Real-Time Pricing Strategy Based on the Stability of Smart Grid for Green Internet of Things

The ever increasing demand of energy efficiency and the strong awareness of environment have led to the enhanced interests in green Internet of things (IoTs). How to efficiently deliver power, especially, with the smart grid based on the stability of network becomes a challenge for green IoTs. Therefore, in this paper we present a novel real-time pricing strategy based on the network stability in the green IoTs enabled smart grid. Firstly, the outage is analyzed by considering the imbalance of power supply and demand as well as the load uncertainty. Secondly, the problem of power supply with multiple-retailers is formulated as a Stackelberg game, where the optimal price can be obtained with the maximal profit for retailers and users. Thirdly, the stability of price is analyzed under the constraints. In addition, simulation results show the efficiency of the proposed strategy

Creative Geometry Games

The geometry game presented in this article was inspired by Bright and Harvey\u27s (1988) Learning and fun with geometry games. In their article, Bright and Harvey (1988) propose three interactive games: Polyhedron Rummy, Polygon Rummy and That are Stretching it. Polygon Rummy is a game pertaining to plane geometry where the instructional objective is to construct a figure using lines and angles, whereas the goal of polyhedron rummy is to construct a solid using faces and angles. Although Bright and Harvey focused on constructing shapes, they also suggested presenting pre-constructed shapes to students, allowing them to classify and identify them (Bright & Harvey, 1988). Our activity does just this

Using Hands-on Experiences including Various Forms of ARTS to Increase Ninth-Grade Female Students’ Interest in STEM – Final Results

Due to the ever-growing number of employment opportunities in the science technology engineering and math fields increasing emphasis is placed on STEM education at all levels This paper reports on research focused on increasing STEM interest and awareness in ninth- grade minority female students primarily from a low socioeconomic background Thirty-seven female students participated in this research study They attended fifteen hour-long sessions of STEM-infused hands-on workshops presented by university professors in the College of Education and the College of Arts and Sciences The workshops took place during school hours adding to their regular curriculum The school does not currently have an arts program although the founder of the school is a music artist Therefore the workshops incorporated the arts as the central theme while the lessons were delivered in biology chemistry engineering and mathematics The study is motivated by the need to close the achievement gap and improve the representation of women and minorities in STEM fields Data were collected through surveys assessments and interviews and analyzed using quantitative and qualitative methods The significance of the study lies in the potential to inspire underserved females to enter the STEM field

Effects of ion motion on linear Landau damping

The effects of ion motion on Landau damping has been studied by the use of one-dimensional Vlasov-Poisson simulation. It is shown that the ion motion may significantly change the development of the linear Landau damping. When the ion mass is multiple of proton mass, its motion will halt the linear Landau damping at some time due to the excitation of ion acoustic waves. The latter will dominate the system evolution at the later stage and hold a considerable fraction of the total energy in the system. With very small ion mass, such as in electron-positron plasma, the ion motion can suppress the linear Landau damping very quickly. When the initial field amplitude is relatively high such as with the density perturbation amplitude δn/n0 > 0.1, the effect of ion motion on Landau damping is found to be weak or even ignorable

Incidence and recurrence of acute otitis media in Taiwan's pediatric population

OBJECTIVE: To report the incidence and recurrence of acute otitis media (AOM) in Taiwan's pediatric population. METHODS: Information from children (aged <= 12 years) with a diagnosis of AOM was retrieved from the 2006 National Healthcare Insurance claims database. We calculated the cumulative incidence rate and the incidence density rate of recurrent AOM within one year after the initial diagnosis in 2006. We used a multivariate logistic regression model to assess the predictors for recurrence of AOM. RESULTS: The annual incidence rate of AOM was estimated to be 64.5 cases per 1,000 children. The overall one-year cumulative incidence rate of recurrence was 33.1%, and the incidence density rate was 33.5 cases per 100 personyears, with the highest figure (41.2 cases per 100 person-years) noted for children aged 0-2 years. Recurrence was significantly associated with age, gender, place of treatment, and physician specialty. CONCLUSION: AOM remains a major threat to children's health in Taiwan. Male children and very young children require more aggressive preventive strategies to reduce the risk of recurrence

Finland: An Exemplary STEM Educational System

There is a need for an increase in the number of students entering fields of science, technology, engineering, and mathematics (STEM) and the only way for that to happen is for educational reforms to be put into place (PCAST, 2012). Improvement and focus on STEM education are a concern of all nations whether they have an emerging economy or one that is long established. The world of the 21st century is such that in order to compete globally countries must invest in STEM education (Kennedy & Odell, 2014). The United States scores on the Program for International Student Assessment (PISA) were not in the top ten for reading, mathematics, nor science. To rectify this, it is imperative that changes be made to the educational system (Schleicher, ed., 2012). Looking at countries that are consistently at the top is one way to find potential solutions and models of reform. One country that has successfully reformed their educational system is Finland. Within their educational system, the strategies of collaboration and communication are widely utilized by the instructors as well as the students (Sislian, Gabardo, Macedo, & Ribeiro, 2015). While analyzing a single country’s instructional program can give insights into what makes it successful, it is beneficial to compare that country to others that are also achieving success in order to determine any trends and commonalities. The countries used for this comparison were chosen because they were different culturally, geographically, and politically, but in spite of their differences, they were among the top-scoring nations on the PISA

Improving Synthetic to Realistic Semantic Segmentation with Parallel Generative Ensembles for Autonomous Urban Driving

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different up-sampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods

Strategy to Estimate Size

Have you ever wondered exactly how much land and space are included in a national park? In this paper, we will explore the vastness of a fictitious Park and calculate its total area. We will: 1. Create a fictitious Park, determine a scale that can be used to perform calculations using a practical unit of measurement. 2. Develop a strategy that students can use to find the area of the Park. 3. Demonstrate the use of the strategy by solving for the area of the fictitious park

Bayesian Inference Federated Learning for Heart Rate Prediction

The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.Peer reviewe