Design of universal chemical relaxation oscillator to control molecular computation

Embedding efficient command operation into biochemical system has always been a research focus in synthetic biology. One of the key problems is how to sequence the chemical reactions that act as units of computation. The answer is to design chemical oscillator, a component that acts as a clock signal to turn corresponding reaction on or off. Some previous work mentioned the use of chemical oscillations. However, the models used either lack a systematic analysis of the mechanism and properties of oscillation, or are too complex to be tackled with in practice. Our work summarizes the universal process for designing chemical oscillators, including generating robust oscillatory species, constructing clock signals from these species, and setting up termination component to eventually end the loop of whole reaction modules. We analyze the dynamic properties of the proposed oscillator model in the context of ordinary differential equations, and discuss how to determine parameters for the effect we want in detail. Our model corresponds to abstract chemical reactions based on mass-action kinetics which are expected to be implemented into chemistry with the help of DNA strand displacement cascades. Our consideration of ordering chemical reaction modules helps advance the embedding of more complex calculations into biochemical environments

(Z)-Ethyl 2-(3-nitro­benzyl­idene)-3-oxo­butanoate

The title mol­ecule, C13H13NO5, adopts a Z conformation at the C= C double bond. The eth­oxy atoms of the ethyl ester group are disordered over two orientations in a 3:2 ratio. Weak inter­molecular C—H⋯O hydrogen bonds help to establish the packing

Accurate control to run and stop chemical reactions via relaxation oscillators

Regulation of multiple reaction modules is quite common in molecular computation and deep learning networks construction through chemical reactions, as is always a headache for that sequential execution of modules goes against the intrinsically parallel nature of chemical reactions. Precisely switching multiple reaction modules both on and off acts as the core role in programming chemical reaction systems. Unlike setting up physical compartments or adding human intervention signals, we adopt the idea of chemical oscillators based on relaxation oscillation, and assign corresponding clock signal components into the modules that need to be regulated. This paper mainly demonstrates the design process of oscillators under the regulation task of three modules, and provides a suitable approach for automatic termination of the modules cycle. We provide the simulation results at the level of ordinary differential equation and ensure that equations can be translated into corresponding chemical reaction networks

The effect of ride experience on changing opinions toward autonomous vehicle safety

Autonomous vehicles (AVs) are a promising emerging technology that is likely to be widely deployed in the near future. People\u27s perception on AV safety is critical to the pace and success of deploying the AV technology. Existing studies found that people\u27s perceptions on emerging technologies might change as additional information was provided. To investigate this phenomenon in the AV technology context, this paper conducted real-world AV experiments and collected factors that may associate with people\u27s initial opinions without any AV riding experience and opinion change after a successful AV ride. A number of ordered probit and binary probit models considering data heterogeneity were employed to estimate the impact of these factors on people\u27s initial opinions and opinion change. The study found that people\u27s initial opinions toward AV safety are significantly associated with people\u27s age, personal income, monthly fuel cost, education experience, and previous AV experience. Further, the factors dominating people\u27s opinion change after a successful AV ride include people\u27s age, personal income, monthly fuel cost, daily commute time, driving alone indicator, willingness to pay for AV technology, and previous AV experience. These results provide important references for future implementations of the AV technology. Additionally, based on the inconsistent effects for variables across different models, suggestions for future transportation survey designs are provided

A COMPUTATIONAL STUDY ON THE HYDROGEN-BONDED COMPLEXES FORMED BY THE ANTHYRIDONE AND DIALDEHYDE DERIVATIVES

A theoretical study on hydrogen-bonded complex 1 formed by anthyridone (monomer A) and 2,6-diaminopyridine-3,5-dialdehyde (monomer B) was performed using the AM1 method to obtain its binding energy. A series of complexes 2 to 9 were designed by changing the R-groups on monomer A in complex 1 into C6H5, p-toluene, p-phenol, OH, OCH3, and turning the X-groups on monomer B into F, Cl, I, respectively. Based on the optimized geometries, the electronic spectra for the complexes were calculated with the INDO/CIS method and the IR spectra were computed utilizing the AM1 method. It was indicated that the dimer could be formed by the two monomers via triple hydrogen bonds because of its negative binding energy. The binding energies of the complexes were changed with the change of the electronic properties and steric effects of the substituents on the monomers. The first absorptions in the electronic spectra of the complexes were red-shifted compared with those of the monomers. The stretching vibrations of the N-H bonds on the monomers were weakened and their frequencies were reduced with the formation of the hydrogen bonds. KEY WORDS: Anthyridone, 2,6-Diaminopyridine-3,5-dialdehyde, Hydrogen bonding, IR spectra, AM1 Bull. Chem. Soc. Ethiop. 2007, 21(2), 263-270

Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks

Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable motion patterns, leading to a wide range of driving behaviors that warrant in-depth investigation. This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction (GIMTP) framework, designed to probabilistically predict future vehicle trajectories by effectively capturing these interactions. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. To holistically capture both spatial and temporal dependencies embedded in this dynamic adjacency matrix, the methodology incorporates the Diffusion Graph Convolutional Network (DGCN), thereby providing a graph embedding of both historical states and future states. Furthermore, we employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings for enhanced intention recognition and trajectory prediction. This model gives two-dimensional predictions for each mode of longitudinal and lateral driving behaviors and offers probabilistic future paths with corresponding probabilities, addressing the challenges of complex vehicle interactions and multi-modality of driving behaviors. Validation using real-world trajectory datasets demonstrates the efficiency and potential