Background: Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. Results: We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model—the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. Conclusions: The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.

The V-Cockpit platform aims to transform the design, testing, and validation of car infotainment systems from the physical realm to virtual reality. It uniquely integrates various aspects of the creative phases—from conceptualization to evaluation—streamlining the process and reducing time and costs compared to traditional methods that focus on individual activities and rely heavily on physical prototyping. This technical note provides a comprehensive overview of the main platform’s aspects, highlighting the integration of hardware and behavioral analysis algorithms to improve user experience and detect potential design flaws early on. The V-Cockpit platform, composed of six key components, leverages virtual reality and digital twins, promising significant cost savings and enhanced design efficiency. This work details the system architecture, implementation, and first benefits of this innovative approach through the analysis of three use cases.

Teaching concurrent and parallel programming presents substantial challenges, primarily due to its conceptual complexity and the required paradigm shift from sequential programming. Railway Quest is a gamified digital platform designed to facilitate students' learning of multithreaded programming concepts through an interactive approach. The platform translates abstract programming challenges into train locomotion scenarios, wherein trains represent threads that must move along tracks while avoiding crashes, resource access conflicts, and indefinite delays. By leveraging game-based learning and gamification principles, Railway Quest enhances students’ motivation and engagement through a visual and interactive representation of multithreaded executions and concurrency issues. The platform allows students to develop and execute code-based solutions, immediately observing their impact in real-time simulations presented through a low-poly isometric interface. Such approach significantly helps in conceptualizing threading constructs and synchronization mechanisms. Railway Quest supports both integrated and remote learning environments, making it an effective educational tool, especially for distance learning. The platform has been deployed in a university course in Spring 2025, with the aim to bridge the gap between theoretical knowledge and practical problem-solving in concurrent and parallel programming. Preliminary data from 45 students suggests a positive impact on students' understanding of key concurrency problems such as race conditions, deadlocks, and starvation, as well as their proficiency in utilizing synchronization tools like mutexes and conditional variables.