Search Results (522)

Objectives: To investigate the reliability and validity of the Singer Reflux Symptom Score (sRSS), a new patient-reported outcome questionnaire documenting the severity of reflux symptoms in singing voice is proposed. Methods: Amateur and professional singers consulting the European Reflux Clinic for laryngopharyngeal reflux disease (LPRD) symptoms and findings were prospectively recruited from January 2022 to February 2023. The diagnosis was based on a Reflux Symptom Score (RSS) > 13 and Reflux Sign Assessment (RSA) > 14. A control group of asymptomatic singer subjects was recruited from the University of Mons. The sRSS was rated within a 7-day period to assess test–retest reliability. Internal consistency was measured using Cronbach’s α in patients and controls. A correlation analysis was performed between sRSS and Singing Voice Handicap Index (sVHI) to evaluate convergent validity. Responsiveness to change was evaluated through pre- to post-treatment sRSS changes. The sRSS threshold for suggesting a significant impact of LPRD on singing voice was determined by receiver operating characteristic (ROC) analysis. Results: Thirty-three singers with suspected LPRD (51.5% female; mean age: 51.8 ± 17.2 years) were consecutively recruited. Difficulty reaching high notes and vocal fatigue were the most prevalent LPRD-related singing complaints. The sRSS demonstrated high internal consistency (Cronbach-α = 0.832), test–retest reliability, and external validity (correlation with sVHI: r = 0.654; p = 0.015). Singers with suspected LPRD reported a significant higher sRSS compared to 68 controls. sRSS item and total scores significantly reduced from pre-treatment to 3 months post-treatment except for the abnormal voice breathiness item. ROC analysis revealed superior diagnostic accuracy for sRSS (AUC = 0.971) compared to sRSS-quality of life (AUC = 0.926), with an optimal cutoff at sRSS > 38.5 (sensitivity: 90.3%; specificity: 85.0%). Conclusions: The sRSS is a reliable and valid singer-reported outcome questionnaire for documenting singing symptoms associated with LPRD leading to personalized management of Singers. Future large-cohort studies are needed to evaluate its specificity for LPRD compared to other vocal fold disorders in singers. Full article

Background: Preterm infants and their families face a challenging experience during their stay in the neonatal intensive care unit (NICU). Family-centered care emphasizes the importance of welcoming parents, involving them in their baby’s daily care, and supporting the development of parenting skills. NICU staff should support parents in understanding their baby’s needs and in strengthening the parent–infant bond. Although many tools outline what parents should learn, there is a limited structured framework to monitor their involvement in the infant’s care. Tracking parental participation in daily caregiving activities could support professionals in effectively guiding families, ensuring a smoother transition to discharge. Aims: The aim of this study was to evaluate the adherence to and effectiveness of a structured tool for parental involvement in the NICU. This tool serves several key purposes: to track the progression and timing of parents’ autonomy in caring for their baby, to support parents in building caregiving competencies before discharge, and to standardize the approach of NICU professionals in promoting both infant care and family engagement. Methods: A structured template form for documenting parental involvement (“together TO-CARE template”, TTCT) was integrated into the computerized chart adopted in the NICU of Modena. Nurses were asked to complete the TTCT at each shift. The template included the following assessment items: parental presence; type of contact with the baby (touch; voice; skin-to-skin); parental involvement in care activities (diaper changing; gavage feeding; bottle feeding; breast feeding); and level of autonomy in care (observer; supported by nurse; autonomous). We evaluated TTCT uploaded data for very low birth weight (VLBW) preterm infants admitted in the Modena NICU between 1 January 2023 and 31 December 2024. Staff compliance in filling out the TTCT was assessed. The timing at which parents achieved autonomy in different care tasks was also measured. Results: The TTCT was completed with an average of one entry per day, during the NICU stay. Parents reached full autonomy in diaper changing at a mean of 21.1 ± 15.3 days and in bottle feeding at a mean of 48.0 ± 22.4 days after admission. The mean length of hospitalization was 53 ± 38 days. Conclusions: The adoption of the TTCT in the NICU is feasible and should become a central component of care for preterm infants. It promotes family-centered care by addressing the needs of both the baby and the family. Encouraging early and progressive parental involvement enhances parenting skills, builds confidence, and may help reduce post-discharge complications and readmissions. Furthermore, the use of a standardized template aims to foster consistency among NICU staff, reduce disparities in care delivery, and strengthen the support provided to families of preterm infants. Full article

To improve the accuracy of Japanese finger-spelled character recognition using an RGB camera, we focused on feature design and refinement of the recognition method. By leveraging angular features extracted via MediaPipe, we proposed a method that effectively captures subtle motion differences while minimizing the influence of background and surrounding individuals. We constructed a large-scale dataset that includes not only the basic 50 Japanese syllables but also those with diacritical marks, such as voiced sounds (e.g., “ga”, “za”, “da”) and semi-voiced sounds (e.g., “pa”, “pi”, “pu”), to enhance the model’s ability to recognize a wide variety of characters. In addition, the application of a change-point detection algorithm enabled accurate segmentation of sign language motion boundaries, improving word-level recognition performance. These efforts laid the foundation for a highly practical recognition system. However, several challenges remain, including the limited size and diversity of the dataset and the need for further improvements in segmentation accuracy. Future work will focus on enhancing the model’s generalizability by collecting more diverse data from a broader range of participants and incorporating segmentation methods that consider contextual information. Ultimately, the outcomes of this research should contribute to the development of educational support tools and sign language interpretation systems aimed at real-world applications. Full article

Disassembly is a crucial process in industrial operations, especially in tasks requiring high precision and strict safety standards when handling components with collaborative robots. However, traditional methods often rely on rigid and sequential task planning, which makes it difficult to adapt to unforeseen changes or dynamic environments. This rigidity not only limits flexibility but also leads to prolonged execution times, as operators must follow predefined steps that do not allow for real-time adjustments. Although techniques like teleoperation have attempted to address these limitations, they often hinder direct human–robot collaboration within the same workspace, reducing effectiveness in dynamic environments. In response to these challenges, this research introduces an advanced human–robot interaction (HRI) system leveraging a mixed-reality (MR) interface embedded in a head-mounted device (HMD). The system enables operators to issue real-time control commands using multimodal inputs, including voice, gestures, and gaze tracking. These inputs are synchronized and processed via the Robot Operating System (ROS2), enabling dynamic and flexible task execution. Additionally, the integration of deep learning algorithms ensures precise detection and validation of disassembly components, enhancing accuracy. Experimental evaluations demonstrate significant improvements, including reduced task completion times, enhanced operator experience, and compliance with strict adherence to safety standards. This scalable solution offers broad applicability for general-purpose disassembly tasks, making it well-suited for complex industrial scenarios. Full article

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

Objective: This study aims to critically review and synthesize the existing literature on the use of voice analysis in assessing nasal obstruction, with a particular focus on acoustic parameters. Data sources: PubMed, Scopus, Web of Science, Ovid Medline, and Science Direct. Review methods: A comprehensive literature search was conducted without any restrictions on publication year, employing Boolean search techniques. The selection and review process of the studies followed PRISMA guidelines. The inclusion criteria comprised studies with participants aged 18 years and older who had nasal obstruction evaluated using acoustic voice analysis parameters, along with objective and/or subjective methods for assessing nasal obstruction. Results: Of the 174 abstracts identified, 118 were screened after the removal of duplicates. The full texts of 37 articles were reviewed. Only 10 studies met inclusion criteria. The majority of these studies found no significant correlations between voice parameters and nasal obstruction. Among the various acoustic parameters examined, shimmer was the most consistently affected, with statistically significant changes identified in three independent studies. A smaller number of studies reported notable findings for fundamental frequency (F0) and noise-related measures such as NHR/HNR. Conclusion: This systematic review critically evaluates existing studies on the use of voice analysis for assessing and monitoring nasal obstruction and hyponasality. The current evidence remains limited, as most investigations predominantly focus on glottic sound and dysphonia, with insufficient attention to the influence of the vocal tract, particularly the nasal cavities, on voice production. A notable gap exists in the integration of advanced analytical approaches, such as machine learning, in this field. Future research should focus on the use of advanced analytical approaches to specifically extrapolate the contribution of nasal resonance to voice thus defining the specific parameters in the voice spectrogram that can give precise information on nasal obstruction. Full article

Bring them home, keep them home is research based in New South Wales (NSW) Australia, that aims to understand successful and sustainable reunification for Aboriginal families who have children in out-of-home care (OOHC). This research is led by Aboriginal researchers, and partners with Aboriginal organisations. It is informed by the experiences of 20 Aboriginal parents and family members, and more than 200 practitioners and professionals working in child protection and reunification. This paper traces the evolution of Bring them home, keep them home which is now at the forefront of influence for NSW child protection reforms. Using specific examples, it highlights the role of research advocacy and resistance in challenging and disrupting systems in ways that amplify the voices of Aboriginal families and communities and embeds these voices as the foundation for radical innovation for child reunification approaches. The paper shares lessons being learned and insights for Aboriginal-led research with communities in the pursuit of restorative justice, system change, and self-determination. Providing a framework for liberating Aboriginal families from child abduction systems, this paper seeks to offer a truth-telling and practical contribution to the international efforts of Indigenous resistance to child abduction systems. Full article

As climate change consequences intensify, higher education institutions (HEIs) have an opportunity and responsibility to model sustainable operations. This study examines how embracing shared knowledge and inclusion in sustainability decision making facilitates green human resource management (GHRM) efforts to invigorate organizational environmental performance. The study examines the effects of shared and inclusive governance on campus sustainability via a regression model and the mediating role of employee participation via a structural equation modeling approach. The results show that shared governance and inclusive governance positively predict the commitment of HEIs to reducing greenhouse gas emissions, and campus engagement mediates these relationships, underscoring the importance of participation. These findings align with stakeholder theory in demonstrating that diverse voices in decision making can enhance commitment to organizational goals like sustainability. The findings also highlight the importance of shared and inclusive governance arrangements at college campuses not only for ethical reasons but also for achieving desired outcomes like carbon neutrality. For campus leaders striving to “green” their institutions, evaluating cross-departmental representation in governance structures and promoting inclusive cultures that make all students and staff feel welcome appear as important complements to GHRM practices. Full article

Background and Clinical Significance: Avatar Therapy (AT) for individuals with treatment-resistant auditory verbal hallucinations (AVHs) in schizophrenia aims to address emotional responses, beliefs about voices, self-perception, and coping strategies. This study focuses on three participants who, during AT, shifted their belief about the origin of their most distressing voice from an external source to a self-generated one. Case Presentation: The objective of this study was to explore the evolution of the reattribution of the participants’ most distressing voice to oneself during AT and the patients’ perception of this reattribution. Immersive sessions and semi-structured interviews were transcribed and qualitatively described to provide a session-by-session account of the evolution of each participant’s AVH reattribution to themselves during the course of AT, along with their perceptions of this reattribution. This process led to the recognition that initially perceived as external voices were internally generated thoughts, reflecting how participants viewed themselves. Two participants reported a reduction in AVH severity. All three described positive changes in how they related to their voices and self-perception. Additional improvements were observed in emotional regulation, social functioning, and engagement in personal projects. Conclusions: This reassignment of the voice from an external source to an internal one suggests that AT can modify how individuals relate to their voices and may empower them to regain control over their hallucinations. However, given the exploratory nature of this study, the results should be interpreted as examples. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article

Background/Objectives: Patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) may report abnormalities in voice and speech; however, no formal research has been conducted in this area. Methods: An online mixed-methods survey was completed by 685 people with ME/CFS. A total of 302 respondents completed the qualitative component (44.09%). Questions assessed disease experience with ME/CFS and post-exertional malaise without prompting on specific symptoms. Within the qualitative results, a search of the terms “speech, voice,” “words,” and “speak” was conducted. Results: Excluding neurocognitive associations, colloquial phrases, and “speech therapy,” there were 38 mentions of the terms in the context of voice or speech changes across 28 unique qualitative survey responses (9.27%). Conclusions: A notable portion of respondents reported voice or speech changes when responding to open-ended qualitative questions about their disease experience. More research is needed regarding the implications of voice and speech anomalies in ME/CFS pathology and disease monitoring. Full article

Young people are frustrated and disheartened with the lack of adult leadership and action to address the climate crisis. Although youth representation in global, regional, and local decision-making contexts on climate change is steadily growing, the desired role and effect of youth in environmental and climate decision-making has shifted from a focus on having youth voices heard, to having a direct and meaningful impact on policy and action. To meaningfully integrate youth perspectives into climate policies and programs, intergenerational approaches and youth–adult partnerships are key. This paper explores strategies to support youth action and engagement as adult partners by investigating youth perspectives on what adults and adult-led organizations should consider when engaging young people in climate-related work. This qualitative research study introduces a revised version of the 7P youth participation framework, developed through focus groups with high school youth. This paper provides reflective questions and practical recommendations for participants engaged in youth–adult partnerships to help guide engagement beyond token representation and create meaningfully participatory conditions for youth agency in climate organizing spaces. Full article

The quality of children’s early childhood education (ECE) experiences significantly impacts their long-term outcomes and wellbeing. While extensive research has explored quality from the perspectives of adult stakeholders, including educators and authorities, there remains a paucity of studies prioritizing the viewpoint of children, the main beneficiaries of ECE. This study sought to address this gap by investigating children’s preschool experiences at an Australian inner-city preschool center. Using child-friendly interview techniques, researchers engaged 32 children aged 3–4 years in discussions about their likes, dislikes, and desired changes in their preschool settings. Open-ended questions such as “What do you love about preschool?” and “What do you think makes a good preschool?” were used to encourage reflection and storytelling. To complement verbal responses, children were invited to illustrate their thoughts through drawings, offering a visual dimension to their perspectives. Deductive thematic analysis identified eight themes within the dimensions of structural and process quality. The findings highlight the unique and insightful ways young children interpret their experiences, shedding light on aspects of preschool life they value most. By amplifying children’s voices, this study highlights the importance of integrating their perspectives into the design and evaluation of ECE environments, promoting practices that better align with their needs and support their wellbeing. Full article

This community-based participatory research (CBPR) study explored, using a Photovoice methodology, the lived expeiences of northeastern Black and/or African American youth and mothers who were currently experiencing the incarceration of their fathers and husbands. Grounded in critical theories of dual consciousness and comparative conflict, the findings provide valuable insights into how this population navigates the intersections of family, school, and community within the context of the criminal legal system, and, in the process, underscore the relevance of Abolitionist practice in capturing their theoretically lived experiences. Participants documented through photography and narrative reflections the multifaceted impacts of incarceration on fathers and husbands, including disrupted family dynamics, social stigma, and barriers to community resources. A focus group with the mothers of these youth highlighted the profound impact of incarceration on their family structure, revealing significant emotional burdens for caregivers as well as personal changes to parenting styles as a result of this project. A central theme that emerged was the development of a “double” or “dual consciousness”—an ability to see humanity and injustice in their circumstances, fueling a desire for systemic change. Overall, this CBPR project amplifies the voices of marginalized youth and mothers, illuminating how the criminal legal system perpetuates cycles of trauma, stigma, and disempowerment. The implications call for a radical reimagining of the role of social work in creating more equitable, restorative, and healing-centered communities, including an immediate embrace of Abolitionist practice concepts and interventions. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article