Deep Q-Learning in Robotics: Improvement of Accuracy and Repeatability

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Fast detection of micro-objects using scanning electrochemical microscopy based on visual recognition and machine learning

Scanning electrochemical microscopy (SECM) is a scanning probe microscope with an ultramicroelectrode (UME) as a probe. The technique is advantageous in the characterization of the electrochemical properties of surfaces. However, the limitations, such as slow imaging and many functions depending on the user, only allow us to use some of the possibilities. Therefore, we applied visual recognition and machine learning to detect micro-objects from the image and determine their electrochemical activity. The reconstruction of the image from several approach curves allows it to scan faster and detect active areas of the sample. Therefore, the scanning time and presence of the user is diminished. An automated scanning electrochemical microscope with visual recognition has been developed using commercially available modules, relatively low-cost components, design, software solutions proven in other fields, and an original control and data fusion algorithm.

Robotics and Aseptic Processing in View of Regulatory Requirements

Several nanomedicine based medicinal products recently reached the market thanks to the drive of the COVID-19 pandemic. These products are characterized by criticality in scalability and reproducibility of the batches, and the manufacturing processes are now being pushed towards continuous production to face these challenges. Although the pharmaceutical industry, because of its deep regulation, is characterized by slow adoption of new technologies, recently, the European Medicines Agency (EMA) took the lead in pushing for process improvements using technologies already established in other manufacturing sectors. Foremost among these technologies, robotics is a technological driver, and its implementation in the pharma field should cause a big change, probably within the next 5 years. This paper aims at describing the regulation changes mainly in aseptic manufacturing and the use of robotics in the pharmaceutical environment to fulfill GMP (good manufacturing practice). Special attention is therefore paid at first to the regulatory aspect, explaining the reasons behind the current changes, and then to the use of robotics that will characterize the future of manufacturing especially in aseptic environments, moving from a clear overview of robotics to the use of automated systems to design more efficient processes, with reduced risk of contamination. This review should clarify the regulation and technological scenario and provide pharmaceutical technologists with basic knowledge in robotics and automation, as well as engineers with regulatory knowledge to define a common background and language, and enable the cultural shift of the pharmaceutical industry.

Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator

This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind of experience. The model was tested under several scenarios and control goals (i.e., position control, path-following and formation control) with control strategies such as reinforcement learning, and Villela and IPC algorithms. The results of these approaches were compared using performance indexes to analyze the performance of the model under different scenarios. The model and examples with different control scenarios are available online.