Modelling the impacts of crowds on occupants in the built environment—A static,rule-based approach to human perception and movement

A building occupant’s experiences are not passive responses to environmental stimuli, but are the results of multifaceted, prolonged interactions between people and space. We present a framework and prototype software tool for logically reasoning about occupant perception and behaviour in the context of dynamic aspects of buildings in operation, based on qualitative deductive rules. In particular, we focus on the co-presence of different user groups and the resulting impact on perceptual and functional affordances of spatial layouts by utilising the concept of spatial artefacts. As a first proof of concept of our approach, we have implemented a prototype crowd analysis software tool in our new system ASP4BIM, developed specifically to support architectural design reasoning in the context of public-facing buildings with complex signage systems and diverse intended user groups. We evaluate our prototype on the Urban Sciences Building at Newcastle University, a large, state-of-the-art living laboratory and multipurpose academic building. Our findings are that the ASP4BIM-based prototype supports a range of novel query services for formally analysing the impacts of crowds on pedestrians that are logically derived through the use of qualitative deductive rules, that complements other powerful crowd analysis approaches such as agent-based simulation.     

Quantifying and comparing the effects of human and organizational factors in electric maloperation accidents with HFACS–CatBoost and SHAP

The proportion of electric maloperation accidents (EMAs) in substations caused by human and organizational factors (HOFs) has gradually increased. Although there has been some research into the factors affecting EMAs in substations, the available results are insufficient to support the interpretation of HOFs in EMAs. This article explores the relationships between the HOFs and EMAs using Human Factors Analysis and Classification System-gradient boosting with categorical features support (HFACS–CatBoost) and Shapley Additive exPlanation (SHAP) methods. First, the HFACS framework was introduced to identify 135 EMAs in the Southern Power Grid risk causation. CatBoost was used to construct an accident classification model to analyze the important relationship between accidents and HOFs and to compare and analyze with the extreme gradient boosting (XGBoost) and the binary logistic regression (BLR) to verify the superiority of CatBoost. Finally, to solve the problem of inadequate interpretation of the CatBoost black-box model, the SHAP value plot was applied to express the contribution degree relationship between accidents and HOFs. The results show that the above method can explore and explain the importance and contribution of HOFs in EMAs. And from this, it is concluded that poor psychological state, poor communication and coordination, inadequate supervision, and inadequate training and education are highly correlated with the occurrence of EMAs. The findings will help substation operations and maintenance staff to develop safety measures to address the confusion of HOFs in substations and prevent the occurrence of EMAs.     

Factor space theory in fuzzy information processing—Composition of states of factors and multifactorial decision making

The concept of factor space used in fuzzy information processing is investigated. The composition of the states of factors, which is a basic tool in multifactorial decision making, is first considered. Then, a general model of multifactorial decision making is formulated. Finally, multi-factorial decision making with multiple objectives is introduced. Numerical examples are given to illustrate the approaches.     

Do we need sustainability as a new approach in human factors and ergonomics?

The International Ergonomics Association Technical Committee ‘Human Factors and Sustainable Development’ was established to contribute to a broad discourse about opportunities and risks resulting from current societal ‘mega-trends’ and their impacts on the interactions among humans and other elements of a system, e.g. in work systems. This paper focuses on the underlying key issues: how do the sustainability paradigm and human factors/ergonomics interplay and interact, and is sustainability necessary as a new approach for our discipline? Based on a discussion of the sustainability concept, some general principles for designing new and enhancing existent approaches of human factors and ergonomics regarding their orientation towards sustainability are proposed.

Practitioner summary: The increasing profile of sustainability on the international stage presents new opportunities for human factors/ergonomics. Positioning of the sustainability paradigm within human factors/ergonomics is discussed. Approaches to incorporating sustainability in the design of work systems are considered.     

The ‘Wheel of Misfortune’: a taxonomic approach to human factors in accident investigation and analysis in aviation and other complex systems

The analysis and reporting of the human factors aspects of accidents in aviation and other complex systems continues to present difficulties for investigators and analysts alike. Reason's ‘latent conditions’ model has had a major impact on the way accidents are conceptualized but it has proven difficult to apply as a practical tool. Recent attempts to overcome these difficulties are discussed and an alternative conceptualization is proposed. This conceptualization is based on a blend of several well-supported theoretical models in cognitive engineering and can be used to formulate a parsimonious analysis system for the investigation and reporting of the human factors aspects of accidents. Two well-known examples of transportation disasters are briefly described and related to the proposed conceptual framework. The proposed framework serves three important functions in accident investigation and analysis: a heuristic function, an investigative function, and an integrative function.     

Do as your parents say?—Analyzing IT adoption influencing factors for full and under age applicants

The article suggests a model for examining the adoption of e-recruiting by individuals. The model is empirically evaluated using survey data from 323 full and under age applicants. The results explain substantial parts of the individual adoption decision. Interestingly, the relative importance of the adoption drivers varies with age, social environment and the level of education. While, as expected, overall Performance Expectancy is the major force behind adopting e-recruiting, the relative importance of the other factors differs a lot. Whereas Facilitating Conditions came out as an important driver for under age pupils, full age students by contrast are highly driven by the influence of their peer groups and the communication of the respective company they apply for. A major outcome is that the Subjective Norm of family and friends, teachers and professors has a weaker influence for under age pupils who mostly live with their parents than for the group of students who already left home to study at college. Consequentially we assume that the social influence of peer groups on an individual’s adoption differs with respect to age, social environment and level of education. This should be investigated more carefully in future adoption research as it might provide an answer for the varying significance of Subjective Norm in adoption research.     

Impact of two types of partner,perceived or actual,in human–human and human–agent interaction

Participants engaged in the Prisoner’s dilemma game with a partner through a computer terminal. We define two types of partner: a perceived partner and an actual partner, and manipulated the two factors independently. A perceived partner means a partner with whom participants imagined themselves to be interacting; instruction given by an experimenter controls the image of the perceived partner. An actual partner can change its behavior. In one scenario participants actually interacted with a human partner, in another scenario their partner was either a mostly cooperating computer agent or a mostly defecting computer agent. Three experiments were performed. The result suggested that the participants’ selection behavior was largely influenced by the instruction given about the partner by the experimenter and not influenced by the partner’s actual behavior. The analysis of the participants’ impressions of the partner showed that the effect of instruction about the partner disappeared. Individual likeability for a partner was very influenced by the partner’s behavior; as the participants incurred more defect actions from the partner, individual likeability for the partner decreased. On the other hand, social likeability for a partner was not so influenced by the partner’s behavior, but rather related to the participants’ own behavior. The participants who made more defect actions rated their partner’s social likeability lower.     

Effects of output display and control—display gain on human performance in interactive systems

Abstract

Human performance comparisons on interactive systems were drawn between output displays (CRT and LCD) across settings of control-display gain. Empirical evidence was sought in light of the common feeling in the user community that motor-sensory tasks are more difficult on a system equipped with an LCD display vs. a CRT display. In a routine target acquisition task using a mouse, movement times were 34% longer and motor-sensory bandwidth was 25% less when the output display was an LCD vs. a CRT. No significant difference in error rates was found. Control-display (C-D) gain was tested as a possible confounding factor; however, no interaction effect was found. There was a significant, opposing main effect for C-D gain on movement lime and error rates, illustrating the difficulty in optimizing C-D gain on the basis of movement time alone.     

Relationships between tree growth and NDVI of grassland in the semi‐arid grassland of north China

This study explores the relationships between the Normalized Difference Vegetation Index (NDVI), recorded above‐ground grass biomass and tree‐ring width index of relict Meyer spruce (Picea meyeri Rehd. et Wils.) forest in the typical steppe, north China. The average NDVI in May, June and August derived from an area of 0.5°×0.5° shows a large correlation with measured above‐ground production, indicating that NDVI can reflect the approximate variability of above‐ground biomass in the typical steppe. The integrated NDVI from 20 May to 10 July also exhibits high agreement with tree‐ring width series of Meyer spruce from 1982 to 1994, which is attributed to their common response to precipitation in the previous August–October and current May. This study provides a basis for linking remotely sensed NDVI of grassland to tree growth in semi‐arid grassland.     

Microcare—promises and pitfalls in implementing microcomputer programs in human service agencies

More than five years after an integrated microcomputer system for direct care, administration, and research was introduced into a human service agency. The promises and pitfalls are reviewed. The positive results included decreased costs, increased quality of care, ad improved working conditions. The negative results included increased time spent on computer-related aspects and the inability of the agency to deal with the quantity and quality of work generated.     

Factors influencing health care professionals’ adoption of mobile platform of medical and senior care in China

A mobile platform of medical and senior care (MPMSC) is a platform that can provide one-stop medical and senior care services through mobile devices. This study developed a research model based on the value-based adoption model to understand the factors influencing health care professionals’ adoption of MPMSC. Empirical analysis has shown that perceived value and legal concerns can predict health care professionals’ intention to adopt MPMSC and that outcome expectations, perceived mobility, perceived effort, and privacy concerns can predict perceived value. Research findings can enhance the understanding of the factors influencing the adoption of MPMSC by health care professionals.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Multilayer perceptron for the learning of spatio-temporal dynamics—application in thermal engineering

Thermal engineering deals with the estimation of the temperature at different spatial points and different instants for a given set of boundary and initial conditions. For this purpose, the reference model is a numerical simulation model but it is time-consuming. Consequently we build a surrogate model in order to replace it. This surrogate model is a recursive multilayer perceptron, independent of the boundary conditions and parametrized by the statistical learning of multidimensional temporal trajectories computed with the reference model. It emulates the outputs of the reference model over time from the only knowledge of initial conditions and exogenous variables. Moreover this model is able to predict these outputs in steady state, even if its formulation is time-dependent.A new methodology is proposed so as to overcome the learning problem associated to the very weak number of trajectories available for the surrogate model construction. The first step attempts to build a more robust surrogate model by considering it as the average of local models resulting from the V-folds cross-validation technique. This new kind of multilayer perceptron is much more robust and accurate, in particular when the learning dataset is very small. The second step consists in the creation of a new learning dataset which is made up of each time observation coming from each trajectory. In this way, we artificially obtain a sizeable sample allowing all the classic neural networks constructions.Furthermore, many approaches exist in order to select the best hidden neurons number but most of them are costly or require a lot of observations. We consider here a non-asymptotic approach based on the minimization of a penalized criterion providing accurate results in an economical computational way. In order to calibrate precisely the penalty term, we use the slope heuristic or the dimension jump, recently introduced in a regression framework. The validation of the method is performed on a toy function.The prediction ability of the surrogate model built with the new methodology is successfully compared to usual constructions on a simplified problem and then applied to thermal engineering.     

Presence in personalised e-learning – the impact of cognitive and emotional factors and the moderating role of gender

ABSTRACT

Digital technologies offer tremendous opportunities to develop flexible and personalised e-learning environments which are capable of delivering high-quality services. The success of these e-learning environments is linked to their ability to elicit subjective experiences of presence, through which e-learners feel individually ‘placed’ within a true, humanised education environment. We built an integrated model that situates cognitive processes (the e-learner’s perceived control, focused attention, mental imagery) and emotion at the core of presence formation, and considers the moderating role of gender. We tested the model with a large sample of students at a personalised e-learning environment. The results indicate that cognitive and emotional processes, all unleashed by external stimuli coming from the personalised e-learning environment, activate presence and that gender has a crucial moderating role for emotion-presence predictions.     

What do human factors and ergonomics professionals value in research publications? Re-examining the research–practice gap

The research–practice gap is of concern in human factors/ergonomics (HF/E) as there is a belief that HF/E research may not be making an impact on practice in the ‘real world’. A potential issue is what researchers and practitioners perceive as important in HF/E journal articles as a primary means of conveying research findings to practitioners. This study examined the characteristics that make scientific journal articles appeal to HF/E researchers and practitioners using a web-based survey. HF/E researchers and practitioners were more similar than expected in judgements of important attributes and the selection of articles. Both practitioners and researchers considered practical significance to be more important than theoretical significance, in direct contrast to professionals from a related discipline – psychology. Well-written articles were appreciated across disciplines. The results signal a strong interest in practical applications in HF/E, but a relative lack of focus on development of theories that should be the basis for practical applications.     

Use and non‐use of VDTs—organization of terminal work: Research findings from Swedish cross‐site studies in the field of office automation

A group of users in Copenhagen were asked to evaluate how important a number of user interface characteristics were for them. The results show high importance of efficient daily use and of possibilities for exploratory learning while tutorial materials were of less importance. Users also were asked to evaluate four usability aspects of a number of popular programs. Results show that the quality pleasant to work with has the largest impact on evaluations of overall user‐friendliness while users seem able to view usability independently from the number of features in an application.     

A comparison of NOAA–AVHRR fire data with three Landsat data sets in arid and semi-arid Australia

Burnt area data, derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery, are validated in 11 regions of arid and semi-arid Australia, using three separate Landsat-derived burnt area data sets. Mapping accuracy of burnt extent is highly variable between areas and from year to year within the same area. Where there are corresponding patches in the AVHRR and Landsat data sets, the fit is good. However, the AVHRR data set misses some large patches. Overall, 63% of the Landsat burnt area is also mapped in the AVHRR data set, but this varies from 0% to 89% at different sites. In total, 81% of the AVHRR burnt area data are matched in the Landsat data set, but range from 0% to 94%. The lower match rates (<50%) are generally when little area has burnt (0–500 km²), with figures generally better in the more northerly sites. Results of regressions analysis based on 10 km?×?10 km cells are also variable, with R ² values ranging from 0.37 (n?=?116) to 0.94 (n?=?85). For the Tanami Desert scene, R ² varies from 0.41 to 0.61 (n?=?368) over three separate years. Combining the data results in an R ² of 0.60 (n?=?1315) (or 0.56 with the intercept set to 0). The slopes of the regressions indicate that mapping the burnt area from AVHRR imagery underestimates the ‘true’ extent of burning for all scenes and years. Differences in mapping accuracy between low and high fire years are examined, as well as the influence of soil, vegetation, land use and tenure on mapping accuracy. Issues which are relevant to mapping fire in arid and semi-arid environments and discontinuous fuels are highlighted.     

Development and evaluation of an ergonomic software package for predicting multiple-task human performance and mental workload in human–machine interface design and evaluation

Predicting human performance and mental workload in multiple task situations at an early stage of system design can save a significant amount of time and cost. However, existing modeling tools either can only predict human performance or require users of tools to learn a new programming language. Queueing Network-Model Human Processor (QN-MHP) is a new cognitive architecture for modeling both human performance and mental workload in multiple tasks. This paper describes the development of a Visual Basic Application in Excel (VBA) software package and an illustrative case study to evaluate its effectiveness. The software package has an easy-to-use user interface for QN-MHP that assists users of the modeling tool to simulate a dual task including definition of the tasks and interfaces by clicking buttons to select options and filling texts in a table, with no need to learn a simulation language. It allows the model user to intuitively observe the information processing state of the model during simulation, and conveniently compare the simulated human performance and mental workload for different designs. The illustrative case study showed that naïve users without prior simulation language programming experience can model human performance and mental workload in a complex multitask situation within 3 min; and this software package can save 71% of modeling time and reduce 30% of modeling errors. Further developments of the VBA software package of QN-MHP are also discussed on how to make it a comprehensive proactive ergonomic design and analysis tool.