Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents

Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation duration were collected as the input variables for the classification and regression tree (CART) model. Based on this model, the classification result explicitly pointed out that the exit searching efficiency was evolving. By ignoring the last three unimportant factors from the Analytic Hierarchy Process (AHP), the ultimate Dynamic Bayesian Network (DBN) was built with the temporal part of the CART output and the time-independent part of the vehicle characteristics. Simulation showed that the most efficient exit searching period is the middle escape stage, which is 10 seconds after the emergency signal is triggered, and the escape probability clearly increases with the efficient exit searching. Furthermore, receiving emergency escape training contributes to a significant escape probability improvement of more than 10%. Compared with different failure modes, the emergency hammer layout and door reliability have a more significant influence on the escape probability improvement than aisle condition. Based on the simulation results, the escape probability will significantly drop below 0.55 if the emergency hammers, door, and aisle are all in a failure state

The Shadow of Supertranslated Schwarzschild Black Hole

The supertranslated black hole proposed by Hawking, Perry, and Strominger might provide a resolution to the information paradox, which is usually defined by a complicated space-time metric with even less space-time symmetries compared to Kerr black hole. In this paper, we figure out the shadow for the supertranslated Schwarzschild black hole by making use of supertranslated 4-velocities and the trajectories of the light rays. Based on this approach, we find that the photon sphere gets distorted due to the supertranslation hairs and the position of the shadow on the projection plane is shifted by the supertranslation vector, but the size and shape of the shadow remain the same as those of Schwarzschild black hole.Comment: 7 pages, 5 figure

Deciphering Raw Data in Neuro-Symbolic Learning with Provable Guarantees

Neuro-symbolic hybrid systems are promising for integrating machine learning and symbolic reasoning, where perception models are facilitated with information inferred from a symbolic knowledge base through logical reasoning. Despite empirical evidence showing the ability of hybrid systems to learn accurate perception models, the theoretical understanding of learnability is still lacking. Hence, it remains unclear why a hybrid system succeeds for a specific task and when it may fail given a different knowledge base. In this paper, we introduce a novel way of characterising supervision signals from a knowledge base, and establish a criterion for determining the knowledge's efficacy in facilitating successful learning. This, for the first time, allows us to address the two questions above by inspecting the knowledge base under investigation. Our analysis suggests that many knowledge bases satisfy the criterion, thus enabling effective learning, while some fail to satisfy it, indicating potential failures. Comprehensive experiments confirm the utility of our criterion on benchmark tasks

Mutual Information Rate of Gaussian and Truncated Gaussian Inputs on Intensity-Driven Signal Transduction Channels

In this letter, we investigate the mutual information rate (MIR) achieved by an independent identically distributed (IID) Gaussian input on the intensity-driven signal transduction channel. Specifically, the asymptotic expression of the continuous-time MIR is given. Next, aiming at low computational complexity, we also deduce an approximately numerical solution for this MIR. Moreover, the corresponding lower and upper bounds that can be used to find the capacity-achieving input distribution parameters are derived in closed-form. Finally, simulation results show the accuracy of our analysis.Comment: Accepted for publication in IEEE Communications Letter

Symmetry-breaking-induced giant Stark effect in 2D Janus materials

Symmetry breaking generally induce exotic physical properties, particularly for low-dimensional materials. Herein we demonstrate that symmetry breaking induces a giant Stark effect in 2D Janus materials using group IV-V monolayers with a four-atom-layer structure as a model system, which are constructed by Ge and As element substitution of symmetrical SnSb monolayer. A linear giant Stark effect is found in Janus semiconductor monolayers, as verified by the band gap variation up to 134 meV of Sn2SbAs monolayer, which is 30 times larger than that of SnSb monolayer (4 meV) when the applied electric field is increased from -0.30 to 0.30 V/{\AA}. By considering the induced electronic field, we propose a generalized and effective formula that efficiently determines the band gap variation owing to Stark effect. The calculated results from proposed formula are well agreement with those from DFT-HSE06 functional. The giant Stark effect is originated from the large spatial separation of centers of the conduction band minimum and valence band maximum states of Janus structure due to its intrinsic potential gradient. The wide-range tuning of band gap under electronic field shows potential applications of 2D Janus materials in optoelectronic devices.Comment: 10 pages, 5 figure

catena-Poly[[bis­(pyridine-κN)nickel(II)]-μ-oxalato-κ4 O 1,O 2:O 1′,O 2′]

The title compound, [Ni(C2O4)(C5H5N)2]n, was synthesized under hydro­(solvo)thermal conditions. The NiII atom, lying on a twofold rotation axis, has an octa­hedral coordination geometry involving two N atoms from two pyridine ligands and four O atoms from two oxalate ligands. The Ni atoms are connected by the tetra­dentate bridging oxalate ligands into a one-dimensional zigzag chain

The Tall Fescue Turf Grass Class I Chitinase Gene \u3cem\u3eFaChit1\u3c/em\u3e Is Activated by Fungal Elicitors, Dehydration, Ethylene, and Mechanical Wounding

The cDNA, genomic DNA, and promoter sequence of FaChit1, a class I chitinase gene from Festuca arundinacea, were isolated and characterized in the present work. The deduced amino acid sequence of FaChit1 contains the chitin binding, catalytic, and proline and glycine-rich domains characteristic for most class I chitinases, but no C-terminal extension region. FaChit1 is induced effectively by fungal elicitors, dehydration, and ethylene, but only slightly by mechanical wounding. To identify potential stress-related cis-acting elements, 5′ sequences 935, 651, and 233 bp upstream of the FaChit1 start codon were fused to the GUS reporter gene and analyzed in transgenic tobacco. The results indicated that the 935 bp fragment closely mirrored endogenous gene expression and that the 651 bp fragment was sufficient to direct reporter the gene expression in response to fungal elicitors, ethylene, dehydration, or mechanical wounding due to both known and presently uncharacterized cis-acting elements