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Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models.

Speech brain-computer interfaces (BCIs), which translate brain signals into spoken words or sentences, have shown significant potential for high-performance BCI communication. Phonemes are the fundamental units of pronunciation in most languages. While existing speech BCIs have largely focused on English, where words contain diverse compositions of phonemes, Chinese Mandarin is a monosyllabic language, with words typically consisting of a consonant and a vowel. This feature makes it feasible to develop high-performance Mandarin speech BCIs by decoding phonemes directly from neural signals. This study aimed to decode spoken Mandarin phonemes using intracortical neural signals. We observed that phonemes with similar pronunciations were often represented by inseparable neural patterns, leading to confusion in phoneme decoding. This finding suggests that the neural representation of spoken phonemes has a hierarchical structure. To account for this, we proposed learning the neural representation of phoneme pronunciation in a hyperbolic space, where the hierarchical structure could be more naturally optimized. Experiments with intracortical neural signals from a Chinese participant showed that the proposed model learned discriminative and interpretable hierarchical phoneme representations from neural signals, significantly improving Chinese phoneme decoding performance and achieving state-of-the-art. The findings demonstrate the feasibility of constructing high-performance Chinese speech BCIs based on phoneme decoding.

This article introduces a novel Deep Reinforcement Learning (DRL)-based approach for mapless navigation in Industrial Autonomous Mobile Robots, emphasizing advancements in generalization through Potential Risk State Augmentation (PRSA) and an adaptive safety optimization reward function. Traditional LiDAR-based state representations often fail to capture environmental intricacies, leading to suboptimal performance. PRSA addresses this by improving the representation of high-dimensional LiDAR data, focusing on essential risk-related information to reduce redundancy and enhance the DRL agent’s generalization across various industrial settings. The adaptive reward function integrated with intrinsic reward mitigates the issue of sparse rewards in complex tasks, promoting faster learning and optimal policy convergence. Extensive experiments demonstrate that our method maintains a high success rate (over 90%) and low collision risk in narrow and dynamic environments compared to existing DRL-based methods. Meanwhile, compared with the classic navigation baseline, the proposed method improves the success rate by about 33% and reduces the mean navigation time by about 48% in real-world navigation tasks. The direct transfer of policies trained in simulations to real-world environments has demonstrated significant potential for enhancing both the efficacy and reliability of autonomous navigation.

The ability to regulate the gut environment has resulted in remarkable great breakthroughs in the treatment of several diseases. Several studies have found that the regulation of the gut environment might provide relief from the symptoms of benign prostatic hyperplasia. However, the correlation between the gut microenvironment and the colon and prostate glands is still unknown. We found that ulcerative colitis (UC) induced an increase in prostate volumes that could be reversed by sodium butyrate (NaB) and fecal microbiota transplantation (FMT). The mechanism by which UC induced changes in the prostate gland was examined via RNA-Seq. The results show that the expression level of GPER was significantly lower in the prostate gland of UC mices than in normal mices. The expression of GPER could be increased via treatment with NaB or FMT. We found that prostate tissues exhibited higher butryic acid levels after they were treated with NaB or FMT. In experiments conducted in vitro , NaB or the fecal filtrate (FF) from healthy mice up-regulated of the expression of GPER, inhibited cell growth, and induced apoptosis in BPH-1 cells. These changes could be alleviated by treatment with the G15 or in GPER-silenced cells.