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With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.

The particle swarm optimization (PSO) algorithm is a reasonable method for solving complex functions. In previous years, it has been extensively applied in cloud computing environments, such as cloud resource schedules and privacy management. However, this algorithm can easily fall into local minimum points and has a slow convergence speed. Using an established ontology model, we proposed a framework and two novel PSO algorithms in this paper. The ontology model is introduced with various types of operators to the cooperation framework. In contrast with traditional algorithms, our algorithms include semantic roles and concepts to update crucial parameters based on the cooperation framework. Using function optimization problems as examples, the experiments show that the particle swarm algorithms within our framework are superior to other classical algorithms.

Oral squamous cell carcinoma is the most common neoplasm of the oral cavity. The incidence rate accounts for 80% of total oral cancer and shows an upward trend in recent years. It has a high degree of malignancy and is difficult to detect in terms of differential diagnosis, as a consequence of which the timing of treatment is always delayed. In this work, Raman spectroscopy was adopted to differentially diagnose oral squamous cell carcinoma and oral gland carcinoma. In total, 852 entries of raw spectral data which consisted of 631 items from 36 oral squamous cell carcinoma patients, 87 items from four oral gland carcinoma patients and 134 items from five normal people were collected by utilizing an optical method on oral tissues. The probability distribution of the datasets corresponding to the spectral peaks of the oral squamous cell carcinoma tissue was analyzed and the experimental result showed that the data obeyed a normal distribution. Moreover, the distribution characteristic of the noise was also in compliance with a Gaussian distribution. A Gaussian process (GP) classification method was utilized to distinguish the normal people and the oral gland carcinoma patients from the oral squamous cell carcinoma patients. The experimental results showed that all the normal people could be recognized. 83.33% of the oral squamous cell carcinoma patients

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The effectiveness of photocatalytic and Fenton reactions in the synergistic treatment of water pollution problems has become indisputable. In this paper, nitrogen-doped TiO2 was selected as the catalyst for the photocatalytic reaction and manganese-substituted phosphomolybdic acid was used as the Fenton reagent, the two of which were combined together by acid impregnation to construct a binary photocatalysis-Fenton composite catalyst. The degradation experiments of the composite catalyst on RhB indicated that under UV–vis irradiation, the composite catalyst could degrade RhB almost completely within 8 min, and the degradation rate was 19.7 times higher than that of N-TiO2, exhibiting a superior degradation ability. Simultaneously, a series of characterization methods were employed to analyze the structure, morphology, and optical properties of the catalysts. The results demonstrated that the nitrogen doping not only expanded the photo response range of TiO2 but reduced the work function of TiO2, which facilitated the transfer of electrons to the loaded Mn-HPMo side and further promoted the electron–hole separation efficiency. In addition, the introduction of Mn-HPMo provided three pathways for the activation of hydrogen peroxide, which enhanced the degradation activity. This study provides novel insights into the construction of binary and efficient catalysts with multiple hydroxyl radical generation pathways.

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