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Spatial Neglect: An Exploration of Clinical Assessment Behaviour in Stroke Rehabilitation
Get PDFObjective: There is a large gap between evidence-based recommendations for spatial neglect assessment and clinical practice in stroke rehabilitation. We aimed to describe factors that may contribute to this gap, clinician perceptions of an ideal assessment tool, and potential implementation strategies to change clinical practice in this area. Design: Qualitative focus group investigation. Focus group questions were mapped to the Theoretical Domains Framework and asked participants to describe their experiences and perceptions of spatial neglect assessment. Setting: Online stroke rehabilitation educational bootcamp. Participants: A sample of 23 occupational therapists, three physiotherapists, and one orthoptist that attended the bootcamp. Intervention: Prior to their focus group, participants watched an hour-long educational session about spatial neglect. Main measures: A deductive analysis with the Theoretical Domains Framework was used to describe perceived determinants of clinical spatial neglect assessment. An inductive thematic analysis was used to describe perceptions of an ideal assessment tool and practice-change strategies in this area. Results: Participants reported that their choice of spatial neglect assessment was influenced by a belief that it would positively impact the function of people with stroke. However, a lack of knowledge about spatial neglect assessment appeared to drive low clinical use of standardised functional assessments. Participants recommended open-source online education involving a multidisciplinary team, with live-skill practice for the implementation of spatial neglect assessment tools. Conclusions: Our results suggest that clinicians prefer functional assessments of spatial neglect, but multiple factors such as knowledge, training, and policy change are required to enable their translation to clinical practice
Consumers’ reaction to sci-fi as a source of information for technological development: An empirical analysis
No full textScience fiction (sci-fi) creative products inspire individuals by envisioning alternative futures and imaginary technological development. The stimuli conveyed by sci-fi creative products can trigger consumers' interest, which nowadays translate into reactions on social media platforms. Consequently, these reactions allow to understand the attitude of potential consumers towards not-yet-developed technologies and inform firms developing novel and unconventional technological solutions, hence reducing their market uncertainty. This study aims at exploring if the individuals' reactions to the technological themes presented through sci-fi creative products and conveyed through social media may become a source of information for innovating firms, that may consequently affect their technology development processes. To answer our research question, we relied on the individuals’ reactions to the sci-fi series Black Mirror conveyed through Twitter and on patents related to the technological themes presented in the series. Results show that in most cases there is a significant variation in social media engagement about the presented technology after the first airing of the episode and, in a subset of cases we found a significant variation in the mean number of related patents filed per day, that can be associated with an actual change in the technology development processes
A novel generative adversarial network‐based super‐resolution approach for face recognition
Get PDFFace recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check-in, and many others. The super-resolution of subject images is an important and challenging element in numerous scenarios. At times the images are low resolution and need to be processed through super-resolution techniques to gain more accurate results. For the problem of image super-resolution, deep learning-based face recognition systems have been explored in recent years; however, low-resolution face recognition remains an arduous task. Generative adversarial network (GAN) based models are a promising approach to address this challenge. However, conventional GAN-based models may generate images that differ significantly from an original high-resolution image in the test set to the point that the identity of the target face may be changed. To address this shortcoming, we propose a novel U-Net style generator architecture, where skip-connections between the encoder and decoder layer can help in preserving the facial characteristics of the input image in the generated image, thus curbing the generator's ability to generate an entirely new image and training it to generate an image more similar in characteristics to the original image. In addition to statistical metrics like structural similarity index measure and Fréchet inception distance, we compute the pixel-wise distance between the original and model-generated images to ascertain that our model generates as close to the original images as possible. While we train the model for 4× super-resolution (64 × 64 images to 256 × 256), our architecture can also be trained for an arbitrary resizing scale. Finally, the number of faces detected over high-resolution images generated by our model is shown to be higher than state-of-the-art high-resolution image creation models for face recognition tasks
Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT
Get PDFFederated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposed a dual-blockchain based multi-layer grouping federated learning architecture (BMFL). BMFL divides the participant groups based on the training tasks, then realizes the model training combining synchronous and asynchronous through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault dataset. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable
How to… support others in developing a career in clinical education research
Get PDFThe Incubator for Clinical Education Research (ClinEdR) is a UK-wide network, established with support from the National Institute for Health Research, to lead initiatives to build capacity in the field. A key barrier identified by this group is that many experienced educators, clinicians, and researchers, who may be committed to supporting others, have little guidance on how best to do this. In this “How to …” paper, we draw on relevant literature and our individual and collective experiences as members of the National Institute for Health Research ClinEdR incubator to offer suggestions on how educators can support others to develop successful careers involving ClinEdR. This article offers guidance and inspiration for all professionals whose role involves research and scholarship and who encounter aspiring or developing clinical education researchers in the course of their work. It will also be of interest to researchers who are starting out and progressing in the field
Effect of pulsating flow on flow-induced vibrations of circular and square cylinders in the laminar regime
Get PDFThrough fluid-structure interaction simulations, this study assesses the dynamic response characteristics of elastically mounted circular and square cylinders subjected to pulsating inflow conditions, providing valuable insights into the analysis and optimization of these systems. The main focus of the present work is on analyzing the effects of two factors: (i) the ratio of the oscillatory velocity component to the steady velocity component in pulsating flow (flow ratio) and (ii) the ratio of the oscillation frequency of pulsating flow to the natural frequency of the structure (frequency ratio). The simulation results for different parameters of interest are analysed using Fourier analysis and Poincaré maps of time series data, and contour plots of vorticity. For the circular cylinder, it is found that cylinder loses synchronization in lock-in as the flow and frequency ratios are increased. Three distinct vibration patterns of vortex-induced vibration are observed for selected combinations of flow and frequency ratios at a Reynolds number of 110 for circular cylinder. For the galloping of square cylinder at a Reynolds number of 250, it is found that the instability and nonlinearity of vortex shedding become more pronounced as the flow ratio increases
Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis
No full textSentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on deep neural networks have emerged as the dominant approach for sentiment analysis. While these models offer significant advantages, their lack of interpretability poses a major challenge in comprehending the rationale behind their reasoning and prediction processes, leading to complications in the models' explainability. Further, only limited research has been carried out into developing deep learning models that describe their internal functionality and behaviors. In this timely study, we carry out a first of its kind overview of key sentiment analysis techniques and eXplainable artificial intelligence (XAI) methodologies that are currently in use. Furthermore, we provide a comprehensive review of sentiment analysis explainability
Two-Level Dynamic Programming-Enabled Non-Metric Data Aggregation Technique for the Internet of Things
Get PDFThe Internet of Things (IoT) has become a transformative technological infrastructure, serving as a benchmark for automating and standardizing various activities across different domains to reduce human effort, especially in hazardous environments. In these networks, devices with embedded sensors capture valuable information about activities and report it to the nearest server. Although IoT networks are exceptionally useful in solving real-life problems, managing duplicate data values, often captured by neighboring devices, remains a challenging issue. Despite various methodologies reported in the literature to minimize the occurrence of duplicate data, it continues to be an open research problem. This paper presents a sophisticated data aggregation approach designed to minimize the ratio of duplicate data values in the refined set with the least possible information loss in IoT networks. First, at the device level, a local data aggregation process filters out outliers and duplicates data before transmission. Second, at the server level, a dynamic programming-based non-metric method identifies the longest common subsequence (LCS) among data from neighboring devices, which is then shared with the edge module. Simulation results confirm the approach’s exceptional performance in optimizing the bandwidth, energy consumption, and response time while maintaining high accuracy and precision, thus significantly reducing overall network congestion
Effect of Particle Size on the Thermal Conductivity of Organic Phase Change Materials with Expanded Graphite
Get PDFNumerous studies have been conducted to enhance the thermal conductivity of phase change materials (PCMs) using various techniques. Among them, adding high-conductive particles, such as expanded graphite (EG). Nevertheless, in the published literature, there is limited information about the impact of particle sizes on the thermophysical properties of the PCM. This work aims to investigate the impact of two particle sizes of EG (20 and 200 µm) on three commercial PCMs, RT62HC, RT64HC and OM65, using different characterisation techniques, including differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and thermal conductivity measurements. The results indicated that the thermal conductivity of PCMs increases by incorporating the high-conductive particles. The highest thermal conductivity enhancement (156%) was obtained for the composite PCM based on RT62HC with 6wt.% of 200 µm EG. It could be highlighted that the solid thermal conductivity results are better with particles of 20 µm in paraffin (RT64HC) and with particles of 200 µm in fatty acids (RT62HC and OM65)
Gendering Narcissism: Different Roots and Different Routes to Intimate Partner Violence
Get PDFResearch has only recently begun to explore narcissism in women using gender-inclusive assessments that move beyond traditional male-centric frameworks associated with grandiosity. Such work indicates gender differences in the onset and expression of narcissism, and risk factors of partner violence perpetration. The pathways to offending in narcissism may therefore be gendered but have yet to be tested. In this study, we investigated the mediating role of grandiose and vulnerable narcissism in the association between childhood exposure to maltreatment and later partner violence perpetration in adulthood, and the moderating role of gender in these associations. Participants (N = 328) completed scales of grandiose and vulnerable narcissism, perceived parenting styles, and physical/sexual and psychological abuse perpetration. Results indicated gender differences in grandiose (men higher) and vulnerable (women higher) narcissism. Retrospective reports of having mothers who were caring was negatively related to grandiose narcissism for men and vulnerable narcissism for women. Father overprotectiveness was positively related to grandiose narcissism in men. Self-reported vulnerable narcissism was related to greater perpetration of physical/sexual and psychological IPV in women, whereas grandiose narcissism was associated with greater perpetration of psychological IPV in men. For women, but not men, mother care was associated with reduced psychological IPV via lower vulnerable narcissism levels. These findings inform gendered risk markers of narcissism and perpetration of violence for intervention efforts
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