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In this paper, the authors offer a methodological discussion and examples of visual analysis processes. The purpose of this paper is to illustrate a data analysis method that the authors named layered textural analysis, which brought together images and texts in ways that changed existing and decontextualized understandings. The authors used layered textural analysis to interpret photo-narratives from a photo elicitation study about the development of intercultural sensitivity in US study abroad students.

Layered textural analysis, as carried out in this study, consisted of structural analysis of narratives present in interview text, visual analysis of the photos, and guiding questions related to the content and relationship of the photos and narratives.

Individual experiences, images, and texts reflect complex connections between matter and thought. The multilayered analysis led to complex understandings and representations of participants’ learning and interpretation of cultural differences, and allowed us to examine photo-narrative events and participants’ individual meaning-making processes.

Visual researchers rarely write about their processes of analysis in sufficient epistemological and methodological detail. Transparency about data analysis may inspire other scholars to experiment with data analysis approaches. The authors share layered textural analysis so that qualitative researchers can gain ideas for their own reflexive analytic techniques and to exemplify how multilayered analysis methods can change understandings of data and work against simplified knowledges.

The purpose of this paper is to investigate the impact of institutional changes in Bolivia (2005-2016) in the power structures within the headquarters (HQs) of a Brazilian energy multinational corporation (MNC) and its subsidiaries in Bolivia.

This investigation is informed by a postcolonial South–South perspective. The Brazilian and Bolivian managers were interviewed and drawing techniques were used to unveil hidden power relationships. To achieve the multilayered objective, a Lukesian power framework was integrated into the analysis.

Traces of a postcolonial relationship between Brazil and Bolivia were found, even though Brazil never colonized Bolivia. The power structure within this MNC’s HQ and subsidiaries reflects a postcolonial relationship: local staff members see the Brazilian MNC as the holder of power of resources, process and meaning. Finally, despite its colonizing role, Brazil is depicted as a savior, not an exploiter. Much to the authors’ surprise, the institutional changes in Bolivia – the nationalization of its oil and gas reserves and the declaration of a plurinational state – have not affected the power relationships within the Brazilian MNC.

The contribution to postcolonial investigations within the international business field was carried out in different ways: a review of EMNC literature was conducted in the study for a South–South postcolonial perspective; empirical data from a case within South America were added; a Lukesian power perspective was integrated into the analysis; and finally, drawing techniques were used to unveil hidden power relations.

Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.

In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.

In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.

In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.

Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.

Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.

Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

Pink ring is a ubiquitous problem arising during the manufacture of multilayer PCBs, being the manifestation of local delamination at the inner‐layer oxide interfaces around drilled holes and subsequent dissolution of the oxide during plating processes. Except in extreme cases, there is no evidence that the occurrence of pink ring identifies any in‐service reliability problem, but it is nevertheless a clear process indicator and is strictly monitored in statistical process control. The UK Printed Circuit Industry has carried out a collaborative research programme aimed at providing an understanding and a quantitative analysis of the pink ring condition. The research has advanced on two fronts: (i) an investigation into the micro‐mechanisms of the delamination and stress relief around drilled holes and subsequent rôles of the desmear and plating chemicals, and (ii) a statistical analysis of boards manufactured in a variety of ways, analysing the quantitative measurements of pink ring in terms of, for example, panel source, drill supplier, drill quality, drilling backing material, drilling chip rate, stack position, and panel entry/exit side.

The purpose of this paper is to calculate near field and far field scattering of SH waves by multiple multilayered anisotropic circular inclusions using parallel volume integral equation method (PVIEM) quantitatively.

The PVIEM is applied for the analysis of elastic wave scattering problems in an unbounded solid containing multiple multilayered anisotropic circular inclusions. It should be noted that this numerical method does not require the use of the Green’s function for the inclusion – only the Green’s function for the unbounded isotropic matrix is needed. This method can also be applied to solve general elastodynamic problems involving inhomogeneous and/or anisotropic inclusions whose shape and number are arbitrary.

A detailed analysis of the SH wave scattering problem is presented for multiple multilayered orthotropic circular inclusions. Numerical results are presented for the displacement fields at the interfaces and the far field scattering patterns for square and hexagonal packing arrays of multilayered circular inclusions in a broad frequency range of practical interest.

To the best of the authors’ knowledge, the solution for scattering of SH waves by multiple multilayered anisotropic circular inclusions in an unbounded isotropic matrix is not currently available in the literature. However, in this paper, calculation of displacements on interfaces and far field scattering patterns of multiple multilayered anisotropic circular inclusions using PVIEM as a pioneer of numerical modeling enables us to investigate the effects of single/multiple scattering, fiber packing type, fiber volume fraction, single/multiple layer(s), the multilayer’s geometry, isotropy/anisotropy and softness/hardness.

Based on the data obtained from a questionnaire of 595 people, the authors explore the relative importance of consumers, checking whether socioeconomic variables influence their centrality, detecting the communities within the network to which they belong, identifying consumption patterns and checking whether there is any relationship between co-marketing and consumer choices.

A multilayer network is created from data collected through a consumer survey to identify customers’ choices in seven different markets. The authors focus the analysis on a smaller kinship and cohabitation network and apply the LART network community detection algorithm. To verify the association between consumers’ centrality and variables related to their respective socioeconomic profile, the authors develop an econometric model to measure their impact on consumer’s degree centrality.

Based on 595 responses analysing individual consumers, the authors find out which consumers invest and which variables influence consumers’ centrality. Using a smaller sample of 70 consumers for whom they know kinship and cohabitation relationships, the authors detect communities with the same consumption patterns and verify that this may be an adequate way to establish co-marketing strategies.

Network analysis has become a widely used technique in the extraction of knowledge on consumers. This paper’s main (and novel) contribution lies in providing a greater understanding on how multilayer networks represent hidden databases with potential knowledge to be considered in business decisions. Centrality and community detection are crucial measures in network science which enable customers with the highest potential value to be identified in a network. Customers are increasingly seen as multidimensional, considering their preferences in various markets.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

The induction of geological disasters is predominantly influenced by the dynamic evolution of the stress and plastic zones of the multilayer rock formations surrounding deep-rock roadways, and the behaviours and mechanisms of high in situ stress are key scientific issues related to deep-resource exploitation. The stress environment of deep resources is more complex owing to the influence of several geological factors, such as tectonic movements and landforms. Therefore, in practical engineering, the in situ stress field is in a complex anisotropic three-dimensional state, which may change the deformation and failure law of the surrounding rock. The purpose of this study is to investigate the tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses.

Based on data from the Yangquan Coal Mine, China, a finite difference model was established, and the elastic-plastic constitutive model and element deletion technology designed in the study were analysed. Gradual tunnelling along the roof and floor of the coal seam was used in the model, which predicted the impact tendency, and compared the results with the impact tendency report to verify the validity of the model. The evolutions of the stress field and plastic zone of the coal roadway in different stress fields were studied by modifying the maximum horizontal in situ stress, minimum horizontal in situ stress and lateral pressure coefficient.

The results shown that the in situ stress influenced the stress distribution and plastic zone of the surrounding rock. With an increase in the minimum horizontal in situ stress, the vertical in situ stress release area of the roof surrounding rock slowly decreased; the area of vertical in situ stress concentration area of the deep surrounding rock on roadway sides decreased, increased and decreased by turn; the area of roof now-shear failure area first increased and then decreased. With an increase in the lateral pressure coefficient, the area of the horizontal in situ stress release area of the surrounding rock increased; the area of vertical in situ stress release area of the roof and floor surrounding rock first decreased and then increased; the area of deep stress concentration area of roadway sides decreased; and the plastic area of the surrounding rock and the area of now-shear failure first decreased and then increased.

The results obtained in this study are based on actual cases and reveal the evolution law of the disturbing stress and plastic zone of multilayer surrounding rock caused by three-dimensional in situ stress during the excavation of deep rock roadways, which can provide a practical reference for the extraction of deep resources.

During recent years, the PCB industry has become more aware of the use of analytical test techniques to aid in materials testing. Repeatable quality of base materials, together with the monitoring of the production process and the finished product, has caused supplier and fabricator to consider existing methods and to see whether modern instrument techniques can improve such tests. During meetings such as Internepcon and Productronica, the author has presented developments by DuPont which have used Thermal Analysis techniques to evaluate the materials and processes involved in the manufacture of a multilayer printed circuit board. The purpose of this paper will be to give an up to date review of the work. It will discuss the successes to date and will show where modifications to experimental methods have been made to give more practical data. Specific test methods currently used in the industry will be discussed and recommendations will be made showing Thermal Analysis techniques may offer a more objective test as well as giving the user time savings and a reduction in manpower through increased instrument productivity and versatility.

To present a method to model woven fibre reinforced metal matrix composite for multilayer circuit boards.

This paper presents a hybrid modelling method to model multilayer multimaterial composites with the combination of metallic and woven composite plies. Firstly, 3D unit cells of woven composite are idealized as orthotropic plies, while metallic layers are taken as isotropic plies. Secondly, the idealized composite plies and metallic plies are modelled into a 2D multilayer finite element (FE). Lastly, scalar damage parameters are used for damage modelling.

Based on this method, static and dynamic analysis of multilayer composite can be performed at both micro and board levels. Meanwhile, the hybrid model illustrates a good agreement with the experimental results and good computational efficiency required for FE simulation. Conceptually, this study is aimed to provide an efficient damage modelling techniques for laminate composites and flexible modelling methodology for further development of new composite material systems.

Damaging testing and simulation is not involved, although damaging modelling method is presented.

This model has high flexibility and efficiency: the micro structure and properties of reinforced fibres, polymer matrix and metallic plies can be changed conveniently in 3D mechanics unit‐cell model; the 2D structure of geometry model provides a high‐computational efficiency in the numerical simulation. The presented work also provides the damage modelling methods, multi‐linear damage law and scalar damage parameters, to simulate damage behaviour after impact.