TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation

Simulating human mobility contributes to city behavior discovery and decision-making. Although the sequence-based and image-based approaches have made impressive achievements, they still suffer from respective deficiencies such as omitting the depiction of spatial properties or ordinal dependency in trajectory. In this article, we take advantage of the above two paradigms and propose a semantic-guiding adversarial network (TrajSGAN) for generating human trajectories. Specifically, we first devise an attention-based generator to yield trajectory locations in a sequence-to-sequence manner. The encoded historical visits are queried with semantic knowledge (e.g., travel modes and trip purposes) and their important features are enhanced by the multihead attention mechanism. Then, we designate a rollout module to complete the unfinished trajectory sequence and transform it into an image that can depict its spatial structure. Finally, a convolutional neural network (CNN)-based discriminator signifies how “real”the trajectory image looks, and its output is regarded as a reward signal to update the generator by the policy gradient. Experimental results show that the proposed TrajSGAN model significantly outperforms the benchmarks under the MTL-Trajet mobility dataset, with the divergence of spatial-related metrics such as radius of gyration and travel distance reduced by 10%–27%. Furthermore, we apply the real and synthetic trajectories, respectively, to simulate the COVID-19 epidemic spreading under three preventive actions. The coefficient of determination metric between real and synthetic results achieves 91%–98%, indicating that the synthesized data from TrajSGAN can be leveraged to study the epidemic diffusion with an acceptable difference. All of these results verify the superiority and utility of our proposed method. IEEE

TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19

The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.

Quantum ozone emission effect in needle-to-plate negative ion generators

Ozone is a highly effective and broad-spectrum non-residual gas disinfectant.The global COVID-19 pandemic has significantly affected public safety and health, and low concentrations of ozone can inactivate the novel coronavirus.The negative ion generator is a safe and efficient method to generate ozone.Through corona discharge on the needle plate, an ion current can be formed between the needle-plate electrodes and a certain concentration of ozone can be released.In the research on the relationship between the electrode-to-plate distance and ozone release in the negative ion generator, different experimental observations show contradictory results, making the theoretical explanation very difficult and complicated.As the needle-to-plate electrode distance increases, the continuous exponential decreasing trend of ozone emission rate changes to a non-continuous step-wised decreasing pattern, which is defined as the Quantum Ozone Emission Effect (QOEE).The QOEE was observed in all negative ion generators when the plate material was aluminium, stainless steel, yellow brass, or copper.The observed quantum ozone emission effect in negative ion generators may be related to the gas ionization potential of the oxygen molecule and to the electron avalanche theory.The quantum effect of ozone emission is a manifestation of the quantum behavior of the microscopic electron world in the macroscopic world.The ozone emission quantum effect provides a novel technical method for measuring the microscopic properties and corona discharge characteristics of materials. © 2022, Editorial Department of Journal of Xidian University. All right reserved.