21649 research outputs found
Sort by
Residential Relocations and Housing Changes Among Immigrants and Their Descendants: An Analysis of Longitudinal Register Data From France
Full text linkThis paper investigates residential mobility and housing changes among immigrants, their descendants, and the native population, alongside the association between family and household characteristics and residential mobility. We apply discrete‐time event history analysis to rich French administrative panel data covering the period 2012–2019. The results show distinct patterns of residential moves among migrant groups and generations. Immigrants from North Africa and Sub‐Saharan Africa are less likely to move to homeownership and more likely to move to social renting compared to the native French population. By contrast, immigrants from Southeast Asia, Turkey, and Europe have a similar likelihood of moving to homeownership as the native population. We find little differences in the probability of moving to homeownership across migrant generations; however, the second generation appears less residentially mobile than the immigrant generation. The descendants of immigrants from North Africa and Sub‐Saharan Africa are the least likely to move to homeownership and the most likely to move to social renting. This suggests that either structural barriers or cultural norms shape the mobility patterns of immigrants and their descendants in the same way. Finally, we observe similarities in the association between household characteristics and residential mobility for migrants, their descendants, and the native population. This suggests that life course events play a similar role in residential mobility across all population groups. For migrants and their descendants, those with low socioeconomic resources move less, suggesting that a lack of resources is a determinant of low mobility
SFTT: A Spatial-Frequency-Temporal-Based End-to-End Transformer for Heart Rate Estimation
Full text linkVision-based Heart Rate (HR) estimation in adverse situations, such as changes in skin tone, arbitrary face movements, and complex backgrounds, etc., is challenging. Unlike state-of-the-art models that use color and spatial-temporal information, the present work exploits a Spatial-Frequency-Temporal Transformer (SFTT) for heart rate estimation. For extracting multi-scale contextual features, we propose an end-to-end transformer that encodes contextual information through a pyramidal structure-based approach. Furthermore, to strengthen the features, the proposed model introduces a new attention approach that performs mutual-sharing operations between spatial-temporal and frequency-temporal domains in an end-to-end fashion. Experimental results on four standard datasets, namely UBFC-rPPG, VIPL-HR, OBF, and MMSE-HR, show that the proposed model is generic and invariant to the aforementioned challenges. Further, a comparative study with the state-of-the-art models demonstrates the effectiveness of the proposed method over the existing methods on all four benchmark datasets. Besides, experiments on cross-dataset validation show that the proposed method is reliable and robust
A Novel Ensemble Empirical Decomposition and Time–Frequency Analysis Approach for Vibroarthrographic Signal Processing
Full text linkSignal processing techniques play a critical role in addressing real-world applications across domains such as sensor analysis, defence, and clinical and biomedical fields. Within healthcare, computer-aided diagnostic (CAD) systems have become pivotal in supporting medical professionals with the interpretation of data and images, especially in medical imaging and radiological diagnostics. For diagnosing joint disorders, both time-domain and frequency-domain analyses are employed to examine complex, non-stationary, and nonlinear signals. To process Vibroarthrographic signals in this context, an initial step involves applying the Hilbert-Huang Transform, which comprises two stages: Empirical Mode Decomposition (EMD) for computing intrinsic mode functions (IMFs), followed by the Hilbert transform for further signal analysis. In our proposed approach, we utilized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Time-Varying Frequency Empirical Mode Decomposition (TVF-EMD) to compute IMFs, as well as Variation Mode Decomposition to calculate mode signals. Subsequent feature extraction incorporates both time and frequency characteristics, focusing on metrics such as pixel intensity, mean, and standard deviation. These features then serve as inputs to machine learning models for classification tasks, distinguishing between healthy and non-healthy signal samples. In our model, we employed a Least Squares Support Vector Machine (LS-SVM) and a Support Vector Machine with Recursive Feature Elimination (SVM-RFE) to enhance classification accuracy. This sequence of signal processing and machine learning steps demonstrates a structured and effective approach for CAD-based diagnosis in joint disorder assessments
A Novel Long Short-Term Complementary Memory Network Based on Temporal Distribution Matching for Soft Sensing in Industrial Processes
Full text linkWith the development of industrial intelligence, deep learning-based soft sensors are widely used in complex industrial systems to estimate key quality variables in real-time, with long short-term memory (LSTM) networks excelling in nonlinear dynamic modeling. However, conventional LSTM networks face the challenge of redundant memory weight allocation, which hinders efficient capture of critical temporal dependencies. Moreover, the probability distribution of industrial process variables often exhibits dynamic temporal shifts, significantly deteriorating model generalization performance. In this study, a novel long short-term complementary memory network based on temporal distribution matching (TDM-LSTCM) is proposed for product quality prediction in industrial processes. First, a novel memory update rate gate is designed based on LSTM to obtain adaptive update rate factors for different time steps, regulating the information flow rate. Second, the memory update rate gate is embedded into the input and forget gates, leading to the development of a complementary input-forget regulation gate structure to optimize the cell state update mechanism. Furthermore, the TDM algorithm is incorporated into the training process through a dual-stage optimization strategy. During the distribution characterization stage, the training dataset is partitioned into distinct periods with the most pronounced distributional discrepancies. In the distribution matching stage, cross-period discrepancies in hidden states are minimized to facilitate the extraction of common information. Finally, the proposed methodology is validated through a penicillin fermentation process and flue gas desulfurization system. Experimental results demonstrate that the developed approach outperforms other modeling methods, showcasing its promising application potential
The Importance of Personhood: Using Corpus Linguistics and Critical Discourse Analysis to Explore Narratives of Autism
Full text linkAlthough the understanding of autism has greatly improved since its discovery in the 1940’s, relatively little attention has been paid to the diversity within the autistic community. For example, journalists often use stereotypes to represent autistic people in the British press, presenting these individuals through a heteronormative and Caucasian lens. Because of this limited representation, the experiences and narratives of autistic individuals who do not fit into these stereotypes are often ignored in the mainstream media and the British press. In this study I have used corpus linguistics and critical discourse analysis to these diverse and underrepresented narratives, comparing the language used in the British press with the language autistic writers use to represent their own experiences on UK charity and blog websites. This included a sample of three hundred and forty-nine newspaper articles and three hundred and fifty self-written narratives, sourced to create the Autism Newspaper Corpus and the Autism Narrative Corpus. Through this analysis, I found that there was a significant lack of diversity and personhood in newspaper articles about autism in the British press, directly juxtaposing the more emotive and individualistic language used by autistic writers. Journalists commonly reinforced stereotypes in these newspaper articles, failing to acknowledge the intersectionality between autism and other factors of identity like gender, sexuality and race. These findings reinforce the view that the representation of autism in the British press and the mainstream media still requires work as the intersectionality between autism and other factors of identity like race, gender and sexuality is frequently ignored by journalists and media producers/writers
Using natural language processing to explore differences in healthcare professionals’ language on Functional Neurological Disorder: a comparative topic and sentiment analysis study
Full text linkBackground: Effective communication is essential for delivering quality healthcare, particularly for individuals with Functional Neurological Disorders (FND), who are often subject to misdiagnosis and stigmatising language that implies symptom fabrication. Variability in communication styles among healthcare professionals may contribute to these challenges, affecting patient understanding and care outcomes. Methods: This study employed natural language processing (NLP) to analyse clinician-to-clinician and clinician-to-patient communication regarding FND. A total of 869 electronic health records (EHRs) were examined to assess differences in language use and emotional tone across various professionals—specifically, neurologists and psychologists—and different document types, such as discharge summaries and letters to general practitioners (GPs). Latent Dirichlet Allocation (LDA) topic modelling and two complementary sentiment models (VADER and Flair) were applied to the corpus. Sentiment analysis was also applied to evaluate the emotional tone of communications. Results: Findings revealed distinct communication patterns between neurologists and psychologists. Psychologists frequently used terms related to subjective experiences, such as “trauma” and “awareness,” aiming to help patients understand their diagnosis. In contrast, neurologists focused on medicalised narratives, emphasising symptoms like “seizures” and clinical interventions, including assessment (“telemetry”) and treatment (“medication”). Sentiment analysis indicated that psychologists tended to use more positive and proactive language, whereas neurologists generally adopted a neutral or cautious tone. Conclusions: These findings highlight differences in communication styles and emotional tones among professionals involved in FND care. The study underscores the importance of fostering integrated, multidisciplinary care pathways and developing standardised guidelines for clinical terminology in FND to improve communication and patient outcomes. Future research should explore how these communication patterns influence patient experiences and treatment adherence
Exploring co-production and its impact on staff and patients lived experience in developing a therapeutic environment within a forensic setting using developmental object relations approach
Full text linkForensic settings play a fundamental role in providing care and treatment for patients with complex psychiatric and psychological needs. This research has focused on early developmental processes to explore and develop an understanding of how the co-production of the components of care provision affects the lived experiences of staff and patients.The specific research questions were:•How can co-production be implemented within a forensic ward to enhance the therapeutic environment?•Can a psychoanalytic object-relations approach be applied to improve understanding of the barriers and enablers of co-production and its impact on the lived experiences of patients and staff?The concept and practices of co-production were viewed through the lens of developmental object-relations to critically examine the parallel processes between early developmental psychological experiences and the capacity for co-production. Psychoanalytical concepts (social defences) should be considered to improve understanding of unconscious processes. In this research, an ethnographic approach was used that combined psychoanalytical theory and methods to examine the impact of co-production within this clinical setting.Co-production in mental healthcare is the principle and practice of working in partnership with patients to design and deliver all components of care provision. Recently, increased attention paid to co-production has influenced policy makers and statutory regulators, and created discourse in clinical practice as to what it is and is not, its associated benefits, and how it can be achieved in settings in which there are inherent power differentials. Power exists in all relationships; this research has addressed issues of intersectionality, power, identity and relational dynamics, and their impact on co-production. This study illustrates how tensions among these issues play out to understand how co-production approaches can be implemented.Few studies have explored both staff and patients’ lived experiences of co-production in forensic inpatient mental health settings. This research has addressed this knowledge gap and contributes knowledge that has implications and leads to recommendations for future practice.
Functional data analysis of joint coordination during the pull of the power clean
Full text linkThe purpose of this study was to use a bivariate functional principal component analysis (bfPCA) to quantify hip-knee joint movement patterns and assess their relationship with power clean performance. Thirty strength-power athletes completed a one repetition maximum (1RM) power clean test where hip and knee joint angle data from the heaviest successful lift were recorded and analysed using bfPCA. Three principal components were extracted, primarily reflecting variability in hip joint angle throughout the pull (pattern 1), knee joint angle at the starting position (pattern 2), hip-knee joint motion from the middle of the first pull through to the end of the second pull (pattern 3). Correlation analyses revealed no significant or meaningful correlations between power clean performance and patterns 1 or 2 (r = −0.10 and 0.04, p = 0.60 and 0.85, respectively), suggesting that inter-individual differences in starting position may not negatively impact power clean performance. However, pattern 3 was weakly but significantly correlated with power clean performance (r = 0.39, p = 0.03), with higher-performing lifters displaying movement patterns characterised by a more upright torso at the power position and a more controlled, prolonged first pull. These findings suggest that coaches focus on these aspects of the movement to potentially maximise power clean performance
Hope enhances treatment outcome of intensive trauma-focused treatment for PTSD
Full text linkBackground: Hope has been found to positively influence trauma-focused treatment outcomes and is associated with post-traumatic growth (PTG), which contributes to improved treatment efficacy.Objective: This observational study examined the extent to which hope predicts a decline in post-traumatic stress disorder (PTSD) symptoms and whether an increase in the level of hope predicts changes in PTSD symptoms. This study also investigated whether PTG mediates the relationship between hope and PTSD symptoms.Method: The sample included 339 participants (82.9% female) who were diagnosed with PTSD and underwent an intensive eight-day trauma-focused treatment programme consisting of eight sessions of prolonged exposure, eight sessions of EMDR therapy, physical activity, and psychoeducation. Assessments were performed pre-, mid-, and post-treatment using the PCL-5, HHI and PTGI. Linear Mixed and mediation models were used.Results: Hope significantly increased (Cohen’s d = 0.47 at mid-treatment and post-treatment), and PTSD symptoms significantly decreased (Cohen’s d = 1.72 at mid-treatment and 2.04 at post-treatment) during treatment. Both hope levels at the start of treatment and subsequent changes in hope during treatment significantly predicted a decline in PTSD symptoms (p < .01 and p < .001) and vice versa (p < .001). Pre-treatment PTG mediated the relationship between pre-treatment hope and mid-treatment PTSD symptoms but did not mediate the relationship between pre-treatment hope and post-treatment PTSD symptoms.Conclusions: These outcomes emphasise the critical importance of hope in PTSD treatment, highlighting its potential to bolster mental well-being and enhance the overall quality of life. More research is needed to gain more insight into the exact mechanisms underlying the interactions between hope, PTG and PTSD symptoms during treatment
Continual Relation Extraction with Wake-Sleep Memory Consolidation
Full text linkContinual Relation Extraction (CRE) has achieved significant success due to its ability to adapt to new relations without frequent retraining. However, existing methods still face challenges such as overfitting and representation bias. Inspired by the wake-sleep memory consolidation process of the human brain, this paper proposes a Wake-Sleep Memory Consolidation (WSMC) framework to address these issues systematically. During the wake phase, the model simulates the brain’s information processing mechanism, quickly encoding new relations and storing them in short-term memory. We also introduce the Experience Iterative Learning (EIL) approach, which dynamically adjusts the distribution of relation samples. This approach corrects the model’s representation bias and enhances memory stability through experience replay. During the sleep phase, the model consolidates existing knowledge by replaying long-term memory. Moreover, the framework generates diverse dream data from existing memory sets, thereby increasing the diversity of the training data and improving the model’s generalization capability. Experimental results show that WSMC significantly outperforms other CRE baseline methods on FewRel and TACRED datasets, demonstrating its superior performance compared to baseline methods. Our source code is available at https://github.com/Gyanis9/WSMC.gi
Data sources:
Access Repository Dashboard
Do you manage University of Salford Institutional Repository? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!