This text explores how electronic musical instruments and electronic music ensembles can relate to composition and music notation by discussing the instruments in terms of existing practice in traditional instrumentation and in relation to symbolic electroacoustic music analysis. Starting from orchestration theory, the text considers how electronic musical instruments behave and are used, both with support from the author’s own practice and from a case study with students within the framework of a live-electronic ensemble course. The case study reflected the participants’ practice as creative composers/musicians, and how their exploratory and experimental approaches to their instruments proved important, creating challenges for notation. Traditionally, music notation relies on continuous changes of simple parameters, while performances with complex electronic instruments may have just as important information to document regarding their initial connectivity and parameter settings.

To address the limitations of existing external pipeline inspection robots, including a narrow range of adaptable pipe diameters and difficulty traversing obstacles like cross-pipelines, a novel wheel-clamping robot capable of circumferential rotation was designed. The composite drive mechanism of the robot adopts a dual-slider multi-link mechanism to realize the rapid switching of the two motion modes of the robot axis: forward and circumferential rotation. Following clarification of the robotic mechanism and component dimensions, a geometric model of key points was established, determining an adaptable pipeline diameter range of 74–203 mm. The force analysis was carried out to analyze the working state of the robot axis forward and circumferential rotation, and the minimum driving torque required to complete the above two motions is 0.84 and 1.23 N·m, respectively. Finally, the robot prototype was made, and the experiments of the prototype running on the pipeline were carried out. The experimental results show that the average speed of the robot is 0.195 m/s when it is moving along the axis on the pipeline, and it stably navigates obstacles through circumferential rotation, smoothly crossing T-shaped pipelines of different diameters, which is adaptable to the complex pipeline working conditions.

Étude n°1 is a solo for feedback and effects pedals by David Caulet. Originally designed to enrich the electric guitar’s timbre, effects pedals have been widely repurposed by experimental and improvising musicians. Although their use is now common, notation practices associated with these devices remain underdeveloped. This work explores the development of a graphic system dedicated to representing instrumental gestures and opens perspectives for a notation framework adapted to contemporary musical practices incorporating electronic technologies.

Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perform these tasks, limiting their broader applicability. Inspired by the transformative success of Large Language Models (LLMs), Time-Series Foundation Models (TSFMs), trained on diverse datasets, have the potential to change this. Were TSFMs to achieve a level of generalizability across tasks and contexts akin to LLMs, they could fundamentally address the scalability challenges pervasive in BEM. To understand where they stand today, we evaluate TSFMs across four dimensions: (1) generalizability in zero-shot univariate forecasting, (2) forecasting with covariates for thermal behavior modeling, (3) zero-shot representation learning for classification tasks, and (4) robustness to performance metrics and varying operational conditions. Our results reveal that TSFMs exhibit limited generalizability, performing only marginally better than statistical models on unseen datasets and modalities for univariate forecasting. Similarly, inclusion of covariates in TSFMs does not yield performance improvements, and their performance remains inferior to conventional models that utilize covariates. While TSFMs generate effective zero-shot representations for downstream classification tasks, they may remain inferior to statistical models in forecasting when statistical models perform test-time fitting. Moreover, TSFMs’ forecasting performance is sensitive to evaluation metrics, and they struggle in more complex building environments compared to statistical models. These findings underscore the need for targeted advancements in TSFM design, particularly their handling of covariates and incorporating context and temporal dynamics into prediction mechanisms, to develop more adaptable and scalable solutions for BEM.