The quadratic spinor Lagrangian is equivalent to the teleparallel theory

The quadratic spinor Lagrangian is shown to be equivalent to the teleparallel / tetrad representation of Einstein's theory. An important consequence is that the energy-momentum density obtained from this quadratic spinor Lagrangian is essentially the same as the ``tensor'' proposed by Moller in 1961.Comment: 10 pages, RevTe

Solution and bulk properties of branched polyvinyl acetates IV--Melt viscosity

The melt viscosities of some randomly branched and some comb shaped branched polyvinyl acetate fractions were compared to the viscosities of linear polymer over a range of molecular weights. The melt viscosity of the branched polymer was usually higher than that of linear polymer of the same weight average molecular weight. The extent of this increase was related to the molecular weight of the branches but no correlation could be found which included the number of branches per molecule. This unusual behaviour is believed to be due to the fact that the length of the branches in the polymers of this study was above the critical chain length for polyvinyl acetate which made it possible for the branches to be engaged in intermolecular chain entanglements.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32168/1/0000223.pd

Charged Dilaton, Energy, Momentum and Angular-Momentum in Teleparallel Theory Equivalent to General Relativity

We apply the energy-momentum tensor to calculate energy, momentum and angular-momentum of two different tetrad fields. This tensor is coordinate independent of the gravitational field established in the Hamiltonian structure of the teleparallel equivalent of general relativity (TEGR). The spacetime of these tetrad fields is the charged dilaton. Our results show that the energy associated with one of these tetrad fields is consistent, while the other one does not show this consistency. Therefore, we use the regularized expression of the gravitational energy-momentum tensor of the TEGR. We investigate the energy within the external event horizon using the definition of the gravitational energy-momentum.Comment: 22 Pages Late

Effect of large graphene particle size on structure, optical property and photocatalytic activity of graphene-titanate nanotube composites

Available online 19 October 2021In this work we investigate the crystal transformation and optical properties of hydrothermal titania nanotube (TNT) when combining with large size of exfoliated graphene achieved by electrochemical process (EC-Gr). The TNT monoclinic structure has been changed to TiO2 anatase phase when TNT was grown in the presence of graphene dispersion. The effect of graphene on the evolution of TNT crystal could be understood by the interaction of carbon elements in graphene and Ti4+ ions in the titania structure. Due to the carrier separation which reduced recombination rate of excited photoelectrons and holes revealed by photoluminescence characterizations, the visible light photocatalytic activity in degradation of methylene blue in solution of the composite was enhanced. The photocatalytic enhancement was discussed and clarified based on UV–vis diffuse absorption spectra and time-resolved photoluminescence investigation.Vo Cao Minh, Phan Tan Dat, Pham Thi Thuy, Nguyen Xuan Sang, Nguyen Tri Tuan, Tran Thanh Tung, Dusan Losi

Fuzzy Guided Autonomous Nursing Robot through Wireless Beacon Network

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon’s signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot’s vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy controller achieves very high accuracy in obstacle detection and avoidance, with a very low computational time for taking directional decisions. Moreover, the experimental results demonstrated that the robotic system achieves superior accuracy in detecting/avoiding obstacles compared to other systems of similar purposes presented in the related works. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning

Precipitation nowcasting is one of the main tasks of weather forecasting that aims to predict rainfall events accurately, even in low-rainfall regions. It has been observed that few studies have been devoted to predicting future radar echo images in a reasonable time using the deep learning approach. In this paper, we propose a novel approach, RainPredRNN, which is the combination of the UNet segmentation model and the PredRNN_v2 deep learning model for precipitation nowcasting with weather radar echo images. By leveraging the abilities of the contracting-expansive path of the UNet model, the number of calculated operations of the RainPredRNN model is significantly reduced. This result consequently offers the benefit of reducing the processing time of the overall model while maintaining reasonable errors in the predicted images. In order to validate the proposed model, we performed experiments on real reflectivity fields collected from the Phadin weather radar station, located at Dien Bien province in Vietnam. Some credible quality metrics, such as the mean absolute error (MAE), the structural similarity index measure (SSIM), and the critical success index (CSI), were used for analyzing the performance of the model. It has been certified that the proposed model has produced improved performance, about 0.43, 0.95, and 0.94 of MAE, SSIM, and CSI, respectively, with only 30% of training time compared to the other methods. © 2022 by the authors. Licensee MDPI, Basel, Switzerland