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Indirect Excitons and Many-Body Interactions in InGaAs Double Quantum Wells
No full textSpatially indirect excitons in semiconductor quantum wells are relevant to basic research and device applications because they exhibit enhanced tunability, delocalized wave functions, and potentially longer lifetimes relative to direct excitons. Here we investigate the properties of indirect excitons and their coupling interactions with direct excitons in asymmetric InGaAs double quantum wells using optical multidimensional coherent spectroscopy and photoluminescence excitation spectroscopy. Analyses of the spectra confirm a strong influence of many-body effects and reveal that excited-state zero-quantum coherences between direct and indirect excitons in the quantum wells dephase faster than the much-higher-energy single-quantum coherences between excitonic excited states and ground states. The results also suggest an important energy-dependent role of continuum states in mediating system dynamics, and they indicate that dephasing mechanisms are associated with uncorrelated or anticorrelated energy-level fluctuations
Navigating Multiple Virtual Teams: How Variety in Communication Rules Affects Knowledge Sharing
No full textIn contemporary workplaces, individuals are often members of more than one virtual team at a time, that is, they experience multiple virtual team membership (MVTM), and they are subjected to context variety due to different rules across their teams. The aim of this paper is to understand the relationship between context variety related to communication rules and knowledge sharing in situations of MVTM. We propose that context variety and switching between teams negatively affect the individual capability to acquire and provide knowledge resources in a team, due in part to an increased perception of role overload. Through an experimental study, we confirm that context variety directly and negatively affected individuals’ ability to acquire resources and, through role overload, negatively influenced the ability to provide resources. Contrary to our hypotheses, switching frequently between teams reduced role overload, which, in turn, increased the ability to provide resources. Our results have theoretical implications for understanding the changing nature of work in increasingly virtual and complex team contexts
Molecular Fluctuations Inhibit Intermittency in Compressible Turbulence
No full textIn the standard picture of fully developed turbulence, highly intermittent hydrodynamic fields are nonlinearly coupled across scales, where local energy cascades from large scales into dissipative vortices and large density gradients. Microscopically, however, constituent fluid molecules are in constant thermal (Brownian) motion, but the role of molecular fluctuations in large-scale turbulence is largely unknown, and with rare exceptions, it has historically been considered irrelevant at scales larger than the molecular mean free path. Recent theoretical and computational investigations have shown that molecular fluctuations can impact energy cascade at Kolmogorov length scales. Here, we show that molecular fluctuations not only modify energy spectrum at wavelengths larger than the Kolmogorov length in compressible turbulence, but also significantly inhibit spatio-temporal intermittency across the entire dissipation range. Using large-scale direct numerical simulations of computational fluctuating hydrodynamics, we demonstrate that the extreme intermittency characteristic of turbulence models is replaced by nearly Gaussian statistics in the dissipation range. These results demonstrate that the compressible Navier–Stokes equations should be augmented with molecular fluctuations to accurately predict turbulence statistics across the dissipation range. Our findings have significant consequences for turbulence modelling in applications such as astrophysics, reactive flows and hypersonic aerodynamics, where dissipation-range turbulence is approximated by closure models
The Effectiveness of Kolmogorov–Arnold Networks in the Healthcare Domain
No full textKolmogorov–Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional Artificial Neural Networks (ANNs), offering superior accuracy and interpretability, which are two critical requirements in healthcare applications. This study investigates the effectiveness of KANs across a range of clinical tasks by applying them to diverse medical datasets, including structured clinical data and time-series physiological signals. Compared with conventional ANNs, KANs demonstrate significantly improved performance, achieving higher predictive accuracy even with smaller network architectures. Beyond performance gains, KANs offer a unique advantage: the ability to extract symbolic expressions from learned functions, enabling transparent, human-interpretable models—a key factor in clinical decision-making. Through comprehensive experiments and symbolic analysis, our results reveal that KANs not only outperform ANNs in modeling complex healthcare data but also provide interpretable insights that can support personalized medicine and early diagnosis. There is nothing specific about the datasets or the methods employed, so the findings are broadly applicable and position KANs as a compelling architecture for the future of AI in healthcare
Scoping Review of Sexual and Gender Minority Health Research in Ireland
No full textAim: To map existing sexual and gender minority (SGM) health research in Ireland, identify gaps in literature and outline priorities for future research and healthcare. SGM is an umbrella term that includes people who identify as lesbian, gay, bisexual, transgender, queer or intersex and is sometimes abbreviated as LGBTQI+. Design: A scoping review of peer-reviewed studies published between 2014 and 2024. Methods: The review followed Joanna Briggs Institute (JBI) guidelines and PRISMA-ScR framework for scoping reviews. Articles were identified through systematic database searches and screened independently by reviewers. Data Sources: PubMed, PsycINFO, CINAHL and Embase were searched for articles published between January 2014 and April 2024. Sixty studies met inclusion criteria. Results: The review highlighted a disproportionate focus on gay, bisexual and other men who have sex with men (gbMSM), particularly regarding HIV and sexual health. Mental health research revealed high levels of anxiety, depression and suicidality, largely attributed to minority stress and systemic discrimination. Transgender health studies documented barriers to accessing gender-affirming care and mental health services. Few studies explored experiences of sexual minority women, older SGM individuals or intersex people. Intersectional perspectives on race, disability and socio-economic status were notably absent. Conclusion: SGM health research in Ireland reflects significant progress in documenting disparities in mental and sexual health. However, there is a lack of representation for some groups. There is also limited attention to intersectionality. Systematic gaps in sexual orientation and gender identity (SOGI) data impede targeted policymaking and service delivery. Implications for the Profession and/or Patient Care: Findings underscore the need for inclusive, culturally competent healthcare services, better integration of SGM health topics into nursing education, and community-centred interventions. Addressing structural barriers and improving provider competence can enhance equitable healthcare access for SGM populations. Impact: This review addresses the fragmented state of SGM health research in Ireland, highlighting gaps in representation and systemic issues. No Patient or Public Contribution: Authorship includes individuals from various sexual and gender minority communities
Grounded in Experience: A Collaborative Approach to Resource Development for Adults with Ehlers-Danlos Syndrome
No full textBackground: Ehlers-Danlos syndrome (EDS) is a complex connective tissue disorder that significantly affects daily occupations, participation, and quality of life, yet early support for individuals pursuing or newly receiving a diagnosis remains limited. Occupational therapy (OT) is uniquely positioned to address these challenges through interventions that promote self-management and functional engagement.
Purpose: This project aimed to develop an experience- and evidence-informed educational resource for individuals undergoing or newly receiving an EDS diagnosis, while increasing awareness of OT’s role in managing this chronic, multisystemic condition.
Methods: A mixed-methods approach integrated qualitative insights from interviews, online support groups, and practitioner feedback. A draft resource and accompanying flyer were evaluated via a Qualtrics, with quantitative data summarized descriptively, and qualitative responses thematically analyzed.
Results: Nine participants completed the survey. Respondents rated the resource as clear, relevant, and aligned with lived experience. Key sections, including Tools and Adaptive Equipment, Knowledge and Self-Advocacy, and Building Your Medical Portfolio, were identified as most helpful, and clinicians found the resource useful and feasible to implement, noting value in practical strategies and OT-focused guidance.
Conclusions: Findings support the value of client-informed, occupation-centered resources in enhancing early self-management and advocacy for individuals with EDS. The resource promotes engagement and strengthens interdisciplinary care, underscoring OT’s essential contribution to chronic condition management
Bridging Gaps with Occupational Therapy: The Unique Role of Occupational Therapy in Women’s Day Shelters
No full textHomelessness is a multifaceted, complex issue that we face as a society. Mainstream services provided to homeless or vulnerable populations tend to focus on housing and basic services. While these are essential and meaningful, literature suggests there is a lack in trauma and self-care support (Marzana et al., 2023; Milaney et al., 2020). Looking through an Occupational Therapy (OT) lens, there are a multitude of ways the profession can provide holistic care. Current OT service provision within this population most commonly focuses on obtaining and maintaining housing and addressing the survival occupations these individuals may be engaging in (Cunningham & Slade, 2019; Thomas et al., 2011). However, the scope of occupational therapy is expansive and many services that are absent within shelters may be addressed through occupation-based services. Additionally, there appears to be a persistent lack of understanding regarding the unique position of OT to provide trauma- and client-centered care to homeless or vulnerable individuals.
Women experiencing homelessness or vulnerability are an underserved subpopulation and require specialized care suited to their needs. The presented findings highlight the unique role of OT in providing gendered and trauma-informed care to support the overall health and well-being of women who frequent shelters. The purpose of this project was to provide a women’s day shelter in Pasadena, CA with a framework for the development of an Occupational Therapy position for homeless or vulnerable women to receive gendered and trauma-informed services and resources for the betterment of their health and wellbeing
Multimodal Deception Detection via Audio-Text Fusion with Deep Learning and ASR
No full textDetecting deception remains a critical challenge across multiple domains, from security screening and forensic investigations to hiring processes and fraud prevention. Traditional methods like polygraph testing suffer from invasiveness, subjectivity, and limited accuracy. This research explores how modern deep learning can address these limitations by analyzing both acoustic and linguistic cues in human speech. We developed a multimodal system that combines audio and text analysis to detect deception in the DOLOS dataset, which contains 1,675 video clips from real high-stakes scenarios. Our approach processes audio through a systematic pipeline that isolates voice, removes silence, and reduces noise before extracting features using Wav2Vec2, a state-of-the-art speech model. This careful preprocessing alone improved detection accuracy by 2.01 F1 points, reaching 75.5% F1 for audio-only classification. For text analysis, we used OpenAI’s Whisper to automatically transcribe speech, then applied BERT combined with bidirectional LSTM networks to detect linguistic deception patterns. Despite transcription errors (12.8% word error rate), the text classifier achieved 65.0% F1, demonstrating that key linguistic markers of deception—such as pronoun usage, hedging, and negation patterns—survive the transcription process. When we combined both modalities through feature-level fusion, the system achieved 74.2% F1 and 64.7% AUC-ROC, outperforming the previous best published result by 3.01 F1 points. Importantly, our evaluation used strong baseline models rather than artificially weakened comparisons, providing an honest assessment of multimodal fusion benefits. The results show that while audio dominates for short utterances, text provides complementary information that meaningfully improvesdetection when both are available. This work demonstrates that practical deception detection systems can work with automatically transcribed speech rather than requiring manual transcriptions, opening pathways for real-world deployment in security and investigative contexts. Keywords: Deception detection, multimodal fusion, automatic speech recognition, Wav2Vec2, BERT, audio preprocessing, deep learnin
Enhancing Communication Features In Yioop
No full textModern communication applications offer a variety of features to improve user experience. Interacting with other users keeps the users engaged with the application, so most modern applications offer some sort of messaging or communication features. Because of this, most users now have a mental model of the basic features that they expect from any communication system. Yioop, an open-source web platform, currently offers basic chat functionality but lacks some of these, and this project aims at bridging the gap between what is offered versus expected. This project augments Yioop’s chat system by integrating features such as audio messages, speech-to-text summary, direct translation and summary of messages and rich text styling using markdowns. These features improve accessibility and make the user experience better. The project was divided into two phases: The first part focused on understanding the coding structure and standards and getting familiar with the User Interface (UI) of the platform. This culminated into the main phase, which was around AI-enhanced features like text translation, transcription, and summarization. We also added helper scripts to populate test data into Yioop so that it can be used as a reference by anyone who wants to independently benchmark the work done in this project
Epigenetic Insights into TDP-43: Uncovering Mechanisms Behind Resilience to Alzheimer\u27s Diagnosis Regardless of Aβ and Tau buildup
No full textAlzheimer’s disease (AD), a neurodegenerative disease generally known to be one of the causes of dementia, continues to be the subject of research. TDP-43, a DNA-binding protein, was linked to other neurodegenerative diseases, yet its role in AD prognosis remains unknown. Recent studies have shown that 30% - 40% of cognitively normal (CN) individuals display the hallmarks of AD (Aβ and tau buildup) but are not diagnosed with the disease. This indicates the possible existence of a protective mechanism to counter the effects of Aβ and tau buildup. The work reported here utilized the ADNI study dataset and the Programming language Python to analyze the significance of TDP-43 and aimed to link TDP-43 expression to AD prognosis. AV45 PET scans of 138 CN subjects were analyzed to determine Aβ buildup and location. Gene expression data for AD, CN, and MCI subjects from the ADNI were analyzed using statistical hypothesis testing to determine if TDP-43 gene expression played a role in AD diagnosis. No statistical significance of TDP-43 was found between the three studied groups. These findings fail to link TDP-43 gene expression to AD prognosis due to limited and incomplete data; further research is necessary to determine if TDP-43 plays a significant role in AD. All data were obtained from the ADNI website (https://adni.loni.usc.edu/)