Building a Bridge between the Two Schools: Realizing a Practical Path to Include Literacy-based Skills within the STEM Curricula

Developing students as well-rounded professionals is increasingly important for our modern society. Although there is a great consensus that technical and professional ("soft") skills should be developed and intertwined in the core of computer science subjects, there are still few examples of alike teaching methodologies at technical schools. This contribution investigates the integration of technical and professional skills while teaching specialized curricula in computer science. We propose a broadly applicable, step-by-step methodology that connects core technical concepts (e.g., information entropy, network security) with fine arts practices such as music, video production, gaming, and performing arts (e.g., Oxford-style debates). The methodology was applied in several computer science courses at technical universities, where quantitative and qualitative assessments, including student questionnaires and exam scores, showed improved learning outcomes and increased student engagement compared to traditional methods. The results indicate that this art-based integration can effectively bridge the historical divide between the two schools of thought, offering a practical direction for educators. Within this context, we also identify open issues that will guide future research on topics such as instructor engagement, female motivation in technical subjects, and scalability of these approaches.

Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things

Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming to research the boundaries of the clustering capabilities, this survey emphasizes data-driven communication strategies in molecular communication (MC) channels as a means of linking nanosensors. Relying on the flexibility and robustness of machine learning (ML) methods to tackle the dynamic nature of MC channels, the MC research community frequently refers to neural network (NN) architectures. This interdisciplinary research field encompasses various aspects, including the use of NNs to facilitate communication in MC environments, their implementation at the nanoscale, explainable approaches for NNs, and dataset generation for training. Within this survey, we provide a comprehensive analysis of fundamental perspectives on recent trends in NN architectures for MC, the feasibility of their implementation at the nanoscale, applied explainable artificial intelligence (XAI) techniques, and the accessibility of datasets along with best practices for their generation. Additionally, we offer open-source code repositories that illustrate NN-based methods to support reproducible research for key MC scenarios. Finally, we identify emerging research challenges, such as robust NN architectures, biologically integrated NN modules, and scalable training strategies.

Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization

Nanodevices with Terahertz (THz)-based wireless communication capabilities are providing a primer for flow-guided localization within the human bloodstreams. Such localization is allowing for assigning the locations of sensed events with the events themselves, providing benefits in precision medicine along the lines of early and precise diagnostics, and reduced costs and invasiveness. Flow-guided localization is still in a rudimentary phase, with only a handful of works targeting the problem. Nonetheless, the performance assessments of the proposed solutions are already carried out in a non-standardized way, usually along a single performance metric, and ignoring various aspects that are relevant at such a scale (e.g., nanodevices' limited energy) and for such a challenging environment (e.g., extreme attenuation of in-body THz propagation). As such, these assessments feature low levels of realism and cannot be compared in an objective way. Toward addressing this issue, we account for the environmental and scale-related peculiarities of the scenario and assess the performance of two state-of-the-art flow-guided localization approaches along a set of heterogeneous performance metrics such as the accuracy and reliability of localization.