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Cement-based sensors include conductive fillers to achieve a sensing capability based on the piezoresistivity phenomenon, in which the electrical resistivity changes with strain. The microstructural characterisation of cement-based sensors can be obtained using a promising non-destructive technique, such as AC impedance spectroscopy (ACIS), which has been recently used by many researchers. This paper reviews the fundamental concepts of piezoresistivity and ACIS in addition to the comparison of equivalent circuit models of cement-based sensors found in the literature. These concepts include piezoresistivity theory, factors affecting piezoresistivity measurement, resistance measurement methodology, strain/damage sensing, causes of piezoresistivity, theories of conduction, AC impedance spectroscopy theory, and the equivalent circuit model. This review aims to provide a comprehensive guide for researchers and practitioners interested in exploring and applying different techniques to self-sensing concrete. [ABSTRACT FROM AUTHOR]

With the expected increase in warmer conditions caused by climate change, heat-related illnesses are becoming a more pressing issue. One way that humans can protect themselves from this is to seek shade. The design of urban spaces can provide individuals with a variety of ways to obtain this shade. The objective of this study was to perform a detailed evaluation and comparison of three shading strategies that could be used in an urban environment: shade from a building, from a tree, and from an umbrella. This was done through using field measurements to calculate the impact of each strategy on a thermal comfort index (Comfort Formula (COMFA)) in two urban settings during sunny days of the summer of 2013 and 2014 in London, Canada. Building shade was found to be the most effective cooling strategy, followed by the tree strategy and the umbrella strategy. As expected, the main determinant of this ranking was a strategy's ability to block incoming shortwave radiation. Further analysis indicated that changes in the convective loss of energy and in longwave radiation absorption had a smaller impact that caused variations in the strategy effectiveness between settings. This suggests that under non-sunny days, these rankings could change. [ABSTRACT FROM AUTHOR]

Group activity recognition is a central theme in many domains, such as sports video analysis, CCTV surveillance, sports tactics, and social scenario understanding. However, there are still challenges in embedding actors' relations in a multi-person scenario due to occlusion, movement, and light. Current studies mainly focus on collective and individual local features from the spatial and temporal perspectives, which results in inefficiency, low robustness, and low portability. To this end, a Spatio-Temporal Attention-Based Graph Convolution Network (STAB-GCN) model is proposed to effectively embed deep complex relations between actors. Specifically, we leverage the attention mechanism to attentively explore spatio-temporal latent relations between actors. This approach captures spatio-temporal contextual information and improves individual and group embedding. Then, we feed actor relation graphs built from group activity videos into our proposed STAB-GCN for further inference, which selectively attends to the relevant features while ignoring those irrelevant to the relation extraction task. We perform experiments on three available group activity datasets, acquiring better performance than state-of-the-art methods. The results verify the validity of our proposed model and highlight the obstructive impacts of spatio-temporal attention-based graph embedding on group activity recognition. [ABSTRACT FROM AUTHOR]

Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars. [ABSTRACT FROM AUTHOR]