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The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

This conceptual paper aims to develop the vision of a novel policy tool called the digital industrial cluster (DIC) thought for a future context shaped by digital economies and Industry 4.0. The DIC departs from the industrial cluster’s precepts and take them into the digital agglomeration phenomenon.

This research uses a discursive and theoretical approach. To create a clear picture related to the research topic, this paper reviews the literature on how industrial clusters deal with digital transformation. The review takes a particular interest in industrial clusters, information and communication technologies, Industry 4.0 and policy tools for digital agglomeration.

The research develops the vision of the DIC as a new policy tool, building its foundations on both industrial clusters and digital economies; furthermore, the DIC was compared with other policy tools based on digital agglomeration to understand the similarities, differences and advantages of the former. Finally, this paper discussed where the DIC could find its way toward implementation and what externalities could be expected from doing so.

This research could be useful for policymakers and cluster organizations looking to deploy policy tools that take advantage of industrial clusters and digital transformation. Besides, the theoretical foundations presented could lead researchers to empirical identification of early incarnations of the DIC.

This paper develops theoretical principles for a new policy tool that connects industrial clusters, digital agglomeration and Industry 4.0 for the first time.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li, Zeng, and Yu (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.

Past studies have paid extensive attention to investigate learning issues in individual, organizational and team contexts. Learning activities in the workplace, however, often occur in the interdisciplinary dyadic context. Hence, the purpose of this paper is to clarify what factors make interdisciplinary dyads lead to better learning effects. The authors attempted to clarify two major agendas: What knowledge factors (intellectual capitals) owned by the parties of the dyads can induce better learning effects? What contextual factors (learning tasks) can make better learning effects during the dyadic learning process?

To examine the previous agendas, the authors first conducted in-depth interviews and an exploratory survey so that a four-element dyadic intellectual capital (DIC) architecture was circumscribed: knowledge interdependency, expertise similarity, collaborative routines and mutual trust. Dyadic learning tasks were classified as exploitative and exploratory learning. The authors then sampled 248 respondents for the formal empirical survey to examine the relationship between DIC, dyadic learning tasks and knowledge sharing.

The statistical evidences confirmed the positive relationships between DIC and knowledge sharing, and the results also signified the previous relationships with a mediating effect from exploratory learning, while the effect of exploitative learning was not supported. Thus, only dyads (the two heads) with knowledge interdependency and mutual trust can make the exploratory task better than that of one individual (the one head).

This study provides a new insight into the learning issue with an interdisciplinary dyadic perspective to supplement the existing gap between academic efforts and learning practices in the workplace.

This work aims to identify the linear elastic orthotropic material paramters of Pinus pinaster Ait. wood, using full-field measurements and an inverse identification strategy based on the finite element (FE) method updating technique.

Compression tests are carried out under uniaxial and quasi-static loading conditions on wood specimens oriented on the radial-tangential (RT) plane, with different grain orientations. Full-field displacements and strains are measured using digital image correlation (DIC), which are then used as a reference in the identification procedure. A FE model is implemented assuming plane stress conditions, where wood is modelled as an orthotropic homogeneous material. Based on the numerical results, a synthetic image reconstruction scheme is implemented to synthetically deform the reference experimental image, which is then processed by DIC and further compared to the experimental results.

The results for both approaches were similar when both specimen configurations were used in a single run. However, when using the DIC-based FEMU approach with the on-axis configuration, the identified modulus of elasticity in the tangential direction and shear modulus are closer to the reference values.

This approach ensures a fair comparison between both sets of data since the full-field strain maps are obtained through the same filter and therefore have the same strain formulation, spatial resolution and data filtering. Firstly, the identification is performed using a single configuration, either the on-axis or the off-axis specimen. Secondly, the identification is carried out by merging data from both on-axis and off-axis configurations.

This paper aims to present an adaptation of digital image correlation (DIC) to the electronics industry for reliability assessment of electronic packages. Two case studies are presented: one for warpage measurement of a micro-electro-mechanical system (MEMS) package under different temperature conditions and the other for the measurement of transient displacements on the surface of a printed circuit board (PCB) assembly under free-fall drop conditions, which is for explaining the typical camera setup requirement and comparing among different boundary conditions by fastening methods of PCB.

DIC warpage measurements on a small device, such as a MEMS package, require a special speckle pattern. A new method for the creation of speckle patterns was developed using carbon coating and aluminum evaporative deposition. To measure the transient response on the surface of a PCB during a free-fall impact event, three-dimensional (3D) DIC was integrated with synchronized stereo-high speed cameras. This approach enables the measurement of full-field displacement on the PCB surface during a free-fall impact event, contrary to the localized information that is obtained by the conventional strain gage and accelerometer method.

The authors suggest the proposed patterning method to the small-sized microelectronics packages for DIC measurements. More generally, the idea is to have a thin layer of the dark or bright color of the background and then apply the white or black colored pattern, respectively, so that the surface has high contrast. Also, to achieve a proper size of speckles, this paper does not want to expose the measuring objects to high temperatures or pressures during the sample preparation stage. Of course, it seems a complicated process to use aluminum evaporator, carbon coater and electroformed mesh. However, the authors intend to share one of the solutions to achieve a proper pattern on such small-sized electronic packages.

3D DIC technique can be successfully implemented for the measurement of micro-scale deformations in small packages (such as MEMS) and for the analysis of dynamic deformation of complex PCB.

In this contribution, the purpose of this study is to extend the established social cost concept of humanitarian logistics into a preference-based bi-objective approach. The novel concept offers an efficient, robust and transparent way to consider the decision-maker’s preference. In principle, the proposed method applies to any multi-objective decision and is especially suitable for decisions with conflicting objectives and asymmetric impact.

The authors bypass the shortcomings of the traditional approach by introducing a normalized weighted sum approach. Within this approach, logistics and deprivation costs are normalized with the help of Nadir and Utopia points. The weighting factor represents the preference of a decision-maker toward emphasizing the reduction of one cost component. The authors apply the approach to a case study for hypothetical water contamination in the city of Berlin, in which authorities select distribution center (DiC) locations to supply water to beneficiaries.

The results of the case study highlight that the decisions generated by the approach are more consistent with the decision-makers preferences while enabling higher efficiency gains. Furthermore, it is possible to identify robust solutions, i.e. DiCs opened in each scenario. These locations can be the focal point of interest during disaster preparedness. Moreover, the introduced approach increases the transparency of the decision by highlighting the cost-deprivation trade-off, together with the Pareto-front.

For practical users, such as disaster control and civil protection authorities, this approach provides a transparent focus on the trade-off of their decision objectives. The case study highlights that it proves to be a powerful concept for multi-objective decisions in the domain of humanitarian logistics and for collaborative decision-making.

To the best of the knowledge, the present study is the first to include preferences in the cost-deprivation trade-off. Moreover, it highlights the promising option to use a weighted-sum approach to understand the decisions affected by this trade-off better and thereby, increase the transparency and quality of decision-making in disasters.

Deposit insurance is a key element in modern banking, as it guarantees the financial safety of deposits at depository financial institutions. It is necessary to have at least a dual fair premium rate system based on creditworthiness of financial institutions, as considering singular premium system for all banks will have moral hazard. This paper aims to develop theoretical and empirical model for calculating dual fair premium rates.

The definition of a fair premium rate in this paper is a rate that covers the operational expenditures of the deposit insuring organization, provides it with sufficient funds to enable it to pay a certain percentage share of deposit amounts to depositors in case of bank default and provides it with sufficient funds as precautionary reserves. To identify and classify healthier and more stable banks, the authors use credit rating methods that use two major dimensional reduction techniques. For forecasting nonperforming loans (NPLs), the authors develop a model that can capture both macro shocks and idiosyncratic shocks to financial institutions in a vector error correction model.

The response of NPLs/loans to macro shocks and idiosyncratic innovations shows that using a model with macro variables only is insufficient, as it is possible that under favorable economic conditions, some banks show negative performance due to bank level reasons such as mismanagement or vice versa. The final results show that deposit insurance premium rate needs to be vary based on banks’ creditworthiness.

The results provide interesting insight for financial authorities to set fair deposit insurance premium rate. A high premium rate reduces the capital adequacy of individual financial institutions, which endangers the stability of the financial system; a low premium rate will reduce the security of the financial system.

This paper aims to present the methodology and results of the experimental characterization of three-dimensional (3D) printed acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) parts utilizing digital image correlation (DIC).

Tensile and shear characterizations of ABS and PC 3D-printed parts were performed to determine the extent of anisotropy present in 3D-printed materials. Specimens were printed with varying raster ([+45/−45], [+30/−60], [+15/−75] and [0/90]) and build orientations (flat, on-edge and up-right) to determine the directional properties of the materials. Tensile and Iosipescu shear specimens were printed and loaded in a universal testing machine utilizing two-dimensional (2D) DIC to measure strain. The Poisson’s ratio, Young’s modulus, offset yield strength, tensile strength at yield, elongation at break, tensile stress at break and strain energy density were gathered for each tensile orientation combination. Shear modulus, offset yield strength and shear strength at yield values were collected for each shear combination.

Results indicated that raster and build orientations had negligible effects on the Young’s modulus or Poisson’s ratio in ABS tensile specimens. Shear modulus and shear offset yield strength varied by up to 33 per cent in ABS specimens, signifying that tensile properties are not indicative of shear properties. Raster orientation in the flat build samples reveals anisotropic behavior in PC specimens as the moduli and strengths varied by up to 20 per cent. Similar variations were observed in shear for PC. Changing the build orientation of PC specimens appeared to reveal a similar magnitude of variation in material properties.

This article tests tensile and shear specimens utilizing DIC, which has not been employed previously with 3D-printed specimens. The extensive shear testing conducted in this paper has not been previously attempted, and the results indicate the need for shear testing to understand the 3D-printed material behavior fully.