We know who they are, because of what they sing: Miao song taxonomy in Fenghuang county, China

The Miao of Fenghuang county, Hunan province, China, maintain two distinct song taxonomies. One is functional and shared across all Miao communities, while the other is specific to the county, is known as the Fenghuang Miao taxonomy, and is based on locality and melody types. This study explores the Fenghuang Miao taxonomy through consultations with community members and examinations of locally produced songbooks and documentary videos. Utilising ethnographic and musicological approaches, the study reveals the intricate relationships between melody type and four aspects: locality, function, theme, and performance setting. Unlike existing scholarly categorisations, this taxonomy not only reflects Miao distinctions and priorities but also extends to broader Miao social dynamics. Interview findings highlight the practical significance of songs as relevant to subgroups' identities, with song-masters and singers able to identify subgroups' based on melody types. Analysis of musical elements reveals correlations between melody types and subgroup aesthetics

The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening

A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening

Steep-slope vertical-transport transistors built from sub-5 nm Thin van der Waals heterostructures

Two-dimensional (2D) semiconductor-based vertical-transport field-effect transistors (VTFETs) – in which the current flows perpendicularly to the substrate surface direction – are in the drive to surmount the stringent downscaling constraints faced by the conventional planar FETs. However, low-power device operation with a sub-60 mV/dec subthreshold swing (SS) at room temperature along with an ultra-scaled channel length remains challenging for 2D semiconductor-based VTFETs. Here, we report steep-slope VTFETs that combine a gate-controllable van der Waals heterojunction and a metal-filamentary threshold switch (TS), featuring a vertical transport channel thinner than 5 nm and sub-thermionic turn-on characteristics. The integrated TS-VTFETs were realised with efficient current switching behaviours, exhibiting a current modulation ratio exceeding 1 × 108 and an average sub-60 mV/dec SS over 6 decades of drain current. The proposed TS-VTFETs with excellent area- and energy-efficiency could help to tackle the performance degradation-device downscaling dilemma faced by logic transistor technologies

The Edinburgh Companion to Literature and Sound Studies

This field-defining collection maps key intersections between sound studies and literary studies Provides a unique focus on literary applications of sound studies research Features a wide range of international, emergent and established scholars Interdisciplinary work throughout Considers a broad range of historical periods Features entirely new commissioned work; no republished material available elsewhere is included Collections on sound studies have seldom explored the vexed relationship between literature – a medium largely defined by its silence – and the dynamics and technologies of sound. This Companion is designed to help sound studies scholars grapple with the auditory capacities of text and encourage literary scholars to take full cognisance of the rich soundscapes mapped, or created, by texts read quietly. The essays assembled here consider a broad range of sound studies topics, including music in writing; the inscription of listening; worlding through sound; military and industrial noise; the gender of sound; racialised soundscapes; theatrical sounds; literature and sound media; and sonic epistemology. Helen Groth and Julian Murphet present a comprehensive set of new research on the relationship between sound and writing over time from a range of eminent, established and emerging sound studies scholars

Explainable AI for early-stage design

With the merging of vast amounts of data and advanced computing resources, machine learning has become a key tool in helping designers make informed decisions in early-stage design. However, the opacity of high-performance machine learning models, often called “black boxes”, makes it hard to understand their workings. Moreover, these models make predictions without explanations, which affects designers’ trust and understanding of the predictions. Explainable AI is a growing field of research focused on creating explanations that humans can understand to improve data, model, and post-hoc explainability. In this dissertation, the incorporation of explainable AI into early-stage design is proposed. Specifically, the application of machine learning in early-stage design is investigated, highlighting the challenges posed by black-box models, particularly regarding data explainability, model transparency, and post-hoc explainability. By situating these challenges within the context of early-stage design, it is demonstrated how the lack of explainability undermines designers’ trust in AI. This erosion of trust affects both the inherent and perceived trustworthiness of AI, thus impeding effective collaboration. To enhance inherent trustworthiness, an explainable AI-centric design framework that harnesses feature-based and data-based explainable AI is introduced. Through a customer segmentation case study, the capacity of explainable AI to boost AI efficacy and transparency is demonstrated: feature-based explanations aid in selecting features and understanding the model mechanism, while data-based explanations inform about valuable datasets. Furthermore, to improve post-hoc explainability and thereby boost perceived trustworthiness, a comprehensive framework that synergizes knowledge graph with ChatGPT is introduced. The customer segmentation case study showcases how the innovative explainable AI method, created by combining knowledge graph with ChatGPT, generates more meaningful and contextually rich explanations. The explanations, which include domain-specific knowledge and information specific to the model, enable designers to understand the underlying predictions more clearly and make well-informed decisions. In conclusion, this research advances the field by bringing explainable AI to design contexts, and developing methods that enhance collaboration between designers and AI in early-stage design

On Trust Recommendations in the Social Internet of Things - A Survey

The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized through trust recommendations. However, the outcome of trust recommendations depends on multiple factors related to the context-dependent nature of SIoT and practical constraints brought by the devices and networks embedded in the SIoT. While the existing literature has proposed numerous trust recommendation models to assess the trustworthiness of devices in various scenarios, researchers have not sufficiently examined the required features for trust recommendations in the SIoT. Consequently, trust recommendation models may inaccurately assess the true risk of device interactions. In this literature survey, we investigate the context-dependent features and recommendation methods used for the SIoT using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We propose a novel taxonomy to categorize trust recommendation models according to their input features and design. Our findings reveal limited attention is given to the context-dependent features, constraints of the information environment, and limited inference capabilities that impede more precise trust recommendations. Finally, we present the research gaps and outline future directions to enable trustworthy inter-domain operations within the SIoT

Development and Application of Artificial Neural Networks for Energy Demand Forecasting in Australia

Energy plays a very significant role in the operation of the economic machinery of a country. Insufficient energy supply can lead to high energy prices, which, in turn, can cause many, if not all, prices of commodities to increase. This leads to inflation and all its consequential adverse outcomes within the economy. For this reason, energy planning is an essential macroeconomic planning activity for the economic planning of the country. Like most other countries, energy planning in Australia is done through econo metric modelling. This is done using linear regression models that correlate energy demand (independent variable) to other macroeconomic factors, like gross domes tic product, as dependent variables. However, such a technique is unsuitable when complex interactions exist between variables. Motivated by the success of Artificial Neural Networks (ANN), this thesis aims to develop an ANN model as an alternative tool for energy demand forecasting in Australia. First, a set of macroeconomic variables is chosen as potential input features (independent variables) for the energy demand forecasting model. The output feature (dependent variable) is the monthly energy use of Australia. The output feature was measured in tons of oil. Several incremental models are developed and run to see the incremental effect of adding features on the accuracy of performance of the ANN models. Correlation analysis of features is also performed to see how each feature affects the output feature and how they are related. Then, to determine what structure of an ANN would provide better perfor mance, three different structures are chosen and run on different datasets. Dif ferent testing and training periods are used to establish the performance of each model. To automate the process of optimising ANN models, a genetic algorithm is designed to optimise the number of neurons in different ANNs. In doing this, the overall structures and performance of optimised ANNs for predicting energy production in the Australian context are presented and assessed. Also, different types of evolutionary operators are designed and tested. The results of all variants are analysed, showing the benefit of optimising the ANN structure

Selective intrafascicular stimulation of myelinated and unmyelinated nerve fibers through a longitudinal electrode: A computational study

Carbon nanotube (CNT) fiber electrodes have demonstrated exceptional spatial selectivity and sustained reliability in the context of intrafascicular electrical stimulation, as evidenced through rigorous animal experimentation. A significant presence of unmyelinated C fibers, known to induce uncomfortable somatosensory experiences, exists within peripheral nerves. This presence poses a considerable challenge to the excitation of myelinated Aβ fibers, which are crucial for tactile sensation. To achieve nuanced tactile sensory feedback utilizing CNT fiber electrodes, the selective stimulation of Aβ sensory afferents emerges as a critical factor. In confronting this challenge, the present investigation sought to refine and apply a rat sciatic-nerve model leveraging the capabilities of the COMSOL-NEURON framework. This approach enables a systematic evaluation of the influence exerted by stimulation parameters and electrode geometry on the activation dynamics of both myelinated Aβ and unmyelinated C fibers. The findings advocate for the utilization of current pulses featuring a pulse width of 600 μs, alongside the deployment of CNT fibers characterized by a diminutive diameter of 10 μm, with an exclusively exposed cross-sectional area, to facilitate reduced activation current thresholds. Comparative analysis under monopolar and bipolar electrical stimulation conditions revealed proximate activation thresholds, albeit with bipolar stimulation exhibiting superior fiber selectivity relative to its monopolar counterpart. Concerning pulse waveform characteristics, the adoption of an anodic-first biphasic stimulation modality is favored, taking into account the dual criteria of activation threshold and fiber selectivity optimization. Consequently, this investigation furnishes an efficacious stimulation paradigm for the selective activation of touch-related nerve fibers, alongside provisioning a comprehensive theoretical foundation for the realization of natural tactile feedback within the domain of prosthetic hand applications

Improving the evidence about supported accommodation for people released from prison

Release from prison often presents multiple and reinforcing challenges for formerly incarcerated people, including poor mental and physical health, substance use disorders, barriers to employment, and unstable housing. Supported accommodation for people released from prison (hereafter referred to as supported accommodation) is intended to ameliorate these co-occurring issues. Supported accommodation may take different forms, including group homes or scattered-site housing (which provide individuals or groups with their own accommodation) along with case management or other therapeutic activities. There is, however, a lack of methodologically rigorous evidence to guide delivery of supported accommodation for people released from prison. The research presented in this thesis was designed to improve the evidence on the impact of supported accommodation. The research employed mixed methods to increase the utility of the evidence it generated about supported accommodation. Firstly, a systematic review was conducted to identify the key characteristics of supported accommodation described in the peer-reviewed literature, including program components and outcomes/impact, and to identify the best-available evidence components (Chapter 2). Secondly, a qualitative study was conducted with clients of The Rainbow Lodge Program (RL), a supported accommodation service for men in Sydney, New South Wales (NSW), to explore their perspectives on the challenges experienced following release from prison, and how RL helped overcome them (Chapter 3). Thirdly, a co-design workshop was conducted with RL staff, at which evidence from Chapters 2 and 3 was synthesised along with the expertise of RL staff to develop a model of supported accommodation which is both standardised by best-evidence and able to be tailored to suit services’ circumstances (Chapter 4). Finally, linked administrative data was used to assess the impact of attending RL on the health and criminal justice outcomes of men released from prison in NSW between 2015 and 2020 (Chapter 5). The findings of this research have several important implications for future research and service delivery. Application of the methods described in this thesis has the potential to increase the capacity of supported accommodation to improve outcomes for people released from prison and improve the current evidence base through more rigorous evaluation