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Context-awareness has emerged as an important principle in the design of flexible business processes. The goal of the research is to develop an approach to extend context-aware business process modeling toward location-awareness. The purpose of this paper is to identify and conceptualize location-dependencies in process modeling.

This paper uses a pattern-based approach to identify location-dependency in process models. The authors design specifications for these patterns. The authors present illustrative examples and evaluate the identified patterns through a literature review of published process cases.

This paper introduces location-awareness as a new perspective to extend context-awareness in BPM research, by introducing relevant location concepts such as location-awareness and location-dependencies. The authors identify five basic location-dependent control-flow patterns that can be captured in process models. And the authors identify location-dependencies in several existing case studies of business processes.

The authors focus exclusively on the control-flow perspective of process models. Further work needs to extend the research to address location-dependencies in process data or resources. Further empirical work is needed to explore determinants and consequences of the modeling of location-dependencies.

As existing literature mostly focusses on the broad context of business process, location in process modeling still is treated as “second class citizen” in theory and in practice. This paper discusses the vital role of location-dependencies within business processes. The proposed five basic location-dependent control-flow patterns are novel and useful to explain location-dependency in business process models. They provide a conceptual basis for further exploration of location-awareness in the management of business processes.

The study aims to investigate the effects of pre-loading histories (pre-shearing and pre-consolidation) on the liquefaction behaviour of saturated loose sand via discrete element method (DEM) simulations.

The pre-shearing history is mimicked under drained conditions (triaxial compression) with different pre-shearing strain levels ranging from 0% to 2%. The pre-consolidation history is mimicked by increasing the isotropic compression to different levels ranging from 100 kPa to 300 kPa. The macroscopic and microscopic behaviours are analysed and compared.

Temporary liquefaction, or quasi-steady state (QSS), is observed in most samples. A higher pre-shearing or pre-consolidation level can provide higher liquefaction resistance. The ultimate state line is found to be unique and independent of the pre-loading histories in stress space. The Lade instability line prematurely predicts the onset of liquefaction for all samples, both with and without pre-loading histories. The redundancy index is an effective microscopic indicator to monitor liquefaction, and the onset of the liquefaction corresponds to the phase transition state where the value of redundancy index is one, which is true for all cases irrespective of the proportions of sliding contacts.

The liquefaction behaviour of granular materials still remains elusive, especially concerning the effects of pre-loading histories on soils. Furthermore, the investigation of the effects of pre-consolidation histories on undrained behaviour and its comparison to pre-sheared samples is rarely reported in the DEM literature.

The aim of this paper is to synthesize graphene-modified titanium dioxide (GR-TiO₂) nanorod arrays nanocomposite films, so that these can enhance the photocatalytic properties of titanium dioxide and overcome the problem of difficult separation and recovery of photocatalysts.

The GR-TiO₂ nanocomposite films were synthesized via hydrothermal method and spin-coating. The obtained samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), ultraviolet–visible (UV-Vis) diffuse reflectance spectrum and Raman spectrum. The photocatalytic performance of the GR-TiO₂ nanocomposite films for degrading Rhodamin B under ultraviolet (UV) was studied by a UV-Vis spectrophotometer. The photocatalytic enhancement mechanism of graphene was studied by photoelectrochemical analysis.

The introduction of graphene expanded the range of the optical response of TiO₂ nanorod arrays, improving the separation efficiency of the photogenerated electron-hole pairs, and thus dramatically increasing its photocatalytic performance.

A simple and novel way for synthesizing GR-TiO₂ nanocomposite films has enhanced the photocatalytic performance of TiO₂.

The photocatalyst synthesized is easy to separate and recycle in the process of photocatalytic reaction, so it is possible to achieve industrialization.

This paper aims to exploit shape-memory polymers as self-healable materials. The underlying mechanism involved the thermal transitions as well as the enrichment of the healing reagents and the closure of the crack surfaces due to shape recovery. The multi-stimuli-triggered shape memory composite was capable of self-healing under not only direct thermal but also electrical stimulations.

The shape memory epoxy polymer composites comprising the AgNWs and poly (ε-caprolactone) were fabricated by dry transfer process. The morphologies of the composites were investigated by the optical microscope and scanning electron microscopy (SEM). The electrical conduction and the Joule heating effect were measured. Furthermore, the healing efficiency under the different stimuli was calculated, whose dependence on the compositions was also discussed.

The AgNWs network maintained most of the pathways for the electrons transportation after the dry transfer process, leading to a superior conduction and flexibility. Consequently, the composites could trigger the healing within several minutes, as applied with relatively low voltages. It was found that the composites having more the AgNWs content had better electrically triggered performance, while 50 per cent poly (ε-caprolactone) content endowed the materials with max healing efficiency under thermal or electrical stimuli.

The findings may greatly benefit the application of the intelligent polymers in the fields of the multifunctional flexible electronics.

Most studies have by far emphasized on the direct thermal triggered cases. Herein, a novel, flexible and conductive shape memory-based composite, which was capable of self-healing under the thermal or electrical stimulations, has been proposed.

This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration.

There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process.

Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process.

The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts.

The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.

This study aims to establish the friction vibration model.

The friction vibration experiment was carried out on a pin disk friction tester. The causes of friction vibration are discussed, and the friction vibration model is established based on the energy method.

The experimental and simulation results show that the main cause of friction vibration is the nonlinear change of friction coefficient; degree of the friction vibration has a positive relationship with the friction relative velocity and normal contact positive pressure; the proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration.

The proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration.

The purpose of this paper is to perform a systematic review on the application of different multi-criteria decision-making (MCDM) methods in solving the site selection problem across multiple problem domains. The domains are energy generation, logistics, public services and retail facilities. This study aims to answer the following research questions: Which evaluating criteria were used for each site selection problem domain? Which MCDM methods were frequently applied in a particular site selection problem domain?

The goals of the systematic review were to identify the evaluating criteria as well as the MCDM method used for each problem domain. A total of 81 recent papers (2014–2018) including 32 papers published in conference proceedings and 49 journal articles from various databases including IEEE Xplore, PubMed, Springer, Taylor and Francis as well as ScienceDirect were evaluated.

This study has shown that site selection for energy generation facilities is the most active site selection problem domain, and that the analytic hierarchy process (AHP) method is the most commonly used MCDM method for site selection. For energy generation, the criteria which were most used were geographical elements, land use, cost and environmental impact. For logistics, frequently used criteria were geographical elements and distance, while for public services population density, supply and demand, geographical layout and cost were the criteria most used. Criteria useful for retail facilities were the size (space) of the store, demographics of the site, the site characteristics and rental of the site (cost).

This study is limited to reviewing papers which were published in the years 2014–2018 only, and only covers the domains of energy generation, logistics, public services and retail facilities.

MCDM is a viable tool to be used for solving the site selection problem across the domains of energy generation, logistics, public services and retail facilities. The usage of MCDM continues to be relevant as a complement to machine learning, even as data originating from embedded IoT devices in built environments becomes increasingly Big Data like.

Previous systematic review studies for MDCM and built environments have either focused on studying the MCDM techniques itself, or have focused on the application of MCDM for site selection in a single problem domain. In this study, a critical review of MCDM techniques used for site selection as well as the critical criteria used during the MCDM process of site selection was performed on four different built environment domains.