A Planar Generator for a Wave Energy Converter

This article presents a permanent magnet planar translational generator which is able to exploit multiple modes of sea wave energy extraction. Linear electrical generators have recently been studied for the exploitation of sea wave energy, but, to the best of our knowledge, no synchronous planar translational generator has been proposed. In this article, to maximize the energy extraction, we have considered all the potential modes of motion due to wave excitation and included them within the mathematical model of the proposed system. The principle of operation of the generator can be summarized as follows: the moving part (translator) of the generator is driven from the sea waves and induces and electromotive force (EMF) on the windings mounted to the armature. The movement of the translator is 2-D and, therefore, all the movement modes of the wave, except heave, can be exploited. The proposed mathematical model includes the dynamic equations of the translator and the electric equations of the windings. The coupling parameters (inductances and fluxes) have been determined by finite element method analysis. Optimization of the device has been performed by considering both, the parameters of the electromagnetic circuit, and, the parameters associated with the stochastic features of the wave

SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting

Multivariate time series forecasting plays a critical role in diverse domains. While recent advancements in deep learning methods, especially Transformers, have shown promise, there remains a gap in addressing the significance of inter-series dependencies. This paper introduces SageFormer, a Series-aware Graph-enhanced Transformer model designed to effectively capture and model dependencies between series using graph structures. SageFormer tackles two key challenges: effectively representing diverse temporal patterns across series and mitigating redundant information among series. Importantly, the proposed series-aware framework seamlessly integrates with existing Transformer-based models, augmenting their ability to model inter-series dependencies. Through extensive experiments on real-world and synthetic datasets, we showcase the superior performance of SageFormer compared to previous state-of-the-art approaches

Developing and Evaluating the Internet of Things System for Room Management on Campus, a Usability Perspective.

This paper aimed to evaluate the existing room management system on School of Information and Library Science, UNC Chapel Hill, and suggested a new room management system that has all the features of the existing system and, based on Internet of Things technology, allows users to find the room by themselves with the help of the mobile app that interacts with Bluetooth beacons. The newly developed mobile app is then evaluated by usability inspection and usability test on a few test subjects. The test showed that new functions come with the mobile app is efficient in providing new features that are useful when booking and checking the room for more information. The author concluded that the new system could be helpful for users to manage room booking by themselves and offered further development suggestions to the system.Master of Science in Information Scienc

Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm

It is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basis for the indoor air quality system to control the PM2.5 concentration. One approach is to introduce the CatBoost algorithm based on rank lifting training into the classification and prediction of indoor PM2.5 concentration. Using actual monitoring data from office building, we consider previous indoor PM2.5 concentration, indoor temperature, relative humidity, CO2 concentration, and illumination as input variables, with the output indicating whether indoor PM2.5 concentration exceeds 25 μg/m3. Based on the CatBoost algorithm, we construct an intelligent classification prediction model for indoor PM2.5 concentration. The model is evaluated using actual data and compared with the multilayer perceptron (MLP), gradientboosting decision tree (GBDT), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN) models. The CatBoost algorithm demonstrates outstanding predictive performance, achieving an impressive area under the ROC curve (AUC) of 0.949 after hyperparameters optimition. Furthermore, when considering the five input variables, the feature importance is ranked as follows: previous indoor PM2.5 concentration, relative humidity, CO2, indoor temperature, and illuminance. Through verification, the prediction model based on CatBoost algorithm can accurately predict the indoor PM2.5 concentration level. The model can be used to predict whether the indoor concentration of PM2.5 exceeds the standard in advance and guide the air quality control system to regulate

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 955606 (DEEP-SEA) and No. 777778 (MATHROCKS). Furthermore, the research leading of this study has received funding from the Ministerio de Educación y Ciencia (Spain) under Project TED2021-131882B-C42.Peer ReviewedPostprint (published version

The Effects of Aroma on Relaxation During the Group Calligraphy Activity of Elderly People: Case Studies in China and Japan

Previous research has found that aromatherapy has positive effects on ability and mood. However, only a limited number of studies have focused on the effects of aroma on elderly people when they are engaged in group activities, although it has proven to be beneficial to the elderly. Consequently, we studied the effects of aroma on the relaxation of elderly participants of a group activity by conducting an experiment in which we used orange oil to provide an "aroma environment" and plain water to provide a "without-aroma environment." Our results suggest that citrus aromas have an effect on the physiological and psychological relaxation of elderly people when they are participating in group activities. Further studies are required focusing on the observation of different kinds of aromas and the sustainability of the same aroma.Art and Design Research for Sustainable Development ; September 22, 2018Conference: Tsukuba Global Science Week 2018Date: September 20-22, 2018Venue: Tsukuba International Congress Center Sponsored: University of Tsukub

CD24 Expression as a Marker for Predicting Clinical Outcome in Human Gliomas

CD24 is overexpressed in glioma cells in vitro and in vivo. However, the correlation of its expression with clinicopathological parameters of gliomas and its prognostic significance in this tumor remain largely unknown. To address this problem, 151 glioma specimens and 10 nonneoplastic brain tissues were collected. Quantitative real-time PCR, immunochemistry assay, and Western blot analysis were carried out to investigate the expression of CD24. As per the results, CD24 was overexpressed in gliomas. Its expression levels in glioma tissues with higher grade (P < 0.001) and lower KPS (P < 0.001) were significantly higher than those with lower grade and higher KPS, respectively. Cox multifactor analysis showed that CD24 (P = 0.02) was an independent prognosis factor for human glioma. Our data provides convincing evidence for the first time that the overexpression of CD24 at gene and protein levels is correlated with advanced clinicopathological parameters and poor prognosis in patients with glioma

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources