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A study of voice quality and acoustic variability in sound prolongation performance in 5–12-year-old children
Get PDFIntroduction: Voice disorders, or dysphonia, in children impact communication, social interactions, and quality of life, emphasizing the need for effective assessment tools with accurate reference norms. Acoustic measures taken during sound prolongation are widely used to evaluate voice quality, but variability in children’s performance and
limited norms from children from diverse backgrounds pose challenges for clinicians. This study investigated voice quality and variability in sound prolongation tasks among 5–12-year-old school children, contributing to the development of acoustic reference data.
Method: A totalof 275 primary school-aged children in Scotland
participated, producing sustained phonations of [a], [s], and [z] to evaluate respiratory and phonatory performance. Durations and acoustic measures, including jitter, shimmer, harmonics-to-noise ratio (HNR), cepstral peak prominence (CPP), and s/z ratio, were analyzed to capture variability in performance.
Results: Analysis indicated significant agerelated increases in sound prolongation durations, with older children (7–12 years) outperforming younger children (5–6 years), reflecting enhanced respiratory capacity and
vocal fold control. While jitter, shimmer, and HNR did not differ significantly across age groups, CPP values were higher in older children, indicating improved vocal stability and harmonic richness. Median s/z ratios also showed significant age-related changes, highlighting developmental changes in phonatory and respiratory coordination.
Notably, children exhibited longer average sound prolongation durations than previously reported norms, with considerable variability in performance. No significant sex differences were found, except for the s/z ratio, where females had higher values. Conclusion: These findings contribute and advance the growing body of reference data for
assessing voice quality in children and emphasize the importance of factors such as age and sex in large, diverse samples. The study highlights the need to account for developmental variability and robust, comprehensive methodologies to contextualize voice quality issues in children
Navigating uncharted waters: the implementation of the EU Directive on Preventive Restructuring Frameworks in Luxembourg
Get PDFThe Business Preservation Law (BPL), adopted in August 2023, marks a milestone in modernising Luxembourg’s insolvency framework, aligning it with Directive (EU) 2019/1023. This paper analyses the BPL’s integration within Luxembourg law, highlighting key features such as early intervention mechanisms, stay of enforcement actions, and court-supervised restructuring plans. It assesses how the reform addresses longstanding deficiencies, promotes business continuity, and fosters a culture of early restructuring, while identifying initial challenges. Offering a first critical appraisal, this contribution positions the BPL as a pivotal step towards a more resilient and competitive restructuring framework
The quality of life of colorectal cancer patients attending the cancer center in Addis Ababa, Ethiopia
Get PDFBackground:
Globally, colorectal cancer (CRC) is the third most common type of cancer and the second most deadly. CRC significantly impairs patients’ overall and health-related quality of life, as well as their psychological and physical function. However, in Ethiopia, there is insufficient local evidence about the quality of life of patients with colorectal cancer. Hence, this study aimed to assess the quality of life of adult colorectal cancer patients who have follow-ups at the cancer center in Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia.
Methods:
An institutional-based cross-sectional study was conducted among 159 colorectal cancer patients attending the Tikur Anbessa Specialized Hospital Cancer Center from February to April 2019. The validated Amharic version of the European Organisation for Research and Treatment of Cancer Core 30 questionnaire (EORTC QLQ C-30) and the disease-specific colorectal cancer questionnaire (EORTC QLQ CR-29) were used. A binary logistic regression model was used to identify the factors associated with quality of life. The adjusted measure of effect (AOR) with a 95% CI was presented and P < 0.05 was used to declare statistical significance.
Results:
There were 159 colorectal cancer patients, 89 of whom were male, and the median time from diagnosis was 12.5 months. The patients had a low global health status score with a mean (± SD) of 52.88 ± 21.02. Being employed (AOR = 3.41; 95% CI 1.15, 10.17), early-stage clinical diagnosis (AOR = 4.98; 95% CI 1.51, 16.4), physical functioning (AOR = 1.04, 95% CI 1.01, 1.06), and social functioning (AOR = 1.02; 95% CI 1.01, 1.04) were associated with good quality of life. Whereas, being female (AOR = 0.16; 95% CI 0.05, 0.52), having financial difficulty (AOR = 0.98; 95% CI 0.96, 0.99), and having blood and mucus in the stool (AOR = 0.94; 95% CI 0.91, 0.96) were associated with poor quality of life.
Conclusion:
In our study, half of our study participants had poor quality of life. The responsible stakeholders should identify and address the patients’ respective symptoms. Female patients, those in severe clinical stages, unemployed patients, those experiencing financial difficulties, and those with blood and mucus in their stool should receive due attention
Predicting the methane production of microwave-pretreated anaerobic digestion of food waste: a machine learning approach
Get PDFAnaerobic digestion (AD) is a widely adopted waste management strategy that transforms organic waste into biogas, addressing both energy and environmental challenges. Feedstock pretreatment is crucial for enhancing organic matter breakdown and improving biogas yield. Among various techniques, microwave (MW) irradiation-based pretreatment has shown significant promise. However, the optimization of MW-assisted AD processes remains underexplored, necessitating predictive tools for process simulation. Machine Learning (ML) has recently emerged as a powerful alternative for predicting and optimizing AD performance. In this study, an ML-driven pipeline was developed to predict methane yield based on food waste (FW) composition, AD reactor parameters, and MW pretreatment conditions. A range of data preprocessing techniques and ML models (linear, non-linear, and ensemble) were systematically evaluated, with model performance assessed via hyperparameter-optimized cross-validation. The most accurate models (non-linear and ensemble) achieved R2 > 0.91 and RMSE < 35 mL/g volatile solids (gVS), whereas linear models underperformed (R2 < 0.71, RMSE > 70 mL/gVS). Support Vector Machine (SVM) emerged as the best-performing model, with R2 ∼0.94 and RMSE ∼34 mL/gVS. Beyond predictive accuracy, this study offers novel insights into MW pretreatment’s role in AD efficiency. Permutation feature importance (PFI) analysis revealed that while MW pretreatment enhances methane yield, its effects are secondary to reactor pH and FW composition. This suggests that MW treatment primarily facilitates substrate disintegration but does not drastically alter biochemical methane potential unless coupled with optimized reactor conditions. Additionally, minor fluctuations in MW pretreatment time and temperature were found to have negligible impacts on methane production, indicating a level of operational flexibility in MW-based AD processes. These findings provide a refined understanding of MW pretreatment’s practical implications, guiding process design for improved scalability and industrial application
“It’s like getting someone who’s been hit by a car to run a speed awareness course”: how young activists in the UK make sense of personal, collective and institutional agency
Get PDFCurrent research into youth activism places emphasis on agency, but few empirical studies have looked into how the perception of agency impacts young people’s engagement (and non-engagement) in political activities. Drawing on online focus group discussions, this study investigates how 16 to 24 year-old young activists perceive agency, differentiating between three categories of agency. These categories include the capacity to act that young people ascribed to themselves (personal agency), to others (collective agency) and to political decisionmakers (proxy or institutional agency). Young people generally held positive beliefs about collective agency. In contrast, they expressed varying perceptions about their own capacity to affect change. Although young people placed significant responsibility on political institutions, they felt these institutions fell short in exercising the entrusted agency. The analysis highlights how structural constraints and societal inequalities shape young people’s perceptions of agency which, thus, may have an impact on participatory behaviors
International(-ised) Students in East and Southeast Asia: Towards a New Conceptualisation
No full textThis book presents new narratives about international students undertaking transnational education (TNE) in East and Southeast Asia.
With contributions from leading experts in international higher education, each chapter provides important critical arguments about the outdatedness of traditional labels that we tend to use when describing international students and their experiences. The book disrupts these narrow labels with voices and authors from Japan, Laos, China, and Vietnam to show that the categories through which we see international students overshadow the complexities of the students themselves and how their TNE experiences shape them. The book examines the traditional label of an ‘international student’ and invites readers to explore and use the alternative phrase ‘internationalised student.’
This book will appeal to scholars, upper-level students, and researchers interested in international and comparative education, transnational education, migration studies, and higher education administration
A green and inclusive post-pandemic recovery of the Blue Economy and coastal communities
Get PDFA team of researchers from the University of Massachusetts Amherst, the University of Glasgow, the United Nations University - Institute for Environment and Human Security (in Bonn) and the University of Costa Rica has been developing a trans-national comparative study across coastal areas in Costa Rica, Germany, Scotland (UK), and the USA to provide science-based guidance for post COVID recovery of coastal communities and shed light on green development and climate resilience strategies adopted at the local level. As part of this project the research team conducted a survey of municipalities and planning agencies in Costa Rica, Germany, Massachusetts, and Scotland, from June 2023, to March, 2024. The key results are outlined below. Even though the sample size for Scotland and Germany is sometimes small, and therefore estimates should be taken with caution, the figures still provide some insightful information
Revisiting the subharmonic route to acoustic chaos: broadband noise clearing via cavitation bubble synchronisation
Get PDFThe cavitation activity within a tube transducer subject to a linearly increasing drive amplitude is studied experimentally, via high-speed imaging and parallel acoustic detection. A spectrogram of the cavitation emission signal collected over the duration of the ramped sonication, confirms a subharmonic route to chaos; the progression from harmonic emissions to broadband noise via a period-doubling bifurcation. Stroboscopic mapping demonstrates that the emergence of broadband signal is due to increasingly asynchronous bubble collapse across the population. Particular attention is given to a region of sudden broadband clearing in the spectrogram, coincident with strong subharmonic lines, approximately halfway through the sonication. The clearing is due to phase synchronization of the bubble oscillations. Broadband noise gradually re-emerges, often with further bifurcation to include period-quadrupled emissions, as oscillations dephase again following synchronization. Numerical modelling investigates the influence of amplitude ramping, inter-bubble distance and bubble dispersity on the oscillations and emissions from a 12-bubble system, based on an experimental observation. For a polydisperse system, it is found that a single bubble responding with period-2 oscillations initiates period-2 synchronization for the population, offering explanation for the consistent observation that prominent subharmonic emissions at half the driving frequency coincide with broadband clearing
NLOS identification and ranging trustworthiness for indoor positioning with LLM-based UWB-IMU fusion
Get PDFIn the rapidly evolving Internet of Things (IoT) landscape, accurate indoor positioning is increasingly vital. The proposed algorithm synergizes Ultra-Wideband (UWB) sensor with Inertial measurement unit (IMU) and Artificial Intelligence to obtain precise positioning in Non-Line-of-Sight (NLOS) scenarios. In the proposed UWB module, Large language model (LLM) such as Bidirectional Encoder Representations from Transformers (BERT) algorithm is designed to utilize the Channel Impulse Response (CIR) for effective NLOS identification and UWB ranging trustworthiness evaluation. Concurrently, the IMU module is also designed with BERT to recognize various pedestrian activity states, thereby optimizing positioning. BERT’s self attention mechanism and deep learning bidirectional training efficiently extract essential features from sequential data, capturing both local and global information. The integration of both UWB and IMU through a proposed tightly coupled algorithm significantly boosts positioning performance. Experiment campaigns demonstrate an average NLOS identification accuracy, Line-of-Sight (LOS) and F2 of 98.8%, 99.4% and 0.9926, respectively. These performance surpass the state-of-the-art Least Squares Support Vector Machine (LS-SVM), Convolutional Neural Network (CNN), CNN with Long Short-Term Memory (CNN-LSTM) up to 17.66% in NLOS identification. In terms of pedestrian activity recognition using BERT, the BERT algorithm achieves a precision(recall) of 99.3%(99.4%), notably outperforming CNN and CNN-LSTM by 17.9%(16.2%) and 11.9%(10.9%), respectively. Finally, the UWB-IMU algorithm significantly enhances positioning accuracy by 80.5%, outperforming Kalman, LSTM-EKF, and Particle filter methods by 68.1%, 48.3%, and 45.0%, respectively. The proposed approach presents a robust solution for indoor positioning for IoT applications, particularly in challenging NLOS environments
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