Muddling between science and engineering: an epistemic strategy for developing human factors and ergonomics as a hybrid discipline

Human Factors and Ergonomics (HFE) recognises itself as a design-driven, systemic and scientific discipline geared towards well-being and performance. Being a scientific discipline and design-oriented requires that the epistemic basis of science and design/engineering be fully comprehended. In interdisciplinary research where these two viewpoints meet, there are often dilemmas posed in terms of knowledge construction and labelling of activity. Therefore, this article scrutinises these two orientations and addresses the differences and commonalities, using case studies from engineering and psychological science (both constituents of HFE). Based on these insights, a way forward is suggested in terms of (1) a reflexive engagement with epistemic concepts and methods; (2) finding a conceptual space for balancing and bridging the science-engineering divide; (3) comprehending ‘design-thinking/design knowledge’ and not treating it as an application of science; (4) providing emphasis on problem formulation and practices of HFE focusing on developing them in systemic terms.     

单一低渗透突出煤层水力冲孔工艺优化应用分析

为了有效提高单一低渗透突出煤层的瓦斯抽采效率，以高压水射流冲击理论为指导，针对平煤股份八矿己组突出煤层特征建立数值模型，模拟分析水力冲孔应力分布规律，通过现场实测分析冲孔有效影响范围，进一步优化设计冲孔参数、指导现场施工。现场应用表明，水力冲孔是一种行之有效的方法，对单一低渗透突出煤层实施水力冲孔卸压增透、提高瓦斯抽采效果具有重要意义。     

Learning Association Rules and Tracking the Changing Concepts on Webpages: An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category (pornographic or normal) and the critical features. Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.     

The contextualization of divergent outlooks in a Greenfield master-planned community: a pathway towards reflexivity

Comprehensive design schemes and specifications have progressively shaped Master-Planned Communities (MPCs). The market largely predetermines these outcomes, with background input from communities. Therefore, this paper endeavours to define the fundamental structures that generate differences between market actors and residents within the North Lakes MPC. This study employs ‘habitus theory’, which is a sociological phenomenon describing the divergence of personal outlooks and expertise. The application of this theory is thus illustrative of the evolution of distinct observations within MPCs. Contemporary studies have under-researched these inherent gaps between communities, authorities, and critics, and these frictions could be exacerbated within restrictive developmental contexts. Epistemic and objectified content analyses collated over many years and sources will uncover the underlying differences between the relevant groups. These analyses will enable the progression of a framework for understanding power relation biases and how reflexivity can enhance current consultative methods.     

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.     

绿色住区参数化优化设计平台的开发进展与展望

绿色住区设计是建设宜居城市和实现节能减排目标的重要手段与方法。文章首先回顾绿色住区参数化优化设计方法及平台概况，在此基础上，重点介绍平面生成、噪声计算、通风计算、可视度计算等新增功能和天空遮挡算法升级，以及广州市的工程应用，最后展望平台研发与应用的未来发展方向。     

Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.