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Person de-Identification: A Comprehensive Review of Methods, Datasets, Applications, and Ethical Aspects Along-With New Dimensions
Get PDFPerson de-identification has become a challenging problem that is receiving substantial attention because of the growing demand for privacy protection and related regulations. In this context, computer vision and Deep Learning (DL) algorithms offer automated solutions for Face de-identification (FDeID), commonly used to conceal personal identities in visual data. The existing survey studies addressing the FDeID topic lack comprehensive coverage of modern generative DL-based FDeID methods, limitations of data resources, proposing new applications, and potential technical and ethical research directions, which are covered for the first time in this survey. Throughout the manuscript, we offer critical analysis from various perspectives with a recurring theme of the growing impact that generative deep learning techniques are beginning to have on FDeID and related areas such as gait de-identification. In addition, we suggest 17 novel research dimensions and corresponding research questions in both technical and dataset perspectives, which will advance the research frontiers in this domain. The insights presented in this survey can benefit the research community and diverse stakeholders such as law enforcement, healthcare, industry, etc. It offers valuable insights into the performance analysis of existing methodologies, identifies research gaps, highlights application domains, and suggests precise possible avenues for future contributions
Modelling of a cargo oil pump turbine system to find the optimum pump operation capacities through the design of experiments approaches
Get PDFThis study aims to thoroughly understand and analyze the Cargo Oil Pump Turbine (COPT) system on tanker ships through thermodynamic and numerical simulations. The goal is to find the best pump capacities to match different cargo discharge rates and properties. A parametric study has been performed on the COPT system by building a thermodynamic model including exergy analysis, and a numerical centrifugal pump model. The design of experiment applications which are factorial design and response surface methodology (RSM) have determined the the optimal pump capacity The findings demonstrate that the risky flow rate region for the cavitation, if the density and vapour pressure are close to limits, is between 3050-3500 m3/h. The factorial design finds the best operating flow rate for the high capacity demands at 4025 m3/h where the efficiencies are the highest, and the RSM calculates the optimum capacity for the low demands at 1460.34 m3/h
The JWST UNCOVER Treasury Survey: Ultradeep NIRSpec and NIRCam Observations before the Epoch of Reionization
Get PDFIn this paper we describe the survey design for the Ultradeep NIRSpec and NIRCam Observations before the Epoch of Reionization (UNCOVER) Cycle 1 JWST Treasury program, which executed its early imaging component in 2022 November. The UNCOVER survey includes ultradeep (∼29–30AB) imaging of ∼45 arcmin2 on and around the well-studied A2744 galaxy cluster at z = 0.308 and will follow up ∼500 galaxies with extremely deep low-resolution spectroscopy with the NIRSpec/PRISM during the summer of 2023, with repeat visits in summer 2024. We describe the science goals, survey design, target selection, and planned data releases. We also present and characterize the depths of the first NIRCam imaging mosaic, highlighting previously unparalleled resolved and ultradeep 2–4 μm imaging of known objects in the field. The UNCOVER primary NIRCam mosaic spans 28.8 arcmin2 in seven filters (F115W, F150W, F200W, F277W, F356W, F410M, and F444W) and 16.8 arcmin2 in our NIRISS parallel (F115W, F150W, F200W, F356W, and F444W). To maximize early community use of the Treasury data set, we publicly release the full reduced mosaics of public JWST imaging including 45 arcmin2 NIRCam and 17 arcmin2 NIRISS mosaics on and around the A2744 cluster, including the Hubble Frontier Field primary and parallel footprints
Effect of COVID-19 Vaccines on Hair Loss
Get PDFAims: The COVID-19 pandemic has led to the global distribution of vaccines, but there are concerns regarding potential side effects. Hair loss is one of the less commonly reported side effects. The present study aimed to investigate the effect of COVID-19 vaccinations on hair loss. Instruments & Methods: A cross-sectional descriptive study was conducted with 580 participants aged between 20 to 72 years, consisting of 270 males and 310 females. Machine learning techniques were employed to analyze the data and determine any potential relationship between COVID-19 vaccines and hair loss. A logistic regression analysis was used to assess the odds ratio and 95% confidence interval for hair loss. Findings: Of the total participants, 17.6% reported experiencing hair loss after receiving the COVID-19 vaccine. This percentage was higher in females (19.4%) compared to the males (15.2%). There was a significant association between the COVID-19 vaccine and hair loss in both males and females. The odds ratio for developing hair loss after receiving the COVID-19 vaccine was 1.34 (95% CI: 1.04¬1.73) for females and 1.12 (95% CI: 0.81-1.54) for males. Conclusion: Hair loss is a rare but possible side effect of COVID-19 vaccination in both males and females, which its prevalence is higher in females than in males. Individuals with certain comorbidities, such as hypertension and diabetes, may be at a higher risk for experiencing hair loss after COVID-19 vaccination
Insider Trading and CEO Pay-Gap Induced Turnover
Get PDFWe explore how insider trading returns, disparities in executive pay, and CEO turnover are interrelated. Our findings reveal both independent and interactive effects for insider trading returns, the CEO pay gap, and the likelihood of CEO turnover. First, an increase in abnormal returns from insider purchases lowers the probability of a CEO’s turnover, while an increase in abnormal returns from insider sales increases the likelihood of a CEO’s dismissal. Second, the CEO pay gap negatively affects the probability of CEO turnover for insider purchases, but it does not have a similar effect on insider sales. Third, the interaction between insider abnormal returns and any CEO pay disparity influences the impact of these returns on CEO turnover. Specifically, this interaction diminishes the positive effect of insider selling on the probability of a CEO’s dismissal, offsets the negative effect of insider purchasing on CEO dismissal, and, finally, amplifies the negative impact of CEO pay disparity on the probability of a CEO’s dismissal during periods witnessing insider purchases
Achieving hybridisation between Miscanthus species: Commercially-scalable methods to manipulate flowering synchronisation and maximise seed yield
Get PDFMiscanthus is a high-yielding lignocellulosic perennial biomass crop. The low multiplication rate of clonal rhizome propagation is a bottleneck to upscaling plantation areas of feedstock needed to supply and expand the bioeconomy. Novel seeded Miscanthus hybrids are currently being developed to overcome this bottleneck by increasing annual multiplication rates from approximately 10 to over 1000 times. We describe a series of field experiments in southern Italy using agronomic methods to optimise multiplication rates through (i) planting configurations and densities, (ii) ratio of seed parents to pollen parents (iii) supplemental pre-dawn misting to increase humidity during pollination. In these trials the seed-bearing M. sinensis started flowering 2–3 weeks earlier than the M. sacchariflorus pollen parent. Earlier experiments indicated that flowering in M. sacchariflorus was mostly determined by photoperiod while in M. sinensis it was modulated by endogenous signals. Consequently, a second set of experiments were conducted to delay flowering time in M. sinensis: (iv) mid-season stem cut back, (v) oversupply of nitrogen, and (vi) undersupply of water. Across all treatments and years, the multiplication rates varied from 140 to 1300 seeds m². Reducing the proportion of the pollen parent plants (M. sacchariflorus) from 50 % to 25 % did not reduce seed yield per plant. This therefore increases the seed yield per m² and reduces seed production upscaling costs. Flowering time and duration in M. sinensis was significantly impacted by mid-season cutting and water stress, but not by nitrogen supply rates. Mid-season shoot cutting reduced number of flowers per plant (77 %), seed quantity (47 %), seed size (46 %), and resulted in seeds with a low germination rate of 39 %. High M. sinensis planting densities produced higher seed yields in the first year. However, in subsequent years higher density plots were more susceptible to autumn lodging lowering seed production by loss of panicles. Pre-dawn misting to prolong pollen life and stigma receptivity had no significant effects on seed production. This study demonstrates the importance of flowering time synchronization in the open field for commercial seed production. The limited effect of agronomic efforts to reduce the interspecies flowering time gap emphasises the importance of genetic factors in controlling flowering time. The most impactful intervention to change flowering time and improve parental synchronisation was mid-season cutting, while this method reduced seed production when applied to the seed parent it could be ideal for pollen parents
Isolation, cytotoxicity evaluation, and molecular docking of 3,4,3’-tri-O-methylflavellagic acid from Anogeissus leiocarpus (DC.) Guill. & Perr. root
Get PDFCancer kills about 10 million people every year. Medicinal plants remain a major source in the global search for anticancer drugs. In this study, 3,4,3’-tri-O-methylflavellagic acid (MFA) was isolated from the methanol root extract of Anogeissus leiocarpus. The structure was determined by 1D- and 2D-NMR data. The cytotoxic effects of MFA were evaluated against human breast (MCF-7), colorectal (Caco-2), and cervical (HeLa) cancer cell lines using the 3-[4,5-dimethylthiazole-2-yl] 3,5-diphenyltetrazolium bromide assay. A multi-protein target screening via molecular docking was conducted against ten cancer-related proteins, and ADMET properties were evaluated. MFA exhibited the most potent activity against Caco-2 (IC50: 46.75±13.00 µM). Molecular docking analysis showed that MFA had a strong binding affinity for the colchicine-binding site of αβ-tubulin and polo-like kinase-1 (binding energies: –8.5 and –8.4 kcal/mol, respectively). MFA also satisfied the Lipinski’s Rule of Five. MFA could, therefore, potentially serve as a scaffold for developing new anticancer molecules
Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction
Get PDFBackground: The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for insulin resistance, triglyceride-glucose index (TyG), has been linked to stroke prognosis. We aims to explore the relationships between TyG with ICU all-cause mortality and other prognosis, and to develop machine learning (ML) models in predicting ICU all-cause mortality in the first-ever strokes. Methods: We included first-ever stroke patients from the eICU Collaborative Research Database in 2014–2015 as the primary analysis cohort (then divided into training and internal validation cohorts) and from local hospital’s ICUs as the external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the association between TyG and ICU/hospital all-cause mortality. Linear regression and correlation analyses were performed to examine the relationships between TyG with length of ICU/hospital stay and Glasgow Coma Score. Results: The primary analysis cohort included 3173 first-ever strokes (median age 68.0 [55.0–68.0] years; 63.0% male), while the external validation cohort included 201 first-ever strokes (median age 71.0 [63.0–77.0] years; 62.3% male). Multivariate Cox proportional hazards models revealed that the high TyG group (TyG ≥ 9.265) was associated with higher ICU (HR 1.92, 95% CI 1.38–2.66) and hospital (HR 1.69, 95% CI 1.32–2.16) all-cause mortality, compared with low TyG group (TyG < 9.265). TyG was also correlated with ICU length of stay (r = 0.077), hospital length of stay (r = 0.042), and Glasgow Coma Score (r = -0.132). TyG and other six features were used to construct ML models. The random forest model performed best in internal validation with AUC (0.900) and G-mean (0.443), and in external validation with AUC (0.776) and G-mean (0.399). Conclusion: TyG (optimal cut-off: 9.265) was identified as an independent risk factor for ICU and hospital all-cause mortality in first-ever strokes. The ML model incorporating TyG demonstrated strong predictive performance. This emphasises the importance of insulin resistance (with TyG as a surrogate measure) in the prognostic assessment and early risk stratification of first-time stroke patients
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