College Tuition and Income Inequality

This paper evaluates the role of rising income inequality in explaining observed growth in college tuition. We develop a competitive model of the college market, in which college quality depends on instructional expenditure and the average ability of admitted students. An innovative feature of our model is that it allows for a continuous distribution of college quality. We find that observed increases in US income inequality can explain more than half of the observed rise in average net tuition since 1990 and that rising income inequality has also depressed college attendance. (JEL D31, I22, I23, I24)

DEVELOPING A COMPETITIVE MODEL FOR HEALTH AND WELL-BEING TOURISM DESTINATIONS IN THAILAND: CONFIRMATORY FACTOR ANALYSIS APPROACH

This study assesses the consistency of the structural components of a model for developing a competitive health and well-being destination as viewed by health and well-being tourism entrepreneurs in an emerging Thai market. The sample consisted of 216 health and well-being tourism entrepreneurs recruited by purposive sampling. A questionnaire formatted using a five-point Likert scale was used. The questionnaire’s Index of Item-Objective Congruence (IOC) varied from 0.60 to 1.00, and its reliability ranged from 0.711 to 0.938. Statistical analysis, frequencies, percentages, means, standard deviations, exploratory factor analysis and confirmatory factor analysis were utilised. The findings revealed the following seven model components: 1) health and well-being tourism resources and attractions, 2) infrastructure and facilities, 3) service design and development, 4) policy, planning and destination management, 5) knowledge management and learning organisation, 6) destination management and 7) innovative capacity. Governors, entrepreneurs, destination managers and stakeholders can use the discovered variables to evaluate a competitive health and well-being destination’s expected performance, strengths, weaknesses and development opportunities. Further, this research should enable continuing support through the critical variable factors.

Prediction of ADHD diagnosis using brief, low-cost, clinical measures: a competitive model evaluation

Proper diagnosis of ADHD is costly, requiring in-depth evaluation via interview, multi-informant and observational assessment, and scrutiny of possible other conditions. The increasing availability of data may allow the development of machine-learning algorithms capable of accurate diagnostic predictions using low-cost measures. We report on the performance of multiple classification methods used to predict a clinician-consensus ADHD diagnosis. Classification methods ranged from fairly simple (e.g., logistic regression) to more complex (e.g., random forest), and also included a multi-stage Bayesian approach. All methods were evaluated in two large (N>1000), independent cohorts. The multi-stage Bayesian classifier provides an intuitive approach that is consistent with clinical workflows, and is able to predict ADHD diagnosis with high accuracy (>86%)—though not significantly better than other commonly used classifiers, including logistic regression. Results suggest that data from parent and teacher surveys is sufficient for high-confidence classifications in the vast majority of cases using relatively straightforward methods.

Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models

Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.

Deep Learning Based Fast Screening Approach on Ultrasound Images for Thyroid Nodules Diagnosis

Thyroid nodules are widespread in the United States and the rest of the world, with a prevalence ranging from 19 to 68%. The problem with nodules is whether they are malignant or benign. Ultrasonography is currently recommended as the initial modality for evaluating thyroid nodules. However, obtaining a good diagnosis from ultrasound imaging depends entirely on the radiologists levels of experience and other circumstances. There is a tremendous demand for automated and more reliable methods to screen ultrasound images more efficiently. This research proposes an efficient and quick detection deep learning approach for thyroid nodules. An open and publicly available dataset, Thyroid Digital Image Database (TDID), is used to determine the robustness of the suggested method. Each image is formatted into a pyramid tile-based data structure, which the proposed VGG-16 model evaluates to provide segmentation results for nodular detection. The proposed method adopts a top-down approach to hierarchically integrate high- and low-level features to distinguish nodules of varied sizes by employing fuse features effectively. The results demonstrated that the proposed method outperformed the U-Net model, achieving an accuracy of 99%, and was two times faster than the competitive model.

Successful microbial colonization of space in a more dispersed manner

Many organisms live in habitats with limited nutrients or space, competition for these resources is ubiquitous. Although spatial factors related to the population’s manner of colonizing space influences its success in spatial competition, what these factors are and to what extent they influence the outcome remains underexplored. Here, we applied a simulated competitive model to explore the spatial factors affecting outcomes of competition for space. By quantifying spatial factors, we show that colonizing space in a more dispersed manner contributes to microbial competitive success. We also find that the competitive edge deriving from a more dispersed manner in colonization can compensate for the disadvantage arising from either a lower growth rate or lower initial abundance. These findings shed light on the role of space colonization manners on maintaining biodiversity within ecosystems and provide novel insights critical for understanding how competition for space drives evolutionary innovation.

Chinese-Funded Electricity Generation in Sub-Saharan Africa and Implications for Public Debt and Transition to Renewable Energy

While China has been increasingly contributing to the recent growth in electricity generation in sub-Saharan Africa (SSA), the effects of China-funded investment on host countries’ debt burden and transition to renewable energy sources have not been sufficiently explored. Drawing on secondary data, combined with deep dive studies of Ethiopia and Uganda, this paper shows that despite significant liberalisation of the power sector in SSA, Chinese investments in the electricity industry continue to follow state-led project contract-based models. We show that this approach has failed to encourage Chinese firms to build compelling investment portfolios for competitive procurements within the region and, instead and inadvertently, it has exacerbated the debt burden of host country governments. Second, in spite of the global drive towards climate resilient energy generation, Chinese funding of electricity generation in SSA is not sufficiently channelled towards modern renewable energy sources such as wind and solar power that could reduce vulnerability to climate change. While recognising that the private sector-led competitive model of power generation is not without limitations, we argue that SSA’s electricity generation strategy that leads to less public debt and more climate resilience involves increased involvement of Chinese investment in the competitive model, with more diversification of such investment portfolios towards modern renewables such as wind and solar energy resources.

An Updated Technical–Tactical Categorisation in Taekwondo: From General Tactical Objectives to Combat Situations

Taekwondo competition underwent enormous development with the recent introduction of electronic scoring devices and rule changes. Although the competitive model of taekwondo had been previously studied, most of the literature that analyses this model was previous to the introduction of electronic devices or not based on a prior system of categories. Not only are results of an up-to-date taxonomy essential to guarantee the sustainability of future research about taekwondo based on methodological observation, but they are also completely necessary. This article proposes and validates a new categorisation of taekwondoist technical–tactical actions in the competition after the modifications were introduced between Beijing 2008 and Tokyo 2021 qualification events. The association between environmental conditions, tactical objectives, and technical actions determines the defining parameters of the combat situations. To design the category system, a dual methodology was used: in the first stage, an in-depth review of the technical and scientific literature based on observation of combats was carried out. From that review, a synthesis document was produced, which subsequently was used as a basis for canvassing an up-to-date view of the question from expert advisers. The existing terminology and categories were rearranged and updated, establishing new parameters involved in the technical–tactical resources of contest situations. This updated categorisation was tested by using the generalisability theory, revealing excellent-to-perfect observers’ agreement and reliable data. This new categorisation will allow designing precise and sustainable tools over time for methodological observation of taekwondo in future studies.