Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks

In this paper, we investigate the problem of controlling probabilistic Boolean control networks (PBCNs) to achieve reachability with maximum probability in the finite time horizon. We address three questions: 1) finding control policies that achieve reachability with maximum probability under fixed, and particularly, varied finite time horizon, 2) leveraging prior knowledge to solve question 1) with faster convergence speed in scenarios where time is a variable framework, and 3) proposing an enhanced Q-learning (QL) method to efficiently address the aforementioned questions for large-scale PBCNs. For question 1), we demonstrate the applicability of QL method on the finite-time reachability problem. For question 2), considering the possibility of varied time frames, we incorporate transfer learning (TL) technique to leverage prior knowledge and enhance convergence speed. For question 3), an enhanced model-free QL approach that improves upon the traditional QL algorithm by introducing memory-efficient modifications to address these issues in large-scale PBCNs effectively. Finally, we apply the proposed method to two examples: a small-scale PBCN and a large-scale PBCN, demonstrating the effectiveness of our approach

Characterization of Shewanella sp. Isolated from Cultured Loach Misgurnus anguillicaudatus

Shewanella infection of fish has become a significant problem in aquaculture. In September 2014, a disease was seen in cultured loach (Misgurnus anguillicaudatus) in Xuzhou, central China. A gram-negative bacillus was isolated from the diseased loaches and was tentatively named strain MS1, which was then identified as Shewanella sp. by physiological and biochemical characteristics analysis. The strain MS1 showed highest 16S rRNA sequence identities (98.93%, 98.87%) with the latest two species listed (Shewanella sp. MR7, Shewanella sp. MR4). The phylogenetic tree constructed on the basis of 16S rRNA gene sequences strongly indicated that the strain MS1 is most closely related to the new Shewanella strains MR7 and MR4. The isolate MS1 was confirmed as the pathogen of the infected loaches by experimental reinoculation. The strain was susceptible to most antimicrobial agents tested, but resistant to glycopeptides (vancomycin, teicoplanin) and lincosamide (lincomycin, clindamycin). This is the second report on Shewanella sp. isolated from the diseased loach

Analysis on the Settlement of Adjacent Buildings Caused by the Underpassing Construction of the Biased Tunnel

Through the simulation analysis of the settlement and deformation law of the surface buildings caused by the construction of the biased tunnel, the following points are obtained: (1) The Peak formula is revised, and the influence range of the biased tunnel is predicted based on the formula. (2) It is concluded that when the tunnel is biased, the position of maximum deformation caused by ground settlement is generally in a parallel area 0.5 times the buried depth from the center line of the tunnel. (3) Through the double-layer verification of simulation analysis and monitoring measurement data, prior to the construction of buildings with similar weak foundations, their foundations should be reinforced in advance. (4) In the process of this simulation, the complicated influence of water pressure on tunnel excavation was not considered, which can be further optimized in the later stage

First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In this paper, we present a novel directionality reconstruction method for atmospheric neutrino events in large homogeneous liquid scintillator detectors based on waveform analysis and machine learning techniques. We demonstrate for the first time that such detectors can achieve good direction resolution and potentially play an important role in future atmospheric neutrino oscillation measurements.Comment: Prepared for submission to PR

Identification and isolation of Genotype-I Japanese Encephalitis virus from encephalitis patients

Historically, Japanese Encephalitis virus (JEV) genotype III (GIII) has been responsible for human diseases. In recent years, JEV genotype I (GI) has been isolated from mosquitoes collected in numerous countries, but has not been isolated from patients with encephalitis. In this study, we report recovery of JEV GI live virus and identification of JEV GI RNA from cerebrospinal fluid (CSF) of encephalitis patients in JE endemic areas of China. Whole-genome sequencing and molecular phylogenetic analysis of the JEV isolate from the CSF samples was performed. The isolate in this study is highly similar to other JEV GI strains which isolated from mosquitoes at both the nucleotide and deduced amino acid levels. Phylogenetic analysis based on the genomic sequence showed that the isolate belongs to JEV GI, which is consistent with the phylogenetic analysis based on the pre-membrane (PrM) and Glycoprotein genes. As a conclusion, this is the first time to isolate JEV GI strain from CSF samples of encephalitis patients, so continuous survey and evaluate the infectivity and pathogenecity of JEV GI strains are necessary, especially for the JEV GI strains from encephalitis patients. With respect to the latter, because all current JEV vaccines (live and inactivated are derived from JEV GIII strains, future studies should be aimed at investigating and monitoring cross-protection of the human JEV GI isolates against widely used JEV vaccines

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

A multi-purpose reconstruction method based on machine learning for atmospheric neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to measure the neutrino mass ordering (NMO) using a 20-kton liquid scintillator (LS) detector. Besides the precise measurement of the reactor neutrino’s oscillation spectrum, an atmospheric neutrino oscillation measurement in JUNO offers independent sensitivity for NMO, which can potentially increase JUNO’s total sensitivity in a joint analysis. In this contribution, we present a novel multi-purpose reconstruction method for atmospheric neutrinos in JUNO at few-GeV based on a machine learning technique. This method extracts features related to event topology from PMT waveforms and uses them as inputs to machine learning models. A preliminary study based on the JUNO simulation shows good performances for event directionality reconstruction and neutrino flavor identification. This method also has a great application potential for similar LS detectors