Surfaces of Revolution with Constant Gaussian Curvature in Four-Space

In this paper, we show that the constant property of the Gaussian curvature of surfaces of revolution in both $\mathbb R^4$ and $\mathbb R_1^4$ depend only on the radius of rotation. We then give necessary and sufficient conditions for the Gaussian curvature of the general rotational surfaces whose meridians lie in two dimensional planes in $\mathbb R^4$ to be constant, and define the parametrization of the meridians when both the Gaussian curvature is constant and the rates of rotation are equal.Comment: 8 paper

MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Previous studies have investigated several machine and deep learning models for electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, conventional feature extractions derived from ECG signals, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete PQRST segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN, for ECG classification tasks. Our extensive experimental results on the PhysioNet Apnea-ECG dataset revealed that with the new feature extraction method, we achieved a per-segment accuracy up to 92.11 \% and a per-recording accuracy of 100\%. Moreover, it yielded the highest correlation compared to state-of-the-art methods, with a correlation coefficient of 0.989. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH

A Late Eocene- Oligocene through-flowing river between the Upper Yangtze and South China Sea

We test the hypothesis of a major Paleogene river draining the SE Tibetan Plateau and the central modern Yangtze Basin that then flowed South to the South China Sea. We test this model using U Pb dated detrital zircon grains preserved in Paleogene sedimentary rocks in northern Vietnam and SW China. We applied a series of statistical tests to compare the U-Pb age spectra of the rocks in order to highlight differences and similarities between them and with potential source bedrocks. Monte Carlo mixing models imply that erosion was dominantly derived from the Indochina and Songpan-Garzê Blocks and to a lesser extent the Yangtze Craton. Some of the zircon populations indicate local erosion and sedimentation, but others show close similarity both within northern Vietnam, as well as more widely in the Eocene Jianchuan, Paleocene-Oligocene Simao and Oligocene-Miocene Yuanjiang basins of China. The presence of younger (<200 Ma) zircons from the Qamdo Block of Tibet are less easily explicable in terms of recycling by erosion of older sedimentary rocks and imply a regional drainage linking SE Tibet and the South China Sea in the Late Eocene-Oligocene. Detrital zircons from offshore in the South China Sea showed initial local erosion, but with a connection to a river stretching to SE Tibet in the Late Oligocene. A change from regional to local sources in the Early Miocene in the Yuanjiang Basin indicates the timing of disruption of the old drainage driven by regional plateau uplift

A SOCIAL SURVEY ON COMMUNITY RESPONSE TO ROAD TRAFFIC NOISE IN HANOI

Joint Research on Environmental Science and Technology for the Eart

A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs

Non-stoichiometric oxide and metal interfaces and reactions

We have employed a combination of experimental surface science techniques and density functional calculations to study the reduction of TiO2(110) surfaces through the doping with submonolayer transition metals. We concentrate on the role of Ti adatoms in self doping of rutile and contrast the behaviour to that of Cr. DFT+U calculations enable identification of probable adsorption structures and their spectroscopic characteristics. Adsorption of both metals leads to a broken symmetry and an asymmetric charge transfer localised around the defect site of a mixed localised/delocalised character. Charge transfer creates defect states with Ti 3d character in the band gap at similar to 1-eV binding energy. Cr adsorption, however, leads to a very large shift in the valence-band edge to higher binding energy and the creation of Cr 3d states at 2.8-eV binding energy. Low-temperature oxidation lifts the Ti-derived band-gap states and modifies the intensity of the Cr features, indicative of a change of oxidation state from Cr3+ to Cr4+. Higher temperature processing leads to a loss of Cr from the surface region, indicative of its substitution into the bulk