Local Behavior of the First-Order Gradient Correction to the Thomas-Fermi Kinetic Energy Functional

The first order gradient correction to the Thomas-Fermi functional, proposed by Haq, Chattaraj and Deb (Chem. Phys. Lett. vol. 81, 8031, 1984) has been studied by evaluating both the total kinetic energy and the local kinetic energy density. For testing the kinetic energy density we evaluate its deviation from an exact result through a quality factor, a parameter that reflects the quality of the functionals in a better way than their relative errors. The study is performed on two different systems: light atoms (up to Z=18) and a noninteracting model of fermions confined in a Coulombic-type potential. It is found than this approximation gives very low relative errors and a better local behavior than any of the usual generalized gradient approximation semilocal kinetic density functionals.Comment: 7 pages, 2 tables, 4 figure

Operational thresholds of moored ships at the oil terminal of inner port of A Coruña (Spain)

[Abstract] Minimizing the stay of a vessel in port can lead to improvements in harbor efficiency. Currently, downtimes of cargo operations or their performance reduction because of excessive vessel motion are especially relevant. This work aims to evaluate the operational conditions of moored vessels in the inner port of A Coruña (Spain), comparing them with motion thresholds established by international standards for cargo operations. To this end, motions of 19 ships were monitored. Data analysis revealed large angular motions, particularly roll and yaw, including amplitudes that exceeded the limiting criteria of the standards in most of the analyzed vessels, with no registered downtime. Regarding linear movements, heave and surge recorded lower amplitudes compared to the values of standard thresholds. The specific behavior of each vessel was analyzed in terms of its size, maritime conditions, and mooring location. Field campaigns such as those performed in this work are an effective way of analyzing the operational conditions of ports, which could help in identifying problems in the mooring zone.Ministerio de Economía, Industria y Competitividad; BIA2017-86738-

Development of an Automatic Low-Cost Air Quality Control System: A Radon Application

[Abstract] Air pollution is the fourth-largest overall risk factor for human health worldwide. Ambient air pollution (outdoors) and household air pollution (indoors) cause about 6.5 million premature deaths. The World Health Organization has established that between 3% and 14% of lung cancer cases are due to radon gas, making it the most important cause of lung cancer after smoking. This work presents a fully automated, low-cost indoor air quality control system that can monitor temperature, pressure, humidity, total volatile organic compounds (TOVC), and radon concentration. Using the radon concentration as an air quality measure, we created a prediction algorithm. The system uses those predictions to control a ventilation system automatically. We tested the algorithm for different prediction windows and compared the results with those without the ventilation system in a radon research room. In this room, the radon concentration is high 100% of the time, reaching a level eleven times higher than the recommended limit. The results show that the system can achieve an 86% reduction of the radon concentration, maintaining it low 90% of the time while having the ventilation system on during only 34% of the time. This work demonstrates that we can control air quality using low-cost resources, keeping a household or workplace safe but comfortable.This work was supported by Spanish Ministry of Economy and Competitiveness through the project BIA2017-86738-R and through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. This work is supported in part by grants from the European Social Fund 2014–2020. CITIC (Research Centre of the Galician University System) and the Galician University System (SUG) obtained funds through Regional Development Fund (ERDF), with 80% from the Operational Program ERDF Galicia 2014–2020 and the remaining 20% from the Secretaría Xeral de Universidades of the Galician University System (SUG) (Ref ED431G 2019/01). Additional support was provided by the Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49)Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2018/4

Application of an Analytic Methodology to Estimate the Movements of Moored Vessels Based on Forecast Data

[Abstract]: A port’s operating capacity and the economic performance of its concessions are intimately related to the quality of its operational conditions. This paper presents an analytical methodology for estimating the movements of a moored vessel based on field measurements and forecast data, specifically including ship dimensions and meteorological and maritime conditions. The methodology was tested and validated in the Outer Port of Punta Langosteira, A Coruña, Spain. It was determined that the significant wave height outside the port, and the ratio of the vessel’s length divided by its beam (L/B), are the variables that most influence movements. Furthermore, heave and surge are the movements with a better value of the coefficient of determination (R2 values of 0.71 and 0.67, respectively), the sway (R2 = 0.30) and roll (R2 = 0.27) being the worst when using the available forecast variables of the Outer Port of Punta Langosteira. Despite their low R2 values, sway and roll models are able to estimate the main trends of these movements. The obtained estimators provide good predictions with assumable error values (root mean square error—RMSE and mean absolute error—MAE), showing their potential application as a predictive tool. Finally, as a consequence, the A Coruña Port Authority has included the results of the methodology in its port management system allowing them to predict moored vessel behavior in the port.The authors gratefully acknowledge the financial support of the Spanish Ministry of Economy, Industry and Competitiveness, R & D National Plan, within the project BIA2017-86738-R. In addition, special thanks to the Port Authority of A Coruña, Spain

Modeling of energy efficiency for residential buildings using artificial neuronal networks

The energy efficiency dataset used to support the findings of this study has been deposited in the GitHub repository https://github.com/mereshow/ann-energy-efficiency.git.[Abstract] Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U-opaque value of a building with a correlation coefficient of 0.967 with the real U-opaque measured value for the same building

Deep Learning-Based Wave Overtopping Prediction

[Abstract]: This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected were merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port’s activities.This research was funded by the Spanish Ministry of Science and Innovation [grant number PID2020-112794RB-I00, funded by MCIN/AEI/10.13039/501100011033]. The authors would like to thank the Port Authority of A Coruña (Spain) for their availability, collaboration, interest and promotion of research in port engineering

Kinetic Energy Density Study of Some Representative Semilocal Kinetic Energy Functionals

There is a number of explicit kinetic energy density functionals for non-interacting electron systems that are obtained in terms of the electron density and its derivatives. These semilocal functionals have been widely used in the literature. In this work we present a comparative study of the kinetic energy density of these semilocal functionals, stressing the importance of the local behavior to assess the quality of the functionals. We propose a quality factor that measures the local differences between the usual orbital-based kinetic energy density distributions and the approximated ones, allowing to ensure if the good results obtained for the total kinetic energies with these semilocal functionals are due to their correct local performance or to error cancellations. We have also included contributions coming from the laplacian of the electron density to work with an infinite set of kinetic energy densities. For all the functionals but one we have found that their success in the evaluation of the total kinetic energy are due to global error cancellations, whereas the local behavior of their kinetic energy density becomes worse than that corresponding to the Thomas-Fermi functional.Comment: 12 pages, 3 figure

Machine Learning Based Moored Ship Movement Prediction

[Abstract] Several port authorities are involved in the R+D+i projects for developing port management decision-making tools. We recorded the movements of 46 ships in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2015 until 2020. Using this data, we created neural networks and gradient boosting models that predict the six degrees of freedom of a moored vessel from ocean-meteorological data and ship characteristics. The best models achieve, for the surge, sway, heave, roll, pitch and yaw movements, a 0.99, 0.99, 0.95, 0.99, 0.98 and 0.98 R2 in training and have a 0.10 m, 0.11 m, 0.09 m, 0.9°, 0.11° and 0.15° RMSE in testing, all below 10% of the corresponding movement range. Using these models with forecast data for the weather conditions and sea state and the ship characteristics and berthing location, we can predict the ship movements several days in advance. These results are good enough to reliably compare the models’ predictions with the limiting motion criteria for safe working conditions of ship (un) loading operations, helping us decide the best location for operation and when to stop operations more precisely, thus minimizing the economic impact of cargo ships unable to operate.This research was funded by the Spanish Ministry of Economy, Industry, and Competitiveness, R&D National Plan, within the project BIA2017-86738-R, the FPI predoctoral grant from the Spanish Ministry of Science, Innovation, and Universities (PRE2018-083777) and the Spanish Ministry of Science and Innovation, Retos Call, within the project PID2020-112794RB-I00