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Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19
Get PDFThe phases of a crisis are critical to understanding its evolution. We construct an economic agent-determined machine learning-based Google search index that associates search terms with uncertainty to isolate COVID-19-related uncertainty from overall uncertainty. Subsequently, we apply directional wavelet analysis that discriminates between positive and negative associations to study the evolving impact of the COVID-19 pandemic on financial market uncertainty and financial markets. Our approach permits us to delineate crisis phases with high precision according to information type. The analysis that follows suggests that policy responses impacted uncertainty and that the novelty of the COVID-19 outbreak had a significant impact on global stock markets. Regression analysis, wavelet entropy and partial wavelet coherence confirm the informational content of our uncertainty index. The approach presented in this study is applied to the COVID-19 crisis but is generalisable beyond the pandemic and can assist in decision-making during times of economic and financial market turmoil and should be of interest to policymakers, researchers and econometricians
The Adoption of Artificial Intelligence in Family Law - Brand New or Well-known Idea?
No full textOn 30 November 2022, sensing artificial intelligence's (AI) capacities have become at people's fingertips more than ever. The public release of ChatGPT, based on the GPT-3.5 engine, was a pinnacle in the long-standing discussion about AI. In a short time, the media was flooded with news heralding the technological breakthrough that would revolutionise every occupation. The improved GPT-4.0 engine, released in March 2023, fitted the narrative, as the new version achieved much better results than its predecessor. The envisaged ubiquitous automatization of work, supported by generative AI, will also affect family lawyers despite many claims that seasoned attorneys, furnished with complex legal knowledge and human compassion, could never be replaced by machines. Regardless of the defensive tone, AI in family law practice and the family justice system has become a fact. Flashy industry news created an image of AI as a brand-new concept, although the first AI-based solutions were introduced in family law in the early 1990s. Many family lawyers are unaware that providing legal aid or representing clients is almost impossible without coming across automated processes, collectively defined as AI. Are we then witnessing sluggish progress, and the information about the breakthrough is intentionally distorted by blatant marketing? In the article, I will attempt to assess AI's development pace in family law by examining the existing and envisaged models of its adoption
Effects of perceived organisational politics and effort–reward imbalance on work outcomes – the moderating role of mindfulness
No full textAbstractPurposeThe purpose of this study is to examine the moderating effects of mindfulness on the relationships between work stressors (perceived organisational politics [POP] and effort–reward imbalance [ERI]) and work outcomes (job burnout [JBO] and job satisfaction [JS]).Design/methodology/approachTime-lagged data were collected from public sector employees in France and Pakistan. The final samples (France, N = 204; Pakistan, N = 217) were tested using multiple moderating regression.FindingsMindfulness moderates the relationship between work stressors and work outcomes. Mindfulness serves as a personal resource for employees: it mitigates the negative influence that POP and ERI have on JBO and JS.Originality/valueThis study extends current knowledge on the relationships between work stressors and work outcomes across cultures by testing mindfulness as a valuable personal resource
ICT Systems Security and Privacy Protection: 39th IFIP International Conference, SEC 2024, Edinburgh, UK, June 12–14, 2024, Proceedings
No full textThis book constitutes the proceedings of the 39th IFIP International Conference on ICT Systems Security and Privacy Protection, SEC 2024, held in Edinburgh, UK, during June 12–14, 2024.The 34 full papers presented were carefully reviewed and selected from 112 submissions. The conference focused on current and future IT Security and Privacy Challenges and also was a part of a series of well-established international conferences on Security and Privacy
Attitudes and Practices of Women Towards Cervical Cancer Screening in Lesotho: A Descriptive Cross-Sectional Survey
Get PDFIn 2020, Lesotho reported 541 new cases of cervical cancer and 362 women died. This study aimed to assess the attitudes and practices of women towards cervical cancer screening. A quantitative descriptive cross-sectional design was used to collect data from 289 participants who were selected using convenience sampling from 27 health facilities. Permission to conduct the study was obtained from the National University of Lesotho and the Ministry of Health (ID43-2022). The written informed consent was sought from the participants who took part voluntarily. Data were analyzed using the SPSS (Statistical Packages for Social Sciences) version (26). Respondents aged 30 to 34 years (94.0%) and above 35 years (95.9%) had positive attitudes towards cervical cancer screening. Fifty-one percent of the respondents had done cervical cancer screening. Respondents who had two (65.5%) and four to eight children (52.4%) and were employed (64.0%) had cervical cancer screening done before. Most of the respondents strongly agreed that cervical cancer screening detected cervical changes before they became cancerous (55%) and if found early, they are easily curable (56.7%), and made women know if they were healthy (58.8%). Healthcare professionals should conduct health education on cervical cancer and screening on a daily basis in health facilities to improve the uptake of cervical cancer screening
DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement
No full textVision-guided Autonomous Underwater Vehicles (AUVs) have gradually become significant tools for human exploration of the ocean. However, distorted images severely limit the visual ability, making it difficult to meet the needs of complex underwater environment perception. Fortunately, recent advancements in deep learning have led to rapid developments in underwater image enhancement. The emergence of the Transformer architecture has further enhanced the capabilities of deep learning. However, the direct application of Transformer to underwater image enhancement presents challenges in computing pixel-level global information and extracting local features. In this paper, we present a novel approach that merges dimension decomposition Transformer with semi-supervised learning for underwater image enhancement. To begin, dimension decomposition attention is proposed, which enables Transformer to compute global dependencies directly at the original scale and correct color distortions effectively. Concurrently, we employ convolutional neural networks to compensate for Transformer's limitations in extracting local features, thereby enriching details and textures. Subsequently, a multi-stage Transformer strategy is introduced to divide the network into high- and low-resolution stages for multi-scale global information extraction. It helps correct color distortions while enhancing the network's focus on regions with severe degradation. Moreover, we design a semi-supervised learning framework to reduce the reliance on paired datasets and construct a corresponding multi-scale fusion discriminator to enhance the sensitivity to input data. Experimental results demonstrate that our method outperforms state-of-the-art approaches, showcasing excellent learning and generalization capabilities on subjective perception and overall evaluation metrics. Furthermore, outstanding results highlight the significant improvements it brings to downstream visual engineering applications. The code of the proposed DDformer is available at https://github.com/ZhiGao-hfuu/DDforme
Critical analysis of the consensus document on current advances in risk assessment, prevention, and treatment of skin tears. A response from ISTAP
Get PDFHow do academic smart city centres operate in complex environments? A business model perspective
Get PDFIn recent years, an increasing number of academic smart city centres have emerged globally. These centres play a crucial role in conducting cutting-edge research and proposing innovative solutions for cities; they often operate in a complex multi-purpose, multi-disciplinary, and multi-stakeholder environment, even as they struggle to secure enough resources for their core activities. Despite their important role in smart city projects and initiatives, we lack scientific understandings of how smart city centres generate and deliver value to city stakeholders. As a first step in addressing this gap, we studied seven smart city centres across Europe and North America through in-depth interviews. To complement those interviews, we conducted an online survey and collected responses from a larger sample of centres from several regions around the world. Consequently, we provide a fresh understanding of how these centres orchestrate their operations and capabilities to create and deliver value to smart city stakeholders. Inspired by recent research on business model innovation for non-profit organizations, we argue that academic centres can benefit from using business models to align their operations with their strategies to achieve their goals. Our study contributes to smart city and business model research while providing practical implications for smart city centres
Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis
Get PDFBACKGROUND: Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA.MATERIAL AND METHODS: The study retrospectively investigated 161 medical records of CA patients admitted to the Intensive Care Unit (ICU). The random forest classifier algorithm was used to assess the parameters of mortality. The best classification trees were chosen from a set of 100 trees proposed by the algorithm. Conditional mortality odds were investigated with the use of logistic regression models featuring interactions between variables.RESULTS: In the logistic regression model, male sex was associated with 5.68-fold higher mortality odds. The mortality odds among the asystole/pulseless electrical activity (PEA) patients were modulated by body mass index (BMI) and among ventricular fibrillation/pulseless ventricular tachycardia (VF/pVT) patients were by serum albumin concentration (decrease by 2.85-fold with 1 g/dl increase). Procalcitonin (PCT) concentration, age, high-sensitivity C-reactive protein (hsCRP), albumin, and potassium were the most influential parameters for mortality prediction with the use of the random forest classifier. Nutritional status-associated parameters (serum albumin concentration, BMI, and Nutritional Risk Score 2002 [NRS-2002]) may be useful in predicting mortality in patients with CA, especially in patients with PCT >0.17 ng/ml, as showed by the decision tree chosen from the random forest classifier based on goodness of fit (AUC score).CONCLUSIONS: Mortality in patients following CA is modulated by many co-existing factors. The conclusions refer to sets of conditions rather than universal truths. For individual factors, the 5 most important classifiers of mortality (in descending order of importance) were PCT, age, hsCRP, albumin, and potassium
Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition
Get PDFCurrent synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks
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