Simulation and Optimization of Pedestrian Regular Evacuation in Comprehensive Rail Transit Hub – A Case Study in Beijing

Extensive efforts have been made in pedestrian evacuation of urban rail transit systems, since there has emerged an increasing number of congestion problems. However, few studies focus on the comprehensive urban rail transit hubs. As a comprehensive interchange hub integrating urban railway and intercity railway lines, Beijing West Railway Station was taken as a case study object. The pedestrian evacuation characteristics were analysed first. Then, a social force-based simulation model of Beijing West Railway Station was constructed in PTV Viswalk. The model was applied to visually display a real evacuation process and help identify evacuation bottlenecks. The results showed that the risk points at different facilities had various causes and features. Furthermore, the simulation model could also be used to evaluate the effectiveness of different optimization measures as long as certain model parameters were changed beforehand.</p

PhotoScout: Synthesis-Powered Multi-Modal Image Search

Due to the availability of increasingly large amounts of visual data, there is a growing need for tools that can help users find relevant images. While existing tools can perform image retrieval based on similarity or metadata, they fall short in scenarios that necessitate semantic reasoning about the content of the image. This paper explores a new multi-modal image search approach that allows users to conveniently specify and perform semantic image search tasks. With our tool, PhotoScout, the user interactively provides natural language descriptions, positive and negative examples, and object tags to specify their search tasks. Under the hood, PhotoScout is powered by a program synthesis engine that generates visual queries in a domain-specific language and executes the synthesized program to retrieve the desired images. In a study with 25 participants, we observed that PhotoScout allows users to perform image retrieval tasks more accurately and with less manual effort

ImageEye: Batch Image Processing Using Program Synthesis

This paper presents a new synthesis-based approach for batch image processing. Unlike existing tools that can only apply global edits to the entire image, our method can apply fine-grained edits to individual objects within the image. For example, our method can selectively blur or crop specific objects that have a certain property. To facilitate such fine-grained image editing tasks, we propose a neuro-symbolic domain-specific language (DSL) that combines pre-trained neural networks for image classification with other language constructs that enable symbolic reasoning. Our method can automatically learn programs in this DSL from user demonstrations by utilizing a novel synthesis algorithm. We have implemented the proposed technique in a tool called ImageEye and evaluated it on 50 image editing tasks. Our evaluation shows that ImageEye is able to automate 96% of these tasks

Analysis and application of safety risks for gas pipelines in karst sinkhole-prone areas based on the D/I-MICMAC-VS integrated method

To mitigate the risk of gas pipelines in karst sinkhole-prone areas, this study employs the DEMATEL/ISM method to elucidate the hierarchical structure and causal relationships among various factors in the system, considering four categories of accident causes: human, material, environment and management. Additionally, the MICMAC method is utilized to analyze the dependence and driving force of risk factors. Utilizing the Visual Studio platform, the software for risk analysis of gas pipelines in karst sinkhole-prone areas is developed. This research introduces the D/I-MICMAC-VS integrated risk analysis method and provides an example analysis. The results demonstrate that: (1) The risk factors for gas pipelines in karst sinkhole-prone areas are distributed across six levels. The possibility of risk accidents can be reduced in the short term by rigorously managing surface-level direct factors, while middle-level indirect factors play an intermediary role in the system. Effective control of gas pipeline accidents can only be achieved by addressing deep-rooted factors fundamentally. (2) The spontaneous cluster serves as a key element for risk management and control of gas pipeline accidents, and prioritized intervention significantly aids in accident prevention. The independent cluster directly influences the system’s risk level through its own changes and development. The linkage cluster plays a pivotal role in transmitting and promoting the evolution and development of accidents. Effective risk management and control can be achieved by discerning the deep root factors that inducing changes in the dependency cluster

Synthesis of a Novel Gemini Cationic Surfactant and Its Inhibition Behaviour and Mechanism Study on 2024 Al-Cu-Mg Alloy in Acid Solution

Isopropylamine was taken as a raw material to synthesize a new multi-alkyl multiple quaternary-ammonium salts gemini surfactant bis[2-hydroxy-3-(dodecyldimethylammonio)propyl]-isopropylamine dichloride. The structure of the synthetic product was characterized by 1H NMR and FTIR. The surface activity was investigated; the inhibition efficiencies and inhibition mechanism of the synthetic product were studied by weight loss method, electrochemical method, microscopic morphology observation, and adsorption model calculation. The results indicate that cmc of synthetic product was 9.204 × 10-4 mol/L; when the concentrations were lower than cmc, the inhibition efficiencies rose substantially, which was up to 89.3% with the concentration of 9.204 ×  10-4 mol/L; when they were higher than cmc, inhibition efficiencies were basically unchanged; polarization tests showed that the synthesis product could restrain both anodic and cathodic reactions; when the concentrations were lower than cmc, the adsorption of the synthetic product conformed to the Langmuir model, which formed monolayer on the 2024 Al-Cu-Mg alloy surface; when they were higher than cmc, it formed bilayer, so the adsorption of the synthetic product did not conform to the Langmuir model anymore

Data Extraction via Semantic Regular Expression Synthesis

Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic pattern matching, they fall short for data extraction tasks that involve both a syntactic and semantic component. To address this issue, we introduce semantic regexes, a generalization of regular expressions that facilitates combined syntactic and semantic reasoning about textual data. We also propose a novel learning algorithm that can synthesize semantic regexes from a small number of positive and negative examples. Our proposed learning algorithm uses a combination of neural sketch generation and compositional type-directed synthesis for fast and effective generalization from a small number of examples. We have implemented these ideas in a new tool called Smore and evaluated it on representative data extraction tasks involving several textual datasets. Our evaluation shows that semantic regexes can better support complex data extraction tasks than standard regular expressions and that our learning algorithm significantly outperforms existing tools, including state-of-the-art neural networks and program synthesis tools