In this work, by considering coherent systems comprising independent components with discrete lifetimes, we introduce the notion of discrete-time signature and then discuss some of its properties. With the use of the introduced signature, a stochastic ordering result is also established. We then introduce transformation formulas for the discrete-time signature to facilitate the comparison of systems of different sizes. Some examples are also presented to illustrate all the results developed here.

Recent developments in spatial audio and immersive technologies have expanded creative possibilities for composers and sound artists. This article presents a novel prototype of a spherical microphone with an ellipsoid casing and ten motorised condenser capsules, each capable of real-time adjustment of orientation and polar pattern. Unlike fixed-pattern or conventional ambisonic arrays, this design enables dynamic control over spatial coverage and directivity, offering new opportunities for multichannel recording, live performance and interactive sound art. While software-based spatialisation offers some flexibility, physical reconfiguration of capsules provides superior responsiveness and avoids latency, phase artefacts or resolution loss. This is especially critical in performance contexts where immediate acoustic adaptation is required. The system allows direct manipulation of capsule parameters during rehearsal or installation, effectively transforming the microphone into a performative instrument. The article compares the prototype with existing commercial ambisonic microphones, highlighting its distinctive advantages in workflow and compositional strategy. Use-case scenarios demonstrate how real-time control over spatial parameters enhances both technical precision and artistic expressiveness. The article concludes with a discussion of future directions, including collaborative testing with practitioners and integration into creative environments where spatial transparency, fidelity and interactivity are essential.

This study investigates experimental luthiery and sound art practices in Latin America through the lenses of postcolonial theory and acoustemology. Within this framework, the musical instrument is conceptualised as a sound-producing object and an active site of cultural representation, historical memory and resistance. These practices, diverging from conventional luthiery traditions, embrace collective, conceptual and material-based modes of production, establishing alternative knowledge systems through sound. Drawing on the works of artists, such as Walter Smetak, Marco Antônio Guimarães, Joaquín Orellana, Wilson Sukorski and Tania Candiani, this study explores how sound mediates relationships with space, the body, memory and technology. Conceptual instrument design is thereby positioned as an aesthetic-political tool developed in parallel with transformations in auditory regimes and responding to epistemic inequalities. This study also focuses on modes of production shaped by technological exclusion, gender and postcolonial identity formation. Experimental luthiery in Latin America is presented as a field of artistic expression and a multilayered epistemic site for the generation of alternative knowledge systems, political subjectivities and spatial justice strategies.

This paper presents a reliability-constrained Bayesian optimization framework for structural design under uncertainty, addressing challenges in stochastic optimization where the objectives and constraints are defined implicitly by potentially expensive numerical models. Our approach explicitly accounts for parameter uncertainty using results from Bayesian quadrature for uncertainty propagation in Gaussian process surrogate models. The method accommodates arbitrary probability distributions and employs gradient-based optimization for acquisition function maximization, strategically selecting sample points to minimize numerical model evaluations. We demonstrate our algorithm’s superior performance over random search and conventional Bayesian optimization through both an analytical test function and a prestressed tie-beam design case study, showing its practical applicability to structural optimization problems.

Notating electroacoustic music can be challenging due to the uniqueness of the instruments employed. Electronic instruments can include generative components that can manipulate sound at different time levels, in which parameter variations can correlate non-linearly to changes in the instrument’s timbre. The way compositions for electronic instruments are notated depends on their interfaces and the parameter controls available to performers, which determine the state of their sound-generating system. In this article, we propose a notation system for generative synthesis based on a projection from its parameter space to a timbre space, allowing to organise synthesiser states based on their timbral characteristics. To investigate this approach, we introduce the Meta-Benjolin, a state-based notation system for chaotic sound synthesis employing a three-dimensional, navigable timbre space and a composition timeline. The Meta-Benjolin was developed as a control structure for the Benjolin, a chaotic synthesiser. Framing chaotic synthesis as a specific instance of generative synthesis, we discuss the advantages and drawbacks of the state- and timbre-based representation we designed based on the thematic analysis of an interview study with 19 musicians, who composed a piece using the Meta-Benjolin notational interface.

Analyzing topics and emotions in social media activism offers valuable insights into the competing voices that shape digital discourse. However, existing research has largely neglected the influence of geographic and linguistic diversity on public dialogue during crises. To address this gap, it is essential to recognize the varied perspectives of local communities and language groups. Doing so helps uncover specific local needs, ensures more inclusive representation, and supports the development of solutions that are responsive to the local context. We leverage machine learning models to analyze 1,036,111 public tweets from the #NoMore movement, including tweets containing #NoMore, #EthiopiaPrevails, and #SayNoMore. Our analysis examined the differences in content, emotional responses, and user influence by comparing tweets from Ethiopia and the United States (US), as well as those written in English and Amharic. The findings reveal distinct societal perspectives, emotional expressions, and opinion dynamics. Ethiopian users emphasized local issues with higher fear and joy responses, while users from the US leaned toward peace-related themes with spikes in anger. Amharic tweets focused on domestic concerns with greater emotional intensity than English tweets. These insights help surface region and language-specific perspectives often marginalized in mainstream coverage, paving the way for more inclusive and effective approaches to societal challenges.

This article presents and discusses a table of audiovisual transformations based on practice-based experience. The transformations were designed to reinforce the link between sound and object by considering what a particular audio process would look like if translated into visual form. The creative work involves installations that focus on objects integrated with projection mapping and electroacoustic sound. Examples of other artists who create object-based works are introduced, followed by a discussion around how electroacoustic music can influence audiovisual approaches. Screen and installation-based audiovisual theory expands on this and links to a two-part table of transformation strategies. The first part of the table describes process-based links that were created to imagine how certain electroacoustic studio techniques would translate to alter visual material. The second part describes broader conceptual links between audio and visual elements. The findings offer an insight into how electroacoustic practice can inform audiovisual composition choices. Whilst the intended use was for sound installations, there is significant scope for others to adopt and adapt the transformation strategies beyond this, including visual artists who wish to work with sound and those seeking to further theorise audiovisual relationships in a variety of settings.

This text addresses the materiality of radio art, situating it within the theoretical frameworks of contemporary research on new materialism as well as the materiality of media and sound. The analysis employs perspectives from Christoph Cox’s sonic materialism and approaches by such writers as Salomé Voegelin, Gregory Whitehead, Allen S. Weiss and Margaret Hall, who emphasise the ontological autonomy of sound and its impact on space and listeners. A critical close reading of the relevant literature is conducted with regard to its applicability to radio art. The article analyses radio art practices structurally and phenomenologically across composition, reception, materiality and technology, aligning with practice-informed media analysis. The author’s aim is to outline and systematise diverse theoretical approaches and frameworks that capture the materiality of radio being, as well as to reveal the ways in which the radio medium co-creates artistic sound reality. The results of the literature and artistic practice analysis highlight the significance of sound’s materiality and its relational character, indicating that sound does not exist in isolation but in interaction with the environment, technology and listener. Consequently, seven dimensions of radio art materiality are delineated, which integrate existing concepts and provide a comprehensive perspective on radio artistic works.

The development of electroacoustic music in China over the past four decades has been shaped not only by the nation’s modernisation strategies but also by the interplay of historical contexts, temporal frameworks and cultural connotations. While certain achievements have been made in the current phase, the entrenched dualistic framework of ‘China versus the West’ and a lack of critical inquiry fundamentally constrain the potential for further advancement in China’s electroacoustic music. Positioning ‘Chineseness’ as a central strategy in electroacoustic music composition has proven effective in specific historical contexts. However, with the evolution of the times, this strategy requires re-examination and reassessment within contemporary contexts. This paper seeks to trace the developmental trajectory of electroacoustic music in China and analyse existing academic research to identify and unpack its deeper, underlying issues. By introducing a broader ecological perspective, the paper aims to transcend the rigid, dichotomous framework dominated by Chinese-Western dualism, deconstruct cultural essentialism and critically reassess the positioning of Chinese electroacoustic music within these constructs. Finally, it will explore the potential possibilities and responses of an ecological perspective in practice, based on a selection of compositional practices, including my own work Mixobloodify.

Surrogate models have gained widespread popularity for their effectiveness in replacing computationally expensive numerical analyses, particularly in scenarios such as design optimization procedures, requiring hundreds or thousands of simulations. While one-shot sampling methods—where all samples are generated in a single stage without prior knowledge of the required sample size—are commonly adopted in the creation of surrogate models, these methods face significant limitations. Given that the characteristics of the underlying system are generally unknown prior to training, adopting one-shot sampling can lead to suboptimal model performance or unnecessary computational costs, especially in complex or high-dimensional problems. This paper addresses these challenges by proposing a novel, model-independent adaptive sampling approach with batch selection, termed Cross-Validation Batch Adaptive Sampling for High-Efficiency Surrogates (CV-BASHES). CV-BASHES is first validated using two analytical functions to explore its flexibility and accuracy under different configurations, confirming its robustness. Comparative studies on the same functions with two state-of-the-art methods, maximum projection (MaxPro) and scalable adaptive sampling (SAS), demonstrate the superior accuracy and robustness of CV-BASHES. Its applicability is further demonstrated through a geotechnical application, where CV-BASHES is used to develop a surrogate model to predict the horizontal deformation of a diaphragm wall supporting a deep excavation. Results show that CV-BASHES efficiently selects training samples, reducing the dataset size while maintaining high surrogate accuracy. By offering more efficient sampling strategies, CV-BASHES streamlines and enhances the process of creating machine learning models as surrogates for tackling complex problems in general engineering disciplines.

This article investigates identity formation among contemporary South American electroacoustic music composers through the lens of place, territory and socio-political context. Drawing on interviews with 27 composers from across the continent, the study explores how artistic practices are shaped by affective ties to geography, histories of colonialism, academic migration and technology. The analysis highlights five intersecting identity factors: mestizaje and cultural hybridity, decolonial thinking, political activism through sound composition, the tension between belonging and displacement in artistic mobility and the workaround aesthetic rooted in limited resources. Rather than portraying scarcity as a limitation, composers often embrace it as a creative force that fosters innovation and local specificity. The findings suggest that electroacoustic music in South America reflects not only a diversity of individual trajectories but also shared cultural dynamics that distinguish the region’s creative processes. These insights contribute to broader discussions on decolonisation, identity and the global circulation of music technologies.

This article proposes the electromagnetic soundwalk as an anti-method for consumer research, a compositional practice that listens to the infrastructural residue of market environments without aiming to interpret, represent or explain. Using a handheld electromagnetic detector, the walk transposes imperceptible emissions into audible frequencies, revealing the operational murmur of retail systems. These include devices such as wireless payment systems, contactless terminals, touch-screen tablets and digital signage, technologies that organise and condition consumer experience, but do so silently, beneath the threshold of ordinary perception. These electromagnetic emissions trace the infrastructures that shape and facilitate consumption yet remain formally outside marketing discourse. The soundwalk stages a form of methodological estrangement, where listening becomes a way of staying with systems that persist without expressive form. While rooted in soundwalking traditions, the project diverges from immersion or participation. Positioned within the sonic turn in consumer research, the paper reframes sound as residue, an ambient trace of logistical systems. For marketing, this is a speculative proposition. For sound studies, it is an example of compositional listening used to breach an adjacent field. What results is not a soundwalk for its own sake, but an acoustic method for hearing how consumer systems continue, quietly and without reward. The first section of the paper adopts a speculative and affective tone, free of citation, to evoke the experiential register of the method. Subsequent sections develop the theoretical and methodological foundations in a more conventional academic voice.

This study presents a framework that combines Bayesian inference with reinforcement learning to guide drone-based sampling for methane source estimation. Synthetic gas concentration and wind observations are generated using a calibrated model derived from real-world drone measurements, providing a more representative testbed that captures atmospheric boundary layer variability. We compare three path planning strategies—preplanned, myopic (short-sighted), and non-myopic (long-term)—and find that non-myopic policies trained via deep reinforcement learning consistently yield more precise and accurate estimates of both source location and emission rate. We further investigate centralized multi-agent collaboration and observe comparable performance to independent agents in the tested single-source scenario. Our results suggest that effective source term estimation depends on correctly identifying the plume and obtaining low-noise concentration measurements within it. Precise localization further requires sampling in close proximity to the source, including slightly upwind. In more complex environments with multiple emission sources, multi-agent systems may offer advantages by enabling individual drones to specialize in tracking distinct plumes. These findings support the development of intelligent, data-driven sampling strategies for drone-based environmental monitoring, with potential applications in climate monitoring, emission inventories, and regulatory compliance.

Design creativity is an inherently complex and recursive cognitive process involving nonlinear transitions between distinct cognitive states. This experimental neurocognitive study provides empirical support for theoretical nonlinear and recursive models of design creativity by examining neurocognitive processes across design creativity cognitive states, including idea generation (IDG), idea evolution (IDE), rating process (IDR), and rest mode (RST). EEG signals were recorded during loosely controlled design creativity tasks, and 13 well-established features were extracted from recurrence quantification analysis (RQA). A feature selection pipeline identified the most significant features for distinguishing between the cognitive states. Statistical analyses of the features provided deeper insights into brain dynamics and confirmed the significance of the selected features, supported by EEG topography maps. The findings revealed distinct and complex recursive dynamics across cognitive states, primarily involving the frontal, parietal and central regions, offering novel insights complementary to prior EEG studies. We also classified the cognitive states using the selected significant features through six classification models: k-Nearest Neighbor, Support Vector Machine, Naïve Bayes, Multi-Layer Perceptron, Linear Discriminant Analysis and Random Forest. To ensure robust evaluation, we applied three cross-validation strategies – hold-out, k-fold and one-subject-out – and combined the classifiers using majority voting fusion. Classification results (10-fold cross-validation) demonstrated high performance, with an average accuracy (96.23%), kappa (93.56%), recall (96.58%), precision (98.08%), F1-score (97.29%) and specificity (98.43%). The study provides findings that are consistent with theoretical expectations. Consistent with theoretical expectations, the findings deepen understanding of recursive and nonlinear neural dynamics in design creativity cognition and guide future research.

This essay investigates intermedial interference – a perceptual phenomenon arising from the interaction of media features within the intermedial space – in the context of electroacoustic audiovisual composition. Grounded in visual music and intermedial arts traditions, this research explores strategies for combining, integrating and fusing sound and moving images to create artefacts that transcend conventional multimedia juxtaposition. This essay refers to the author’s doctoral practice-based research, in which a portfolio of six works is examined through the study, discussing the nature of interference, the interaction of media features in the intermedial space, the role of balance in managing perceptual equilibrium and novel compositional methods, including associative mapping and synchrony typologies. A case study of one of the portfolio works illustrates the application of these concepts, emphasising remediation, meta-narrative and audience interpretation. The findings contribute new insights into intermedial audiovisual practice, offering methodologies for composers to harness media interactions and foster open, subjective engagements with intermedial artefacts.

All around Santiago, Chile, there are water towers known to citizens as Copas de Agua. These towers are recognised as modern industrial heritage and integral landmarks within the city’s urban landscape, contributing significantly to its cultural identity. This article presents strategies for exploring aural architecture by creating a new space defined by sound in motion within an existing architectural structure with unique morphological and acoustic characteristics through the medium of sound installation art. The project Polyphono, a multichannel sound installation located within the Copa de Agua of Quinta Bella, Chile, establishes a dialogue between three different spaces: the invisible space of the sound installation, the existing space of the water tower and the symbolic space of experience. In this process, an interior space of sound emerges within the physical space of acoustic reactions, which is experienced by the audience as aural architecture. This dynamic situation involves animating the monumentality of the water tower, transforming it through the performative action of the sound installation, thereby intensifying the historical significance of the site in physical, sensory, political and social terms. The outcomes are framed as a transitional space, from site-specific sound to aural architecture, creating an affective space for aesthetic experience.

Creative thinking is a crucial step in the design ideation process, where analogical reasoning plays a vital role in expanding the design concept space. The emergence of Generative AI has brought a significant revolution in co-creative systems, with a growing number of studies on Design-by-Analogy support tools. However, there is a lack of studies investigating the creative performance of Large Language Model (LLM)-generated analogical content and benchmarking of language models in creative tasks such as design ideation. Through this study, we aim to (i) investigate the effect of creativity heuristics by leveraging LLMs to generate analogical stimuli for novice designers in ideation tasks and (ii) evaluate and benchmark language models across analogical creative tasks. We developed a support tool based on the proposed conceptual framework and validated it by conducting controlled ideation experiments with 24 undergraduate design students. Groups assisted with the support tool generated higher-rated ideas, thus validating the proposed framework and the effectiveness of analogical reasoning for augmenting creative output with LLMs. Benchmarking of the models revealed significant differences in the creative performance of analogies across various language models, suggesting that future studies should focus on evaluating language models across creative, subjective tasks.

As the volume of meteorological observations continues to grow, automating the quality control (QC) process is essential for timely data delivery. This study evaluates the performance of three machine learning algorithms—autoencoder, variational autoencoder, and long short-term memory (LSTM) autoencoder—for detecting anomalies in air temperature data. Using expert-quality-controlled data as ground truth, all models demonstrated anomaly detection capability, with the LSTM outperforming others due to its ability to capture temporal patterns and minimize false positives. When applied to raw data, the LSTM achieved 99.6% accuracy in identifying valid observations and replicated 79% of manual flags, with only five false negatives and six false positives over a full year. Its sensitivity to subtle meteorological changes, such as those caused by rainfall or cloud cover, highlights its robustness. The LSTM’s performance using a three-day timestep, combined with basic QC checks in SaQC (System for Automated Quality Control), suggests a scalable and effective solution for automated QC at Met Éireann, with potential for expansion to include additional variables and multi-station generalization.