conditions, including constant current and dynamic load profiles. This type of analysis is crucial for improving the accuracy of battery management systems (BMS), which are fundamental for optimizing the performance, safety, and lifespan of batteries in applications such as electric vehicles and renewable energy storage. The work addresses the need for battery models that are accurate enough to capture complex phenomena like heating and degradation, yet also computationally efficient for real-time implementation in a BMS. Equivalent circuit models (ECMs) are a popular choice due to their balance between complexity and precision. However, their performance can vary significantly depending on how they are parameterized and the operating conditions. The novelty of this study lies in its focus on joint electro-thermal evaluation, which is essential given that temperature has a direct impact on the internal resistance and capacity of batteries. The researchers evaluated different ECM configurations, comparing their ability to predict the terminal voltage and internal temperature of battery cells. The results provide guidance on the selection and parameterization of ECMs for specific applications, highlighting the strengths and limitations of each model under realistic load scenarios. This detailed characterization is a step forward in developing more robust and predictive BMS algorithms, which will in turn contribute to greater efficiency and reliability of lithium-ion battery systems.

NASA Tests Wing Concept for Attenuated Natural Laminar Flow

Engineers at NASA's Armstrong Flight Research Center in Edwards, California, have conducted flight tests of a new wing concept designed to maximize natural laminar flow. This initiative, named Crossflow Attenuated Natural Laminar Flow (CATNLF), aims to improve aircraft aerodynamic efficiency by manipulating airflow over the wing surface. The tests are part of NASA's ongoing effort to advance aviation and push the limits of aerodynamics, a tradition that dates back nearly 80 years at this center. The primary goal of this technology is to maintain smooth and orderly laminar airflow over as much of the wing surface as possible. Laminar flow drastically reduces aerodynamic drag compared to turbulent flow, leading to greater fuel efficiency and reduced environmental impact. The CATNLF concept focuses on attenuating crossflow instabilities, which are a key factor in the transition from laminar to turbulent flow, especially on swept wings. Although the original text does not detail the specific results of these recent tests, the importance of this type of research lies in its potential to revolutionize the design of commercial and military aircraft. Successful implementation of natural laminar flow technologies could lead to a new generation of quieter, faster, and, crucially, much more fuel-efficient airplanes. This advancement aligns with the global aviation industry's sustainability and emissions reduction goals.