An Analysis of Disability in The Little Mermaid: Examining Disparities and Similarities in the Fairytale and Its Movie Adaptation

Differences and disabilities have always been a part of oral tradition and folklore. These differences greatly influenced story-telling that eventually stemmed from oral tradition. The western canon has included disability in their literature for some time now, but their portrayal of disability from the beginning to the twentieth century has drastically improved. Fairytales and folktales were historically associated with values and morals, and the moral system during the olden times was completely patriarchal and abelistic. The tales never offered any space for disability to exist as a simple part of an individual's life. This paper aims to investigate the representation of disability in Disney's version of Hans Christian Andersen's fairytale, "The Little Mermaid", and attempts to understand disability in the light of fairytales. There are some major and vital differences between the original story by Hans Christian and the movie by Disney, but do they accommodate positive signals that counteract the ableist society? Does the movie reflect the truth about a disabled person's life? Or is it still a profusion of negative elements that reinforce oppression and discrimination? This paper examines the narratives employed in the movie and the original fairy tale and attempts to address the issues of identity, stigma, and stereotypes based on the representation of disability in both genres

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

Accelerating the Discovery of g-C3_3N4_4-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy

Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy and bandgap analysis demonstrate that the single atoms of B, Mn and Co anchored on g-C$_3$N$_4$ can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption (${\Delta}$GH ) for the test set with a lower mean absolute error (MAE) and a high coefficient of determination (R$^2$) of 0.45 and 0.81, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valance electron as key descriptors. Overall, the multistep work-flow employed through DFT calculations combined with ML models for efficient screening of potential hydrogen evolution reaction (HER) from g-C$_3$N$_4$-based single atom catalysis can significantly contribute to the catalyst design and fabrication.Comment: 10 pages, 4 figure

Effects of Stepwise Denervation of the Stellate Ganglion: Novel Insights from an Acute Canine Study

Background The stellate ganglion (SG) is important for cardiac autonomic control. SG modification is an option for treating refractory ventricular tachyarrhythmias. The optimal extent of left- and right-sided SG denervation necessary for antiarrhythmic effect, however, remains to be learned. Objective The purpose of this study was to evaluate the effects of stepwise SG denervation on hemodynamic and electrophysiological parameters in dogs. Methods After sequential left and right thoracotomy in 8 healthy dogs, the SG was exposed by dissection. Two pacing wires were placed in the upper SG to deliver high-frequency stimulation. The lower SG, ansae subclaviae, and upper SG were removed in a stepwise manner. The same protocol was performed on the right side. Blood pressure (BP), heart rate, and electrophysiological parameters were recorded at baseline and after 5 minutes of stimulation. Results Systolic and diastolic BP significantly increased during stimulation of the upper left SG. The mean increase in systolic BP from baseline was 49.4 ± 26.6 mm Hg (P = .007), 25.5 ± 14.1 mm Hg after the lower SG was removed (P = .02), and 8.6 ± 3.4 mm Hg after resection of the ipsilateral ansae subclaviae (P = .048). Heart rate and other electrophysiological parameters did not change significantly. After the complete removal of the left SG, systolic BP increased by 34.0 ± 17.6 mm Hg (P = .005) after stimulation of the right SG. Conclusion Sympathetic output remains after the lower SG is removed, and sympathetic output from the right SG remains after the complete resection of the left SG and ansae subclaviae. Thus, some patients who undergo left SG denervation can still have significant sympathetic response via right SG regulation

Stability and change in the mental health of New Zealand secondary school students 2007–2012: Results from the national adolescent health surveys

Objective: To describe the self-reported mental health of New Zealand secondary school students in 2012 and to investigate changes between 2007 and 2012. Methods: Nationally representative health and wellbeing surveys of students were completed in 2007 (n=9107) and 2012 (n=8500). Logistic regressions were used to examine the associations between mental health and changes over time. Prevalence data and adjusted odds ratios are presented. Results: In 2012, approximately three-quarters (76.2%, 95% CI 74.8–77.5) of students reported good overall wellbeing. By contrast (also in 2012), some students reported self-harming (24.0%, 95% CI 22.7–25.4), depressive symptoms (12.8%, 95% CI 11.6–13.9), 2 weeks of low mood (31%, 95% CI 29.7–32.5), suicidal ideation (15.7%, 95% 14.5–17.0), and suicide attempts (4.5%, 95% CI 3.8–5.2). Between 2007 and 2012, there appeared to be slight increases in the proportions of students reporting an episode of low mood (OR 1.14, 95% CI 1.06–1.23, p=0.0009), depressive symptoms (OR 1.16, 95% CI 1.03–1.30, p=0.011), and using the Strengths and Difficulties Questionnaire - emotional symptoms (OR 1.38, 95% CI 1.23–1.54, p<0.0001), hyperactivity (OR 1.16, 95% CI 1.05–1.29, p=0.0051), and peer problems (OR 1.27, 95% CI 1.09–1.49, p=0.0022). The proportion of students aged 16 years or older reporting self-harm increased slightly between surveys, but there was little change for students aged 15 years or less (OR 1.29, 95% CI 1.15–1.44 and OR 1.10, 95% 0.98–1.23, respectively, p=0.0078). There were no changes in reported suicidal ideation and suicide attempts between 2007 and 2012. However, there has been an improvement in self-reported conduct problems since 2007 (OR 0.78, 95% CI 0.70–0.87, p<0.0001). Conclusions: The findings suggest a slight decline in aspects of self-reported mental health amongst New Zealand secondary school students between 2007 and 2012. There is a need for ongoing monitoring and for evidence-based, accessible interventions that prevent mental ill health and promote psychological wellbeing

Purification, characterization and utilization of polysaccharide of Araucaria heterophylla gum for the synthesis of curcumin loaded nanocarrier

In this study, gum of Araucaria heterophylla was collected. The collected gum was subjected for extraction of polysaccharide using solvent extraction system. Thus, extracted polysaccharide was further purified using solvent method and was characterized using UV-Vis spectroscopy, Phenol sulfuric acid assay, FTIR, TGA, TLC and GC-MS. The gum derived polysaccharide was found to have the following sugars Rhamnose, Allose, Glucosinolate, Threose, Idosan, Galactose and Arabinose. The extracted polysaccharide was tested for various in-vitro bioactive studies such as antibacterial activity, antioxidant activity and anticancer activity. The polysaccharide was found to have antioxidant and anticancer activity. Further, the polysaccharide was subjected for carboxymethylation to favor the nanocarrier synthesis, where it was chelated using Sodium Tri Meta Phosphate (STMP) to form nanocarriers. The nanocarriers so formed were loaded with curcumin and were characterized using FTIR, SEM, EDX and AFM. Both the loaded and unloaded nanocarriers were studied for its in-vitro cytotoxic effect against the MCF7 human breast cancer cell lines. The nanocarriers were found to deliver the drug efficiently against the cancer cell line used in this study

Contribution of infection and vaccination to population-level seroprevalence through two COVID waves in Tamil Nadu, India.

This study employs repeated, large panels of serological surveys to document rapid and substantial waning of SARS-CoV-2 antibodies at the population level and to calculate the extent to which infection and vaccination separately contribute to seroprevalence estimates. Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling ≥ 20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India's first COVID wave. Seroprevalence fell to 22.9% in round 2 (April 2021), a roughly one-third decline in 6 months, consistent with dramatic waning of SARS-Cov-2 antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), with infections from the Delta-variant induced second COVID wave accounting for 74% of the increase. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), with vaccinations accounting for 63% of the increase. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas

Unhealthy Gambling Amongst New Zealand Secondary School Students: An Exploration of Risk and Protective Factors

This study sought to determine the prevalence of gambling and unhealthy gambling behaviour and describe risk and protective factors associated with these behaviours amongst a nationally representative sample of New Zealand secondary school students (n = 8,500). Factor analysis and item response theory were used to develop a model to provide a measure of ‘unhealthy gambling’. Logistic regressions and multiple logistic regression models were used to investigate associations between unhealthy gambling behaviour and selected outcomes. Approximately one-quarter (24.2 %) of students had gambled in the last year, and 4.8 % had two or more indicators of unhealthy gambling. Multivariate analyses found that unhealthy gambling was associated with four main factors: more accepting attitudes towards gambling (pp = 0.0061); being worried about and/or trying to cut down on gambling (p p = 0.0009). Unhealthy gambling is a significant health issue for young people in New Zealand. Ethnic and social inequalities were apparent and these disparities need to be addressed