The building façade has considerable effects on the aesthetic experience of observers. However, the experience may differ depending on the observers' expertise. This study was conducted to explore the impact of expertise on preference, visual exploration, and cognitive experience during the aesthetic judgment of designed façades. For this purpose, we developed a paradigm in two separate parts: aesthetic judgment (AJ) and eye movement recording (EMR). Thirty-eight participants participated in this experiment in two groups (21 experts/17 nonexperts). The results revealed significant differences between the two groups in terms of the type and number of preferred façades, as well as eye movement indicators. In addition, based on judgment reaction time and fixation duration as proxy measures of cognitive experience, it was found that expertise might be correlated with cognitive load and task demand. The findings indicate the importance of façades for both groups and suggest that their physical attributes could be effectively manipulated to impact aesthetic experiences in relation to architectural designs.

Background and Objective:Health is a multi-dimensional concept, and the WHO has pointed to its four physical, psychological, spiritual and social dimensions. Social health is one of the most important health indicators of each country and can be influenced by different factors. The purpose of this study was to investigate the relationship between self-efficacy and dimensions of nurses' social health.

Symmetry is an important visual feature for humans and its application in architecture is completely evident. This paper aims to investigate the role of symmetry in the aesthetics judgment of residential building façades and study the pattern of eye movement based on the expertise of subjects in architecture. In order to implement this in the present paper, we have created images in two categories: symmetrical and asymmetrical façade images. The experiment design allows us to investigate the preference of subjects and their reaction time to decide about presented images as well as record their eye movements. It was inferred that the aesthetic experience of a building façade is influenced by the expertise of the subjects. There is a significant difference between experts and non-experts in all conditions, and symmetrical façades are in line with the taste of non-expert subjects. Moreover, the patterns of fixational eye movements indicate that the horizontal or vertical symmetry (mirror symmetry) has a profound influence on the observer’s attention, but there is a difference in the points watched and their fixation duration. Thus, although symmetry may attract the same attention during eye movements on façade images, it does not necessarily lead to the same preference between the expert and non-expert groups.

No abstract

Rail track deterioration models are integral components of rail infrastructure maintenance management systems. In particular, track geometry defects are one of the leading causes of train accidents. Also, control, management, and modification of geometric conditions are one of the most important tasks of railway maintenance management systems. Track geometry data such as profile, alignment, gauge, crosslevel, and twist constantly change over time. Therefore, these features have the characteristics of time series data.In this study, a large database from outputs of EM120, a track recording machine, was provided for the years 2009 to 2020 and for all 19 railway zones of Iranian Railways (approximately 14,000 km of railway track and 100 GB of data). From Deep Learning techniques, CNN, LSTM, and CNN-LSTM models were selected to predict track geometry degradation. Long short-term memory (LSTM) has the advantage of analysing relationships among time-series data through its memory function, while CNN models may filter out the noise of the input data and extract more valuable features that would be more useful for the final prediction model. By integrating convolutional neural networks (CNN) with long short-term memory (LSTM), a CNN-LSTM model is considered to be more accurate and can make better point-wise predictions.The models were built from the average segments of 100 and 200 meters. The forecasting results of proposed models were analysed and compared, and the CNN-