Performance-driven design with the support of digital tools： Applying.discrete event simulation and space syntax on the design of the emergency department

Ptanning the design of the emergency department （ED） is a complex process. Hospital readers and architects must consider many complex and interdependent factors, including evolving market demands, patient volume, care models, operational processes, staffing, and medical equipment. The application of digital toots, such as discrete event simulation （DES） and space syntax analysis （SSA）, arrows hospital administrators and designers to quantitativety and objectively optimize their facilities. This paper presents a case study that utitized both DES and SSA to optimize the care process and to design the space in an ED environment. DES was apptied in three phases： master planning, process improvement in the existing ED, and designing the new ED. SSA was used to compare the new design with the existing layout to evatuate the effectiveness of the new design in supporting visuat surveiltance and care coordination. This case study demonstrates that DES and SSA are effective toots for facilitating decision-making retated to design, reducing capital and operational costs, and improving organizational performance. DES focuses on operational processes and care flow. SSA complements DES with its strength in linking space to human behavior. Combining both tools can lead to high-performance ED design and can extend to broad applications in health care.

Event Extraction via DMCNN in Open Domain Public Sentiment Information

Event extraction(EE)is a difficult task in natural language processing(NLP).The target of EE is to obtain and present key information described in natural language in a structured form.Internet opinion,as an essential bearer of social information,is crucial.In order to help readers quickly get the main idea of news,a method of analyzing public sentiment information on the Internet and extracting events from news information is proposed.It enables users to quickly obtain information they need.An event extraction method was proposed based on Chinese language public opinion information,aiming at automatically classifying different types of public opinion events by using sentence-level features,and neural networks were applied to extract events.A sentence feature model was introduced to classify different types of public opinion events.To ensure the effective retention of text information in the calculation process,attention mechanism was added to the semantic information,and an effective public opinion event extractor was trained through CNN and LSTM networks.Experiments show that structured information can be extracted from unstructured text,and the purpose of obtaining public opinion event entities,event-entity relationships,and entity attribute information can be achieved.