Conceptualizing Walking and Walkability in the Smart City through a Model Composite w2 Smart City Utility Index

This paper explores walking and walkability in the smart city and makes a case for their centrality in the debate on the resilience and sustainability of smart cities, as outlined in the United Nations’ (UN) Sustainable Development Goals (SDGs). It is argued that, while the human/inhabitant-centric paradigm of urban development consolidates, and research on walking, walkability, and pedestrian satisfaction flourishes, the inroads of ICT render it necessary to reflect on these issues in the conceptually- and geographically-delimited space of the smart city. More importantly, it becomes imperative to make respective findings useful and usable for policymakers. To this end, by approaching walking and walkability through the lens of utility, the objective of this paper is to develop a conceptual framework in which the relevance of walking and walkability, hereafter referred to as w2, as a distinct subject of research in the smart cities debate is validated. This framework is then employed to construct a model of a composite w2smart city utility index. With the focus on the development of the conceptual framework, in which the w2 utility index is embedded, this paper constitutes the first conceptual step of the composite index development process. The value added of this paper is three-fold: First, the relevance of walking and walkability as a distinct subject of research in the realm of smart cities research is established. Second, a mismatch between end-users’ satisfaction derived from walking and their perception of walkability and the objective factors influencing walking and walkability is identified and conceptualized by referencing the concept of utility. Third, a model smart city w2 utility index is proposed as a diagnostic and prognostic tool that, in the subsequent stages of research and implementation, will prove useful for decisionmakers and other stakeholders involved in the process of managing smart cities.

Vibroacoustic design optimization of curved composite shells

The need to simultaneously optimize the structural design properties, and attain a satisfactory vibroacoustic performance for composite structures, has been a challenging task for modern structural engineers. This work is aimed at developing a statistical energy analysis (SEA) based numerical scheme for computing the optimal design parameters of each individual layer of layered curved shells having arbitrary complexities and layering. The main novelty of the work focuses on the computation of SEA properties for curved composite shells and derive the sensitivities of the acoustic transmission coefficient, expressed through the computed SEA properties, with respect to the structural design characteristics to be optimized. A wave finite element approach is employed to calculate the wave propagation constants of the curved shell. The calculated wave constants are then applied to compute the vibroacoustic properties for the curved shell using a SEA approach. Sensitivity analyses are conducted on the vibroacoustic properties to estimate their response to changes in the structural properties. Gradient vector is then formulated and hence the Hessian matrix, which is employed to formulate a Newton-like optimisation algorithm for optimizing the properties of the layered composite shell. The developed scheme is applied to a sandwich shell; optimal design parameters of [Formula: see text] and [Formula: see text] are obtained for the facesheet and the core of the shell whose base parameters are [Formula: see text] and [Formula: see text], respectively. This simultaneously optimizes the structure with maximum stiffness and minimum mass and attains a satisfactory dynamic performance for acoustic transmission through the sandwich shell. The principal advantage of the scheme is the ability to accurately model composite panels of arbitrary curvature at a rational computational time.

Acoustic Emission Signal Due to Fiber Break and Fiber Matrix Debonding in Model Composite: A Computational Study

Acoustic emission monitoring is a useful technique to deal with detection and identification of damage in composite materials. Over the last few years, identification of damage through intelligent signal processing was particularly emphasized. Data-driven models are developed to predict the remaining useful lifetime. Finite elements modeling (FEM) was used to simulate AE signals due to fiber break and fiber/matrix debonding in a model carbon fiber composite and thereby better understand the AE signals and physical phenomena. This paper presents a computational analysis of AE waveforms resulting from fiber break and fiber/matrix debonding. The objective of this research was to compare the AE signals from a validated fiber break simulation to the AE signals obtained from fiber/matrix debonding and fiber break obtained in several media and to discuss the capability to detect and identify each source.

Statistical tests for heterogeneity of clusters and composite endpoints

Clinical trials and epidemiological cohort studies often group similar diseases together into a composite endpoint, to increase statistical power. A common example is to use a 3-digit code from the International Classification of Diseases (ICD), to represent a collection of several 4-digit coded diseases. More recently, data-driven studies are using associations with risk factors to cluster diseases, leading this article to reconsider the assumptions needed to study a composite endpoint of several potentially distinct diseases. An important assumption is that the (possibly multivariate) associations are the same for all diseases in a composite endpoint (not heterogeneous). Therefore, multivariate measures of heterogeneity from meta analysis are considered, including multi-variate versions of the I2 statistic and Cochran's Q statistic. Whereas meta-analysis offers tools to test heterogeneity of clustering studies, clustering models suggest an alternative heterogeneity test, of whether data are better described by one, or more, clusters of elements with the same mean. The assumptions needed to model composite endpoints with a proportional hazards model are also considered. It is found that the model can fail if one or more diseases in the composite endpoint have different associations. Tests of the proportional hazards assumption can help identify when this occurs. It is emphasised that in multi-stage diseases such as cancer, some germline genetic variants can strongly modify the baseline hazard function and cannot be adjusted for, but must instead be used to stratify the data.

Automated detection and quantification of transverse cracks on woven composites

Various loading conditions—cyclic, quasi-static, and dynamic—can induce transverse matrix cracks in cross-ply and woven composite structures. Identification and quantification of this damage on a composite’s surface can provide valuable information on the overall damage state of the structure. This work seeks to develop automated methods for identifying and quantifying transverse matrix crack damage on the surface of composites. To this end, model plain weave glass–epoxy composite specimens were developed that were consistent in geometry and manufacturing process and for which the loading conditions and resulting damage quantity and damage mode could be controlled. High-resolution images (80 megapixel) were captured of the model composite specimen surfaces. These images were then subjected to a manual transverse crack identification method, which established a control with known quantity and spatial location of transverse cracks. Two automated methods were developed to identify and quantify transverse cracks. The first used 8-bit (256 shades of gray) images, an ImageJ preprocessing step, and finally used MATLAB to identify the damage. The second used 16-bit (65,536 shades of gray) images processed directly by MATLAB (no ImageJ preprocessing) to identify the damage. It was found that the 8-bit method more accurately assessed the quantity of transverse cracks because the preprocessing step reduced error-causing high-contrast artifacts (e.g., reflections, composite material inconsistencies, dirt, and ink/marks). Finally, binned scatterplot maps indicating damage quantity and spatial location were created to provide at-a-glance assessment of composite damage condition.

Conductive Regenerated Cellulose Fibers for Multi-Functional Composites: Mechanical and Structural Investigation

Regenerated cellulose fibers coated with copper via electroless plating process are investigated for their mechanical properties, molecular structure changes, and suitability for use in sensing applications. Mechanical properties are evaluated in terms of tensile stiffness and strength of fiber tows before, during and after the plating process. The effect of the treatment on the molecular structure of fibers is investigated by measuring their thermal stability with differential scanning calorimetry and obtaining Raman spectra of fibers at different stages of the treatment. Results show that the last stage in the electroless process (the plating step) is the most detrimental, causing changes in fibers’ properties. Fibers seem to lose their structural integrity and develop surface defects that result in a substantial loss in their mechanical strength. However, repeating the process more than once or elongating the residence time in the plating bath does not show a further negative effect on the strength but contributes to the increase in the copper coating thickness, and, subsequently, the final stiffness of the tows. Monitoring the changes in resistance values with applied strain on a model composite made of these conductive tows show an excellent correlation between the increase in strain and increase in electrical resistance. These results indicate that these fibers show potential when combined with conventional composites of glass or carbon fibers as structure monitoring devices without largely affecting their mechanical performance.