Composite w. Quiet Barrier™ Specialty Composite: The Ultimate Soundproofing Solution

How does Quiet Barrier™ Specialty Composite work. What are its key features. Where can it be applied. How does it compare to other soundproofing materials. What are its limitations and considerations.

Understanding the Triple Soundproofing Function of Quiet Barrier™ Specialty Composite

Quiet Barrier™ Specialty Composite is an innovative soundproofing solution that employs a triple-action approach to noise reduction. This unique design combines multiple layers to effectively block, absorb, and dampen sound waves.

• Sound Blocking: The Quiet Barrier™ layer uses its mass to block incoming sound waves.
• Sound Dampening: The flexible nature of the barrier layer helps to dampen sound energy.
• Sound Absorption: A thick layer of acoustic foam reduces ambient reflected noise.
• Decoupling: A thinner foam layer structurally isolates the barrier, reducing vibration transmission.

How does this multi-layered approach enhance soundproofing performance? By addressing different aspects of sound transmission, Quiet Barrier™ Specialty Composite provides comprehensive noise control that outperforms single-function materials.

Key Features and Benefits of Quiet Barrier™ Specialty Composite

Quiet Barrier™ Specialty Composite offers several unique advantages that set it apart from traditional soundproofing materials:

Heat Resistance and Reflectivity

The product features a fiber-reinforced Mylar® facing, which serves a dual purpose:

• Reflects high-frequency sound waves
• Reflects heat back towards its source

This makes it particularly effective in high-temperature environments where other soundproofing materials might fail.

Durability and Longevity

Quiet Barrier™ Specialty Composite is built to last, with several features contributing to its durability:

• Cleanable, tear-resistant Mylar surface
• Embedded fibers prevent further tearing if punctured
• Thermally bonded layers for superior adhesion

How does this enhanced durability benefit users? It ensures long-lasting performance, even in challenging industrial environments where exposure to oils, grease, or physical impacts is common.

Space-Efficient Design

With a thickness of only 1 1/4 inches, Quiet Barrier™ Specialty Composite offers effective soundproofing without sacrificing valuable space. This low profile makes it ideal for applications where space is at a premium, such as equipment enclosures or automotive interiors.

Easy Installation Options

Quiet Barrier™ Specialty Composite is available with an optional pressure-sensitive adhesive (PSA) backing, allowing for simple “peel and stick” installation. This feature significantly reduces installation time and complexity, making it accessible for both professional and DIY applications.

Applications Across Various Industries

The versatility of Quiet Barrier™ Specialty Composite makes it suitable for a wide range of applications across multiple industries:

Construction and HVAC

• Air conditioning unit enclosures
• Computer and server rooms
• Compressor enclosures
• Equipment rooms
• Generator enclosures
• HVAC cabinets
• Industrial facilities

Automotive and Heavy Equipment

• Hood and trunk liners
• Engine compartment liners
• Cab liners
• Floor liners

Marine Industry

• Engine compartment liners
• Hatch cover liners
• Hull liners

How does this wide range of applications demonstrate the product’s versatility? It showcases the ability of Quiet Barrier™ Specialty Composite to address noise issues in diverse environments, from industrial settings to transportation and marine applications.

Customization Options and Availability

Quiet Barrier™ Specialty Composite offers flexibility in terms of customization and ordering:

• In-stock availability for standard sizes and configurations
• Same-day shipping for most products
• Custom profiles, shapes, and sizes available upon request
• Minimum order quantities apply for custom options

How does this combination of stock availability and customization options benefit customers? It allows for quick delivery of standard products while also providing the flexibility to meet specific project requirements through customization.

Important Considerations and Limitations

While Quiet Barrier™ Specialty Composite offers numerous benefits, there are important factors to consider:

Flammability Ratings

The product contains polyurethane foam with a flammability rating of UL94 HF-1. Users should verify that this rating complies with local building codes for exposed materials before installation.

Polyurethane Foam Warning

All polyurethane foams, including those used in Quiet Barrier™ Specialty Composite, are combustible. Proper precautions should be taken:

• Avoid exposure to flame sources
• Be aware of potential rapid flame spread, intense heat, and toxic gas production if ignited
• Inform employees and customers of these risks

Why is it crucial to understand these flammability considerations? Awareness of these factors ensures safe application and compliance with local regulations, preventing potential hazards in the event of a fire.

Surface Preparation

For optimal adhesion and performance, all surfaces must be dry, clean, and free from grease or oil before applying Quiet Barrier™ Specialty Composite.

Comparing Quiet Barrier™ Specialty Composite to Traditional Soundproofing Methods

To fully appreciate the benefits of Quiet Barrier™ Specialty Composite, it’s helpful to compare it with traditional soundproofing techniques:

Mass Loaded Vinyl (MLV)

MLV is a common soundproofing material, but it has limitations:

• Single-function (sound blocking only)
• Requires additional materials for sound absorption
• Less heat-resistant than Quiet Barrier™ Specialty Composite

Acoustic Foam Panels

While effective for sound absorption, acoustic foam panels have drawbacks:

• Limited sound blocking capabilities
• Less durable in industrial environments
• Often require more space for effective performance

Green Glue Compound

Green Glue is popular for its damping properties, but it differs from Quiet Barrier™ Specialty Composite in several ways:

• Requires sandwiching between rigid materials
• No inherent sound absorption properties
• More complex installation process

How does Quiet Barrier™ Specialty Composite address the limitations of these traditional methods? By combining multiple soundproofing functions in a single, durable product, it offers a more comprehensive and space-efficient solution.

Case Studies: Quiet Barrier™ Specialty Composite in Action

To illustrate the real-world effectiveness of Quiet Barrier™ Specialty Composite, let’s examine a few case studies:

Industrial Generator Enclosure

A manufacturing facility needed to reduce noise from a large backup generator:

• Challenge: High heat output and limited space around the generator
• Solution: Quiet Barrier™ Specialty Composite applied to enclosure walls
• Result: 15 dB noise reduction and improved heat management

Marine Engine Compartment

A luxury yacht owner wanted to reduce engine noise in the cabin:

• Challenge: Humid environment and exposure to fuel and oil
• Solution: Custom-cut Quiet Barrier™ Specialty Composite panels
• Result: 20 dB noise reduction and improved durability compared to previous solution

HVAC Equipment Room

An office building needed to reduce noise from rooftop HVAC equipment:

• Challenge: Limited headroom and high ambient temperatures
• Solution: Quiet Barrier™ Specialty Composite applied to ceiling and walls
• Result: 18 dB noise reduction and improved thermal efficiency

What do these case studies demonstrate about the versatility of Quiet Barrier™ Specialty Composite? They showcase its ability to perform effectively in diverse environments, addressing multiple challenges such as heat, space constraints, and harsh conditions.

Installation Tips and Best Practices

To maximize the performance of Quiet Barrier™ Specialty Composite, consider the following installation tips:

Surface Preparation

• Thoroughly clean and degrease all surfaces
• Ensure surfaces are completely dry before application
• Use a primer on porous surfaces for better adhesion

Cutting and Fitting

• Use sharp utility knives or scissors for clean cuts
• Measure twice, cut once to minimize waste
• Leave a small gap (1/8 inch) between panels for thermal expansion

Sealing and Finishing

• Use acoustic caulk to seal gaps between panels
• Apply strips of MLV tape over seams for added sound blocking
• Consider using corner beads for a polished look in visible areas

Safety Considerations

• Wear gloves and eye protection during installation
• Use respiratory protection if cutting or sanding in enclosed spaces
• Follow all local building codes and regulations

Why are these installation tips crucial for optimal performance? Proper installation ensures that Quiet Barrier™ Specialty Composite achieves its full potential in terms of sound reduction, durability, and aesthetics.

Future Developments and Innovations in Soundproofing Technology

As the field of acoustics continues to evolve, we can expect to see further innovations in soundproofing materials and techniques. Some potential areas of development include:

Advanced Composite Materials

Future soundproofing solutions may incorporate:

• Nano-engineered materials for enhanced sound absorption
• Self-healing polymers for improved durability
• Bio-based composites for eco-friendly soundproofing

Smart Soundproofing Systems

Integration of technology could lead to:

• Active noise control systems embedded in soundproofing materials
• Adaptive materials that adjust their properties based on ambient noise levels
• IoT-connected soundproofing for real-time monitoring and optimization

Multifunctional Soundproofing Solutions

Future products may offer additional benefits beyond noise reduction:

• Soundproofing materials with enhanced thermal insulation properties
• Antimicrobial soundproofing for healthcare and food processing environments
• Energy-harvesting soundproofing that converts sound waves into usable electricity

How might these future developments build upon the foundation laid by products like Quiet Barrier™ Specialty Composite? They could potentially offer even more comprehensive solutions that address multiple environmental challenges simultaneously, further improving comfort, efficiency, and sustainability in various applications.

As we look to the future of soundproofing technology, it’s clear that innovations like Quiet Barrier™ Specialty Composite are just the beginning. The ongoing pursuit of quieter, more comfortable environments will continue to drive advancements in materials science and acoustic engineering, promising even more effective and versatile solutions in the years to come.

Quiet Barrier™ Specialty Composite | Soundproof Cow

Triple Soundproofing Function — The mass of the Quiet Barrier™ layer blocks sound while its flexible nature dampens sound energy. The thicker of the two layers of Acoustic foam acts as a sound absorber, decreasing ambient reflected noise. The thinner layer of acoustic foam acts as a decoupler, structurally isolating the heavy barrier layer from the construction assembly, ultimately decreasing vibration and increasing the sound blocking performance.

Heat Resistant — Quiet Barrier™ Specialty Composite has a fiber-reinforced Mylar® facing that not only reflects high frequencies but also reflects heat back towards the source. This product is our best performing sound blocking material in exposed locations with above-average temperatures.

Durable — The Mylar facing provides a cleanable, tear-resistant surface for applications that may come in contact with oils or grease. Once punctured, the embedded fibers will prevent additional tearing or damage. The acoustic foam and barrier layers are thermally bonded, providing a much stronger, longer-lasting bond over traditional adhesives.

Low Profile — Quiet Barrier™ Specialty Composite has a thickness of 1 1/4 inch, saving valuable square footage. Quiet Barrier™ Specialty Composite is used in many equipment enclosures where space is tight.

Ease of Installation — Quiet Barrier™ soundproofing composite is available with an optional “peel and stick” pressure-sensitive adhesive (PSA). Simply peel the silicone coated paper backing and place the product where it is needed.

In-stock — We stock a large amount of Quiet Barrier™ Specialty Composite in all advertised sizes and configurations. Most products are shipped the same day they are ordered and arrive at your project in three business days.

Customization — We will create a custom Quiet Barrier™ Specialty Composite for your application. Custom profiles, shapes and sizes can be quoted upon request. For custom Quiet Barrier™ Specialty Composites there are minimum order quantities, please contact our Acoustic Consultants for more information!

Flammability — Quiet Barrier™ Specialty Composite contains polyurethane foam that has a flammability rating of UL94 HF-1. Before choosing this product, be sure the flammability rating passes your local building code for exposed materials.

Sound Test

See how our Quiet Barrier™ Specialty Composite produced great results for a happy customer!

Product Availability

Also available in Quiet Barrier™ Specialty Composite

Special Order Sizes and Thicknesses are available.

For details, call 1-866-949-9COW.

Details

Construction Industry:

• Air Conditioning Unit Enclosures
• Computer and Server Rooms
• Compressor Enclosures
• Equipment Rooms and Enclosures
• Generator Enclosures
• HVAC Cabinets
• Industrial Facilities

Automotive Industry:

• Hood and Trunk Liners in Automobiles

Heavy Equipment Industry:

• Engine Compartment liners
• Cab Liners
• Floor liners
• Hood and Trunk Liners

Marine Industry:

• Engine Compartment liners
• Hatch Cover liners
• Hull Liners

Polyurethane Foam Warning

All Polyurethane Foams, including Combustion Modified Foams, will burn. Do not expose to any flame source. Once ignited, they can produce rapid flame spread, intense heat, dense smoke and toxic gases causing death. Warnings should be given to your employees and or customers. Test data does not necessarily reflect a foams performance under actual fire conditions. Before purchasing any foam, be sure to check with your local code officials to confirm the required flammability rating of exposed materials for your area.

Cautions and Limitations

Before applying this product all surfaces must be dry and clean, free of any grease or oil.

Frontiers | Confirmatory Composite Analysis

1. Introduction

Structural equation modeling with latent variables (SEM) comprises confirmatory factor analysis (CFA) and path analysis, thus combining methodological developments from different disciplines such as psychology, sociology, and economics, while covering a broad variety of traditional multivariate statistical procedures (Bollen, 1989; Muthén, 2002). It is capable of expressing theoretical concepts by means of multiple observable indicators to connect them via the structural model as well as to account for measurement error. Since SEM allows for statistical testing of the estimated parameters and even entire models, it is an outstanding tool for confirmatory purposes such as for assessing construct validity (Markus and Borsboom, 2013) or for establishing measurement invariance (Van de Schoot et al., 2012). Apart from the original maximum likelihood estimator, robust versions and a number of alternative estimators were also introduced to encounter violations of the original assumptions in empirical work, such as the asymptotic distribution free (Browne, 1984) or the two-stage least squares (2SLS) estimator (Bollen, 2001). Over time, the initial model has been continuously improved upon to account for more complex theories. Consequently, SEM is able to deal with categorical (Muthén, 1984) as well as longitudinal data (Little, 2013) and can be used to model non-linear relationships between the constructs (Klein and Moosbrugger, 2000).

Researchers across many streams of science appreciate SEM’s versatility as well as its ability to test common factor models. In particular, in the behavioral and social sciences, SEM enjoys wide popularity, e.g., in marketing (Bagozzi and Yi, 1988; Steenkamp and Baumgartner, 2000), psychology (MacCallum and Austin, 2000), communication science (Holbert and Stephenson, 2002), operations management (Shah and Goldstein, 2006), and information systems (Gefen et al., 2011),—to name a few. Additionally, beyond the realm of behavioral and social sciences, researchers have acknowledged the capabilities of SEM, such as in construction research (Xiong et al., 2015) or neurosciences (McIntosh and Gonzalez-Lima, 1994).

Over the last decades, the operationalization of the theoretical concept and the common factor has become more and more conflated such that hardly any distinction is made between the terms (Rigdon, 2012). Although the common factor model has demonstrated its usefulness for concepts of behavioral research such as traits and attitudes, the limitation of SEM to the factor model is unfortunate because many disciplines besides and even within social and behavioral sciences do not exclusively deal with behavioral concepts, but also with design concepts (so-called artifacts) and their interplay with behavioral concepts. For example Psychiatry: on the one hand it examines clinical relevant behavior to understand mental disorder, but on the other hand it also aims at developing mental disorder treatments (Kirmayer and Crafa, 2014). Table 1 displays further examples of disciplines investigating behavioral concepts and artifacts.

Table 1. Examples of behavioral concepts and artifacts across several disciplines.

Typically, the common factor model is used to operationalize behavioral concepts, because it is well matched with the general understanding of measurement (Sobel, 1997). It assumes that each observable indicator is a manifestation of the underlying concept that is regarded as their common cause (Reichenbach, 1956), and therefore fully explains the covariation among its indicators. However, for artifacts the idea of measurement is unrewarding as they are rather constructed to fulfill a certain purpose. To account for the constructivist character of the artifact, the composite has been recently suggested for its operationalization in SEM (Henseler, 2017). A composite is a weighted linear combination of observable indicators, and therefore in contrast to the common factor model, the indicators do not necessarily share a common cause.

At present, the validity of composite models cannot be systematically assessed. Current approaches are limited to assessing the indicators’ collinearity (Diamantopoulos and Winklhofer, 2001) and their relations to other variables in the model (Bagozzi, 1994). A rigorous test of composite models in analogy to CFA does not exist so far. Not only does this situation limit the progress of composite models, it also represents an unnecessary weakness of SEM as its application is mainly limited to behavioral concepts. For this reason, we introduce confirmatory composite analysis (CCA) wherein the concept, i.e., the artifact, under investigation is modeled as a composite. In this way, we make SEM become accessible to a broader audience. We show that the composite model relaxes some of the restrictions imposed by the common factor model. However, it still provides testable constraints, which makes CCA a full-fledged method for confirmatory purposes. In general, it involves the same steps as CFA or SEM, without assuming that the underlying concept is necessarily modeled as a common factor.

While there is no exact instruction on how to apply SEM, a general consensus exists that SEM and CFA comprise at least the following four steps: model specification, model identification, model estimation, and model assessment (e.g., Schumacker and Lomax, 2009, Chap. 4). To be in line with this proceeding, the remainder of the paper is structured as follows: Section 2 introduces the composite model providing the theoretical foundation for the CCA and how the same can be specified; Section 3 considers the issue of identification in CCA and states the assumptions as being necessary to guarantee the unique solvability of the composite model; Section 4 presents one approach that can be used to estimate the model parameters in the framework of CCA; Section 5 provides a test for the overall model fit to assess how well the estimated model fits the observed data; Section 6 assesses the performance of this test in terms of a Monte Carlo simulation and presents the results; and finally, the last section discusses the results and gives an outlook for future research. A brief example on how to estimate and assess a composite model within the statistical programming environment R is provided in the Supplementary Material.

2. Specifying Composite Models

Composites have a long tradition in multivariate data analysis (Pearson, 1901). Originally, they are the outcome of dimension reduction techniques, i.e., the mapping of the data to a lower dimensional space. In this respect, they are designed to capture the most important characteristics of the data as efficiently as possible. Apart from dimension reduction, composites can serve as proxies for concepts (MacCallum and Browne, 1993). In marketing research, Fornell and Bookstein (1982) recognized that certain concepts like marketing mix or population change are not appropriately modeled by common factors and instead employed a composite to operationalize these concepts. In the recent past, more and more researchers recognized composites as a legitimate approach to operationalize concepts, e.g., in marketing science (Diamantopoulos and Winklhofer, 2001; Rossiter, 2002), business research (Diamantopoulos, 2008), environmental science (Grace and Bollen, 2008), and in design research (Henseler, 2017).

In social and behavioral sciences, concepts are often understood as ontological entities such as abilities or attitudes, which rests on the assumption that the concept of interest exists in nature, regardless of whether it is the subject of scientific examination. Researchers follow a positivist research paradigm assuming that existing concepts can be measured.

In contrast, design concepts can be conceived as artifacts, i.e., objects designed to serve explicit goal(s) (Simon, 1969). Hence, they are inextricably linked to purposefulness, i.e., teleology (Horvath, 2004; Baskerville and Pries-Heje, 2010; Møller et al., 2012). This way of thinking has its origin in constructivist epistemology. The epistemological distinction between the ontological and constructivist nature of concepts has important implications when modeling the causal relationships among the concepts and their relationships to the observable indicators.

To operationalize behavioral concepts, the common factor model is typically used. It seeks to explore whether a certain concept exists by testing if collected measures of a concept are consistent with the assumed nature of that concept. It is based on the principle of common cause (Reichenbach, 1956), and therefore assumes that all covariation within a block of indicators can be fully explained by the underlying concept. On the contrary, the composite model can be used to model artifacts as a linear combination of observable indicators. In doing so, it is more pragmatic in the sense that it examines whether a built artifact is useful at all. Figure 1 summarizes the differences between behavioral concepts and artifacts and their operationalization in SEM.

Figure 1. Two types of concepts: behavioral concepts vs. artifacts.

In the following part, we present the theoretical foundation of the composite model. Although the formal development of the composite model and the composite factor model (Henseler et al., 2014), were already laid out by Dijkstra (2013, 2015), it has not been put into a holistic framework yet. In the following, it is assumed that each artifact is modeled as a composite c_j with j = 1, …, J. By definition, a composite is completely determined by a unique block of K_j indicators, xj′=(xj1…xjKj), cj=wj′xj.

The weights of block j are included in the column vector w_j of length K_j. Usually, each weight vector is scaled to ensure that the composites have unit variance (see also Section 3). Here, we assume that each indicator is connected to only one composite. The theoretical covariance matrix Σ of the indicators can be expressed as a partitioned matrix as follows:

Σ=(Σ11Σ12…Σ1J Σ22…Σ2J  ⋱⋮   ΣJJ)    (1)

The intra-block covariance matrix Σ_jj of dimension K_j × K_j is unconstrained and captures the covariation between the indicators of block j; thus, this effectively allows the indicators of one block to freely covary. Moreover, it can be shown that the indicator covariance matrix is positive-definite if and only if the following two conditions hold: (i) all intra-block covariance matrices are positive-definite, and (ii) the covariance matrix of the composite is positive-definite (Dijkstra, 2015, 2017). The covariances between the indicators of block j and l are captured in the inter-block covariance matrix Σ_jl, with j ≠ l of dimension K_j × K_l. However, in contrast to the intra-block covariance matrix, the inter-block covariance matrix is constrained, since by assumption, the composites carry all information between the blocks:

Σjl=ρjlΣjjwjwl′Σll=ρjlλjλl′,    (2)

where ρjl=wj′Σjlwl equals the correlation between the composites c_j and c_l. The vector λ_j = Σ_jjw_j of length K_j contains the composite loadings, which are defined as the covariances between the composite c_j and the associated indicators x_j. Equation 2 is highly reminiscent of the corresponding equation where all concepts are modeled as common factors instead of composites. In a common factor model, the vector λ_j captures the covariances between the indicators and its connected common factor, and ρ_jl represents the correlation between common factor j and l. Hence, both models show the rank-one structure for the covariance matrices between two indicator blocks.

Although the intra-block covariance matrices of the indicators Σ_jj are not restricted, we emphasize that the composite model is still a model from the point of view of SEM. It assumes that all information between the indicators of two different blocks is conveyed by the composite(s), and therefore, it imposes rank-one restrictions on the inter-block covariance matrices of the indicators (see Equation 2). These restrictions can be exploited for testing the overall model fit (see Section 5). It is emphasized that the weights w_j producing these matrices are the same across all inter-block covariance matrices Σ_jl with l = 1, …, J and l ≠ j. Figure 2 illustrates an example of a composite model.

Figure 2. Example of a composite model.

The artifact under investigation is modeled as the composite c, illustrated by a hexagon, and the observable indicators are represented by squares. The unconstrained covariance σ₁₂ between the indicators of block x′=(x1x2) forming the composite is highlighted by a double-headed arrow.

The observable variables y and z do not form the composite. They are allowed to freely covary among each other as well as with the composite. For example, they can be regarded as antecedents or consequences of the modeled artifact.

To emphasize the difference between the composite model and the common factor model typically used in CFA, we depict the composite model as composite factor model (Dijkstra, 2013; Henseler et al., 2014). The composite factor model has the same model-implied indicator covariance matrix as the composite model, but the deduction of the model-implied covariances and the comparison to the common factor is more straightforward. Figure 3 shows the same model as Figure 2 but in terms of a composite factor representation.

Figure 3. Example of a composite model displayed as composite factor model.

The composite loading λ_i, i = 1, 2 captures the covariance between the indicator x_i and the composite c. In general, the error terms are included in the vector ϵ, explaining the variance of the indicators and the covariances between the indicators of one block, which are not explained by the composite factor. As the composite model does not restrict the covariances between the indicators of one block, the error terms are allowed to freely covary. The covariations among the error terms as well as their variances are captured in matrix Θ. The model-implied covariance matrix of the example composite model can be displayed as follows:

In comparison to the same model using a common factor instead of a composite, the composite model is less restrictive as it allows all error terms of one block to be correlated, which leads to a more general model (Henseler et al., 2014). In fact, the common factor model is always nested in the composite model since it uses the same restriction as the composite model; but additionally, it assumes that (some) covariances between the error terms of one block are restricted (usually to zero). Under certain conditions, it is possible to rescale the intra- and inter-block covariances of a composite model to match those of a common factor model (Dijkstra, 2013; Dijkstra and Henseler, 2015).

3. Identifying Composite Models

Like in SEM and CFA, model identification is an important issue in CCA. Since analysts can freely specify their models, it needs to be ensured that the model parameters have a unique solution (Bollen, 1989, Chap. 8). Therefore, model identification is necessary to obtain consistent parameter estimates and to reliably interpret them (Marcoulides and Chin, 2013).

In general, the following three states of model identification can be distinguished: under-identified, just-identified, and over-identified. An under-identified model, also known as not-identified model, offers several sets of parameters that are consistent with the model constraints, and thus, no unique solution for the model parameters exists. Therefore, only questionable conclusions can be drawn. In contrast, a just-identified model provides a unique solution for the model parameters and has the same number of free parameters as non-redundant elements of the indicator covariance matrix (degrees of freedom (df) are 0). In empirical analysis, such models cannot be used to evaluate the overall model fit since they perfectly fit the data. An over-identified model also has a unique solution; however, it provides more non-redundant elements of the indicator covariance matrix than model parameters (df > 0). This can be exploited in empirical studies for assessing the overall model fit, as these constraints should hold for a sample within the limits of sampling error if the model is valid.

A necessary condition for ensuring identification is to normalize each weight vector. In doing so, we assume that all composites are scaled to have a unit variance, wj′Σjjwj=1. Besides the scaling of the composite, each composite must be connected to at least one composite or one variable not forming a composite. As a result, at least one inter-block covariance matrix Σ_jl, l = 1, …, J with l ≠ j satisfies the rank-one condition. Along with the normalization of the weight vectors, all model parameters can be uniquely retrieved from the indicator covariance matrix since there is a non-zero inter-block covariance matrix for every loading vector. Otherwise, if a composite c_i is isolated in the nomological network, all inter-block covariances Σ_jl, l = 1, …, J with l ≠ j, belonging to this composite are of rank zero, and thus, the weights forming this composite cannot be uniquely retrieved. Although the non-isolation condition is required for identification, it also matches the idea of an artifact that is designed to fulfill a certain purpose. Without considering the artifact’s antecedents and/or consequences, the artifact’s purposefulness cannot be judged.

In the following part, we give a description on how the number of degrees of freedom is counted in case of the composite model. It is given by the difference between the number of non-redundant elements of the indicator population covariance matrix Σ and the number of free parameters in the model. The number of free model parameters is given by the number of covariances among the composites, the number of covariances between composites and indicators not forming a composite, the number of covariances among indicators not forming a composite, the number of non-redundant off-diagonal elements of each intra-block covariance matrix, and the number of weights. Since we fix composite variances to one, one weight of each block can be expressed by the remaining ones of this block. Hence, we regain as many degrees of freedom as fixed composite variances, i.e., as blocks in the model. Equation 4 summarizes the way of determining the number of degrees of freedom of a composite model.

df=number of non-redundant off-diagonal elements of the        indicator covariance matrix     −number of free correlations among the composites     −number of free covariances between the composites and        indicators not forming a composite     −number of covariances among the indicators not forming        a composite     −number of free non-redundant off-diagonal elements of        each intra-block covariance matrix     −number of weights     +number of blocks    (4)

To illustrate our approach to calculating the number of degrees of freedom, we consider the composite model presented in Figure 2. As described above, the model consists of four (standardized) observable variables; thus, the indicator correlation matrix has six non-redundant off-diagonal elements. The number of free model parameters is counted as follows: no correlations among the composites as the models consists of only one composite, two correlations between the composite and the observable variables not forming a composite (σ_yc and σ_cz), one correlation between the variables not forming a composite (σ_yz), one non-redundant off-diagonal of the intra-block correlation matrix (σ₁₂), and two weights (w₁ and w₂) minus one, the number of blocks. As a result, we obtain the number of degrees of freedom as follows: df = 6−0−2−1−1−2 + 1 = 1. Once identification of the composite model is ensured, in a next step the model can be estimated.

4. Estimating Composite Models

The existing literature provides various ways of constructing composites from blocks of indicators. The most common among them are principal component analysis (PCA, Pearson, 1901), linear discriminant analysis (LDA, Fisher, 1936), and (generalized) canonical correlation analysis ((G)CCA, Hotelling, 1936; Kettenring, 1971). All these approaches seek composites that “best” explain the data and can be regarded as prescriptions for dimension reduction (Dijkstra and Henseler, 2011). Further approaches are partial least squares path modeling (PLS-PM, Wold, 1975), regularized general canonical correlation analysis (RGCCA, Tenenhaus and Tenenhaus, 2011), and generalized structural component analysis (GSCA, Hwang and Takane, 2004). The use of predefined weights is also possible.

We follow Dijkstra (2010) and apply GCCA in a first step to estimate the correlation between the composites. In the following part, we give a brief description of GCCA. The vector of indicators x of length K is split up into J subvectors x_j, so called blocks, each of dimension (K_j × 1) with j = 1, …, J. We assume that the indicators are standardized to have means of zero and unit variances. Moreover, each indicator is connected to one composite only. Hence, the correlation matrix of the indicators can be calculated as Σ = E(xx′) and the intra-block correlation matrix as Σjj=E(xjxj′). Moreover, the correlation matrix of the composites cj=xj′wj is calculated as follows: Σc=E(cc′). In general, GCCA chooses the weights to maximize the correlation between the composites. In doing so, GCCA offers the following options: sumcor, maxvar, ssqcor, minvar, and genvar.

In the following part, we use maxvar under the constraint that each composite has a unit variance, wj′Σjjwj=1, to estimate the weights, the composites, and the resulting composite correlations. In doing so, the weights are chosen to maximize the largest eigenvalue of the composite correlation matrix. Thus, the total variation of the composites is explained as well as possible by one underlying “principal component,” and the weights to form the composite c_j are calculated as follows (Kettenring, 1971):

wj=Σjj-12a~j/a~j′a~j.    (5)

The subvector a~j, of length J, corresponds to the largest eigenvalue of the matrix ΣD-12ΣΣD-12, where the matrix Σ_D, of dimension J × J, is a block-diagonal matrix containing the intra-block correlation matrices Σ_jj, j = 1, …, J on its diagonal. To obtain the estimates of the weights, the composites, and their correlations, the population matrix Σ is replaced by its empirical counterpart S.ij equals s_ij.

Since all distance measures considered are functions of the sample covariance matrix, a procedure proposed by Beran and Srivastava (1985) can be used to test the overall model fit: H₀ : Σ = Σ(θ). The reference distribution of the distance measures as well as the critical values are obtained from the transformed sample data as follows:

where the data matrix x of dimension (N × K) contains the N observations of all K indicators. This transformation ensures that the new dataset satisfies the null hypothesis; i.e., the sample covariance matrix of the transformed dataset equals the estimated model-implied covariance matrix. The reference distribution of the distance measures is obtained by bootstrapping from the transformed dataset. In doing so, the estimated distance based on the original dataset can be compared to the critical value from the reference distribution (typically the empirical 95% or 99% quantile) to decide whether the null hypothesis, H₀ : Σ = Σ(θ) is rejected (Bollen and Stine, 1992).

5.2. Fit Indices for Composite Models

In addition to the test of overall model fit, we provide some fit indices as measures of the overall model fit. In general, fit indices can indicate whether a model is misspecified by providing an absolute value of the misfit; however, we advise using them with caution as they are based on heuristic rules-of-thumb rather than statistical theory. Moreover, it is recommended to calculate the fit indices based on the indicator correlation matrix instead of the covariance matrix.

The standardized root mean square residual (SRMR) was already introduced as a measure of overall model fit (Henseler et al., 2014). As described above, it represents the average discrepancy between the sample and the model-implied indicator correlation matrix. Values below 0.10 and, following a more conservative view, below 0.08 indicate a good model fit (Hu and Bentler, 1998). However, these threshold values were proposed for common factor models and their usefulness for composite models needs to be investigated.

Furthermore, the normed fit index (NFI) is suggested as a measure of goodness of fit (Bentler and Bonett, 1980). It measures the relative discrepancy between the fit of the baseline model and the fit of the estimated model. In this context, a model where all indicators are assumed to be uncorrelated (the model-implied correlation matrix equals the unit matrix) can serve as a baseline model (Lohmöller, 1989, Chap. 2.4.4). To assess the fit of the baseline model and the estimated model, several measures can be used, e.g., the log likelihood function used in CFA or the geodesic distance. Values of the NFI close to one imply a good model fit. However, cut-off values still need to be determined.

Finally, we suggest considering the root mean square residual covariance of the outer residuals (RMS_theta) as a further fit index (Lohmöller, 1989). It is defined as the square root of the average residual correlations. Since the indicators of one block are allowed to be freely correlated, the residual correlations within a block should be excluded and only the residual correlations across the blocks should be taken into account during its calculation. Small values close to zero for the RMS_theta indicate a good model fit. However, threshold values still need to be determined.

6. A Monte Carlo Simulation

In order to assess our proposed procedure of statistically testing the overall model fit of composite models and to examine the behavior of the earlier presented discrepancy measures, we conduct a Monte Carlo simulation. In particular, we investigate the type I error rate (false positive rate) and the power, which are the most important characteristics of a statistical test. In designing the simulation, we choose a number of concepts used several times in the literature to examine the performance of fit indices and tests of overall model fit in CFA: a model containing two composites and a model containing three composites (Hu and Bentler, 1999; Heene et al., 2012). To investigate the power of the test procedure, we consider various misspecifications of these models. Figures 4 and 5 summarize the conditions investigated in our simulation study.

Figure 4. Simulation design for the model containing two composites.

Figure 5. Simulation design for the model containing three composites.

6.1. Model Containing Two Composites

All models containing two composites are estimated using the specification illustrated in the last column of Figure 4. The indicators x₁₁ to x₁₃ are specified to build composite c₁, while the remaining three indicators build composite c₂. Moreover, the composites are allowed to freely correlate. The parameters of interest are the correlation between the two composites, and the weights, w₁₁ to w₂₃. As column “Population model” of Figure 4 shows, we consider three types of population models with two composites.

6.1.1. Condition 1: No Misspecification

First, in order to examine whether the rejection rates of the test procedure are close to the predefined significance level in cases in which the null hypothesis is true, a population model is considered that has the same structure as the specified model. The correlation between the two composites is set to ρ = 0.3 and the composites are formed by its connected standardized indicators as follows: ci=xi′wi with i = 1, 2, where w1′=(0.60.20.4) and w2′=(0.40.20.6). All correlations between the indicators of one block are set to 0.5, which leads to the population correlation matrix given in Figure 4.

6.1.2. Condition 2: Confounded Indicators

The second condition is used to investigate whether the test procedure is capable of detecting misspecified models. It presents a situation where the researcher falsely assigns two indicators to wrong constructs. The correlation between the two composites and the weights are the same as in population model 1: ρ = 0.3, w1′=(0.60.20.4), and w2′=(0.40.20.6). However, in contrast to population model 1, the indicators x₁₃ and x₂₁ are interchanged. Moreover, the correlations among all indicators of one block are 0.5. The population correlation matrix of the second model is presented in Figure 4.

6.1.3. Condition 3: Unexplained Correlation

The third condition is chosen to further investigate the capabilities of the test procedure to detect misspecified models. It shows a situation where the correlation between the two indicators x₁₃ and x₂₁ is not fully explained by the two composites. As in the two previously presented population models, the two composites have a correlation of ρ = 0.3. The correlations among the indicators of one block are set to 0.5, and the weights for the construction of the composites are set to w1′=(0.60.20.4), and w2′=(0.40.20.6). The population correlation matrix of the indicators is presented in Figure 4.

6.2. Model Containing Three Composites

Furthermore, we investigate a more complex model consisting of three composites. Again, each composite is formed by three indicators, and the composites are allowed to freely covary. The column “Estimated model” of Figure 5 illustrates the specification to be estimated in case of three composites. We assume that the composites are built as follows: c1=x1′w1, c2=x2′w2, and c3=x3′w3. Again, we examine two different population models.

6.2.1. Condition 4: No Misspecification

The fourth condition is used to further investigate whether the rejection rates of the test procedure are close to the predefined significance level in cases in which the null hypothesis is true. Hence, the structure of the fourth population model matches the specified model. All composites are assumed to be freely correlated. In the population, the composite correlations are set to ρ₁₂ = 0.3, ρ₁₃ = 0.5, and ρ₂₃ = 0.4. Each composite is built by three indicators using the following population weights: w1′=(0.60.40.2), w2′=(0.30.50.6), and w3′=(0.40.50.5). The indicator correlations of each block can be read from Figure 5. The indicator correlation matrix of population model 4 is given in Figure 5.

6.2.2. Condition 5: Unexplained Correlation

In the fifth condition, we investigate a situation where the correlation between two indicators is not fully explained by the underlying composites, similar to what is observed in Condition 3. Consequently, population model 5 does not match the model to be estimated and is used to investigate the power of the overall model test. It equals population model 4 with the exception that the correlation between the indicators x₁₃ and x₂₁ is only partly explained by the composites. Since the original correlation between these indicators is 0.084, a correlation of 0.25 presents only a weak violation. The remaining model stays untouched. The population correlation matrix is illustrated in Figure 5.

6.3. Further Simulation Conditions and Expectations

To assess the quality of the proposed test of the overall model fit, we generate 10,000 standardized samples from the multivariate normal distribution having zero means and a covariance matrix according to the respective population model. Moreover, we vary the sample size from 50 to 1,450 observations (with increments of 100) and the significance level α from 1% to 10%. To obtain the reference distribution of the discrepancy measures considered, 200 bootstrap samples are drawn from the transformed and standardized dataset. Each dataset is used in the maxvar procedure to estimate the model parameters.

All simulations are conducted in the statistical programming environment R (R Core Team, 2016). The samples are drawn from the multivariate normal distribution using the mvrnorm function of the MASS packages (Venables and Ripley, 2002). The results for the test of overall model fit are obtained by user-written functions and the matrixpls package (Rönkkö, 2016).

Since population models 1 and 4 fit the respective specification, we expect rejection rates close to the predefined levels of significance α. Additionally, we expect that for an increasing sample size, the predefined significance level is kept with more precision. For population model 2, 3, and 5, much larger rejection rates are expected as these population models do not match the respective specification. Moreover, we expect that the power of the test to detect misspecifications would increase along with a larger sample size. Regarding the different discrepancy measures, we have no expectations, only that the squared Euclidean distance and the SRMR should lead to identical results. For standardized datasets, the only difference is a constant factor that does not affect the order of the observations in the reference distribution and, therefore, does not affect the decision about the null hypothesis.

6.4. Results

Figure 6 illustrates the rejection rates for population model 1 i.e., no misspecification. Besides the rejection rates, the figure also depicts the 95% confidence intervals (shaded area) constructed around the rejection rates to clarify whether a rejection rate is significantly different from the predefined significance level.

Figure 6. Rejection rates for population model 1.

First, as expected, the squared Euclidean distance (d_L) as well as the SRMR lead to identical results. The test using the squared Euclidean distance and the SRMR rejects the model somewhat too rarely in case of α = 10% and α = 5% respectively; however, for an increasing sample size, the rejection rates converge to the predefined significance level without reaching it. For the 1% significance level, a similar picture is observed; however, for larger sample sizes, the significance level is retained more often compared to the larger significance levels. In contrast, the test using the geodesic distance mostly rejects the model too often for the 5% and 10% significance level. However, the obtained rejection rates are less often significantly different from the predefined significance level compared to the same situation where the SRMR or the Euclidean distance is used. In case of α = 1% and sample sizes larger than n = 100, the test using the geodesic distance rejects the model significantly too often.

Figure 7 displays the rejection rates for population models 2 and 3. The horizontal line at 80% depicts the commonly recommended power for a statistical test (Cohen, 1988). For the two cases where the specification does not match the underlying data generating process, the test using the squared Euclidean distance as well as the SRMR has more power than the test using the geodesic distance, i.e., the test using former discrepancy measures rejects the wrong model more often. For model 2 (confounded indicators) the test produces higher or equal rejection rates compared to model 3 (unexplained correlation). Furthermore, as expected, the power decreases for an increasing level of significance and increases with increasing sample sizes.

Figure 7. Rejection rates for population model 2 and 3.

Figure 8 depicts the rejection rates for population model 4 and 5. Again, the 95% confidence intervals are illustrated for population model 4 (shaded area) matching the specification estimated. Considering population model 4 which matches the estimated model, the test leads to similar results for all three discrepancy measures. However, the rejection rate of the test using the geodesic distance converges faster to the predefined significance level, i.e., for smaller sample sizes n ≥ 100. Again, among the three discrepancy measures considered, the geodesic distance performs best in terms of keeping the significance level.

Figure 8. Rejection rates for population model 4 and 5.

As the extent of misspecification in population model 5 is minor, the test struggles to detect the model misspecification up to sample sizes n = 350, regardless of the discrepancy measure used. However, for sample sizes larger than 350 observations, the test detects the model misspecification satisfactorily. For sample sizes larger than 1,050 observations, the misspecification was identified in almost all cases regardless of the significance level and the discrepancy measure used. Again, this confirms the anticipated relationship between sample size and statistical power.

7. Discussion

We introduced the confirmatory composite analysis (CCA) as a full-fledged technique for confirmatory purposes that employs composites to model artifacts, i.e., design concepts. It overcomes current limitations in CFA and SEM and carries the spirit of CFA and SEM to research domains studying artifacts. Its application is appropriate in situations where the research goal is to examine whether an artifact is useful rather than to establish whether a certain concept exists. It follows the same steps usually applied in SEM and enables researchers to analyze a variety of situations, in particular, beyond the realm of social and behavioral sciences. Hence, CCA allows for dealing with research questions that could not be appropriately dealt with yet in the framework of CFA or more generally in SEM.

The results of the Monte Carlo simulation confirmed that CCA can be used for confirmatory purposes. They revealed that the bootstrap-based test, in combination with different discrepancy measures, can be used to statistically assess the overall model fit of the composite model. For specifications matching the population model, the rejection rates were in the acceptable range, i.e., close to the predefined significance level. Moreover, the results of the power analysis showed that the boostrap-based test can reliably detect misspecified models. However, caution is needed in case of small sample sizes where the rejection rates were low, which means that misspecified models were not reliably detected.

In future research, the usefulness of the composite model in empirical studies needs to be examined, accompanied and enhanced by simulation studies. In particular, the extensions outlined by Dijkstra (2017); to wit, interdependent systems of equations for the composites estimated by classical econometric methods (like 2SLS and three-stage least squares) warrant further analysis and scrutiny. Robustness with respect to non-normality and misspecification also appear to be relevant research topics. Additionally, devising ways to efficiently predict indicators and composites might be of particular interest (see for example the work by Shmueli et al., 2016).

Moreover, to contribute to the confirmatory character of CCA, we recommend further study of the performance and limitations of the proposed test procedure: consider more misspecifications and the ability of the test to reliably detect them, find further discrepancy measures and examine their performance, and investigate the behavior of the test under the violation of the normality assumption, similar as Nevitt and Hancock (2001) did for CFA. Finally, cut-off values for the fit indices need to be determined for CCA.

Author Contributions

FS conducted the literature review and wrote the majority of the paper (contribution: ca. 50%). JH initiated this paper and designed the simulation study (contribution: ca. 25%). TD proposed the composite model and developed the model fit test (contribution: ca. 25%).

Conflict of Interest Statement

JH acknowledges a financial interest in ADANCO and its distributor, Composite Modeling.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02541/full#supplementary-material

Footnotes

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Composite Fillings | Palo Alto, CA

A composite (tooth colored) filling is used to repair a tooth that is affected by decay, cracks, fractures, etc.  The decayed or affected portion of the tooth will be removed and then filled with a composite filling.

There are many types of filling materials available, each with their own advantages and disadvantages.  You and your dentist can discuss the best options for restoring your teeth.  Composite fillings, along with silver amalgam fillings, are the most widely used today.  Because composite fillings are tooth colored, they can be closely matched to the color of existing teeth, and are more aesthetically suited for use in front teeth or the more visible areas of the teeth.

As with most dental restorations, composite fillings are not permanent and may someday have to be replaced.  They are very durable, and will last many years, giving you a long lasting, beautiful smile.

Reasons for composite fillings:

How are composite fillings placed?

Composite fillings are usually placed in one appointment.  While the tooth is numb, your dentist will remove decay as necessary.  The space will then be thoroughly cleaned and carefully prepared before the new filling is placed.  If the decay was near the nerve of the tooth, a special medication will be applied for added protection.  The composite filling will then be precisely placed, shaped, and polished, restoring your tooth to its original shape and function.

It is normal to experience sensitivity to hot and cold when composite fillings are first placed, however this will subside shortly after your tooth acclimates to the new filling.

You will be given care instructions at the conclusion of your treatment.  Good oral hygiene practices, eating habits, and regular dental visits will aid in the life of your new fillings.

Composite Fillings | Williamsburg, VA

A composite (tooth colored) filling is used to repair a tooth that is affected by decay, cracks, fractures, etc.  The decayed or affected portion of the tooth will be removed and then filled with a composite filling.

There are many types of filling materials available, each with their own advantages and disadvantages.  You and your dentist can discuss the best options for restoring your teeth.  Composite fillings, along with silver amalgam fillings, are the most widely used today.  Because composite fillings are tooth colored, they can be closely matched to the color of existing teeth, and are more aesthetically suited for use in front teeth or more visible areas of the mouth.

As with most dental restorations, composite fillings are not permanent and may someday have to be replaced.  They are very durable and will last many years, giving you a long lasting, beautiful smile.

Reasons for composite fillings:

How are composite fillings placed?

Composite fillings are usually placed in one appointment.  While the tooth is numb, your dentist will remove decay as needed.  The space will then be thoroughly cleaned and carefully prepared before the new filling is placed.  If the decay was near the nerve of the tooth, a special medication will be applied for added protection.  The composite filling will then be precisely placed, shaped, and polished, restoring your tooth to its original shape and function.

It is normal to experience sensitivity to hot and cold when composite fillings are first placed, however this will subside shortly after your tooth acclimates to the new filling.

You will be given care instructions at the conclusion of your treatment.  Good oral hygiene practices, eating habits, and regular dental visits will aid in the life of your new fillings.

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Composite Resin Veneers

Do you find yourself being self-conscious about your smile because of discolored, chipped, or crooked teeth? You can instantly improve your smile with composite resin veneers! The direct composite resin veneer can be used to correct gapped, chipped, poorly shaped, and stained teeth. They can be affordably created chairside and can often be placed in as little as one visit.

Composite resin is a less expensive, tooth-colored material frequently used for veneers. While composite resin veneers might wear down quicker than their porcelain counterpart, they are easier to repair and cost less.

Although composite veneers typically have a shorter shelf life than porcelain veneers, they offer an affordable and convenient option that requires a far smaller investment of time and money. They are a particularly serviceable option for fixing cosmetic damage and righting issues with tooth size and shape.

For more information on veneers, continue reading or give W Dental Group in San Antonio, Texas a call today at 210-523-0000!

Single-Visit Composite Veneer Application Procedure

Composite veneers require less or no enamel removal. Your dentist may be able to make and place your veneers in one visit! If your composite veneer requires a lab, the steps are similar to porcelain veneers. The composite material is typically applied to the surface of the tooth and molded into shape by your dentist, thus giving you a timely yet effective smile makeover.

Composite Resin Veneers vs. Porcelain Veneers

Composite veneers have many advantages, namely being cost-effective while not sacrificing quality. Another advantage is timeliness: composites can typically be fabricated while you wait. Direct composite veneers are sculpted onto your teeth rather than at a lab. The tooth-shaded resin is directly applied to teeth where it can be shaped, sculpted, and polished to elicit a more natural, tooth-like appearance.

Arguably, the biggest benefit of composite veneers is reversibility. Minimal prep work is done to your teeth when it comes to composites, meaning that they are not permanently altered to such an extent that the composite material cannot be removed and replaced as needed.

While many patients prefer composite veneers to porcelain veneers, porcelain veneers, with proper care, can be a great option. Porcelain veneers typically last between 10-15 years, while composite resin dental veneers last around 4-8 years.

Composite Veneers FAQ

How to Get Composite Veneers Near Me?

If you are interested in getting composite veneers, our dental team is here for you! Our dentists can help you decide if composite veneers are the right option for you or determine if you would benefit more from porcelain veneers or one of our other cosmetic dentistry services. If you are interested in getting composite veneers, our dental team is here for you! Our dentists can help you decide if composite veneers are the right option for you or determine if you would benefit more from porcelain veneers or one of our other cosmetic dentistry services.

What are Composite Veneers?

Composite resin veneers are a porcelain alternative derived from translucent resin and meticulously sculpted and hardened by your dentist, typically in a single appointment. If you have worn, chipped, or damaged teeth, composite resin can be directly sculpted onto the teeth for exponential results. This is a separate procedure from a dental crown, dental bridge, or dental fillings. While they use the same materials, composite veneers are more functional than aesthetic treatments.

What Issues Can Composite Veneers Help With?

They’re used to correct discolored, gapped, crooked, or misshapen teeth. The veneer is placed over the imperfect teeth and given the appearance of straight, white, radiant teeth.

How Long Do Veneers Take?

Often referred to as “chair-side veneers,” composite veneers are designed by the dentist directly on your tooth, requiring a dentist’s technique combined with an artist’s eye. Typically, composite veneers can be placed in just one visit, but porcelain veneers usually require multiple visits before being placed.

How are Veneers Placed?

During your appointment, with your input, your dentist will select and sculpt a shade directly onto your teeth to build a veneer that best fits your smile. Then, a high-intensity light will be used to harden the composite. Finally, the composite resin will be shined and polished until it blends in with the natural, healthy appearance of the rest of your smile.

How Long Do Composite Veneers Last?

This depends on several factors, including the patient’s home care routine and how often they attend checkups (we recommend twice a year, at least). If you maintain a strict and comprehensive routine, your composite resin veneers can last at least 5 years and as long as 10!

How Do I Take Care of My Resin Veneers?

Their care requires the same care your natural teeth would require. Over time, they will be affected by darker liquids and hard objects such as ice and hard candy. Regular visits to the dentist every six months will increase the longevity of your veneers.

Why Do Patients Choose Composite Veneers?

Composite veneers are a great alternative to porcelain and can right many of your smile’s wrongs. Customers also like them because they are not time-consuming or pricey, they are minimally invasive, and because they give your teeth that lifelike sheen they once had!

What is Composite Resin?

Dental composite resin is a special material that is applied and sculpted to the tooth.  Once the optimal tooth shape has been achieved, it is cured with a special light and subsequently polished.

Composite materials are extremely advanced and are continuously being developed to produce better results for patients.

Composite veneers are a relatively new dental procedure, favored by recent dental generations interested in conserving the underlying tooth structure.

Are Composite Resin Veneers Right for You?

Consider composite veneers over porcelain veneers if you:

• Are looking for affordable porcelain veneer alternatives
• Want an improved smile for a wedding or other special event
• Want the convenience of a same-day placement
• Desire a less-invasive procedure to retain more tooth structure
• Would like veneers that can be repaired quickly and easily if damaged

Benefits of Composite Veneers

• A straighter, more attractive smile
• Brighter, natural-looking teeth
• A continuous, uninterrupted smile  
• A stronger tooth structure to prevent further damage

Looking for composite veneers near you? Give W Dental Group in San Antonio, TX a call today at 210-523-0000 for more information on composite resin dental veneers!

How to make a composite in minecraft

Specifications

Type – crafting ingredient;

Where to look – do it yourself;

Stackable – yes, 64 pieces in a stack.

Description and features

Minecraft composite material is used in a large number of crafting mod Industrial Craft 2, so you should immediately figure out how this ingredient is created, as well as in which assemblies and crafting it is involved.The fact is that it is with the help of this thing that you will eventually collect high-tech blocks, as well as objects. The creation cannot be called very difficult, although you will certainly have to work – to look for everything that is necessary for this.

We continue to deal with the question of how to make a composite in minecraft. You will need a compressor, since everything will take place in it and it will look like this:

You will need:

1. Composite ingot;
2. Charged battery.

And now you can pick up the finished product.

Use in crafting as an ingredient

1) Minecraft composite can help you create hardened glass:

You just have to add 7 glass blocks and that’s it.

2) You can also create a fortified stone by adding 8 blocks:

Here you will need to add 8 more stones and as a result you will get 8 already fortified ones.

3) This way you can create a shell for the reactor by adding 4 copper ingots to the craft:

4) If you wanted to create an improved mechanism case – please:

To do this, you will have to add two carbon fiber reinforced plastic and a regular mechanism case.

5) The reactor chamber is assembled as follows:

You will also need Reactor Sheathing, Improved Heat Sinking, Mechanism Body.

6) You can make a mining laser in this way:

Do not forget that in addition to the composite, you need to add 2 red dust, an electrical crystal and an improved electrical circuit.

7) Iridium composite can be made in this way:

There are also 4 iridium and 1 diamond involved.

8) The quantum vest is assembled like this:

Nanofiber vest, 4 Iridium composites, 1 Azuretron crystal are also present here.

9) Here comes the nanosaber:

Don’t forget to add 2 CFRP, 2 Light Dust, 1 Energy Crystal.

10) A composite vest is created in this way:

You will also need to add 1 Iron cuirass and 1 Leather cuirass here.

Now you know not only how to make a composite in minecraft, but the whole craft, where it is involved as an ingredient, therefore, now you can already purposefully create any of the elements.

The sea sponge was saturated with carbon and turned into a composite – Science

TASS, October 10. Scientists have developed a new catalyst for industry based on the carcass of sea sponges – organisms that live by attaching themselves to the seabed. This is stated in the message of the press service of the Ministry of Science and Higher Education.

The development was created by the participants of the “Extreme Biomimetics” project, which includes scientists from Russia, Germany, France, Poland and Slovakia, who are engaged in the study of natural and artificial phenomena for the development of new biosimilar three-dimensional composites.The aim of the project is to research and use renewable, naturally occurring non-toxic organic structures on a scale from centimeter to meter.

“The new three-dimensional composite material obtained by the project participants as a result of research has unique structural, mechanical and thermal properties, in the future it can serve as a basis for the preparation of catalysts and compete with such materials as carbon nanotubes,” the message says.

Over the past two years, scientists have been studying the structure of sea sponges that have existed on the planet for 600 million years.As a result of heat treatment, the carbon-saturated sponge reproduces the shape and unique microarchitecture of the original marine animal carcass.

When the framework is coated with a metal layer, it becomes a unique hybrid material with excellent catalytic properties. That is, it can accelerate chemical reactions, which is important for the development of modern technologies and the materials industry.

In the course of research, scientists have obtained a catalyst that can purify seawater from toxic compounds of nitrophenols, converting them into non-toxic compounds widely used in the pharmaceutical industry.

This study was supported by the German Research Foundation (DFG) and the Russian Foundation for Basic Research (RFBR).

Company “Factory of Composites”

LLC “Factory of Composites” is a Russian full-cycle engineering company for the development, design and production of products from composite materials.

Own production facilities allow you to create products of any complexity with exact observance of geometric, physical and mechanical parameters and color solutions.

The high quality of the products is confirmed by the company’s participation in large-scale Russian projects, incl. serial. “Factory of Composites” is a developer and operating supplier of fiberglass exterior and interior elements of modern urban electric transport.

The company also produces a wide range of auto components for special equipment, components for railway cars and other fiberglass products.

Main activities:

Advantages of Composites Factory LLC:

• Full cycle of product manufacturing: from the search for a visual idea and the development of concept design to serial delivery, including the creation of load-bearing metal frames, the production of split full-scale dies, and the painting of the product according to RAL.
• Creation of products, both according to our own calculations, and according to the technical documentation of the customer.
• The use of modern technical solutions that allow you to accurately program the properties of the future composite product and optimize the consumption of materials used in its production.
• Strict adherence to the delivery time of the finished product thanks to a streamlined logistics scheme.
• The management system operating at the enterprise in accordance with the standards of GOST R ISO 9001-2015 (ISO9001: 2015).

We are ready to work on new projects. We are waiting for your orders by phone: +7 (831) 215-03-35

Or leave a request on the website, and our manager will contact you to discuss the details of your project and calculate its cost:

PRESS ABOUT US

CERTIFICATES

DETAILS

Composite-Test Independent Product Certification Center (Moscow)

JOINT STOCK COMPANY “CERTIFICATION CENTER” COMPOSIT- TEST “is a Russian company, one of the leaders in the market of services for testing and confirmation of product compliance with regulatory requirements.

The Composite-Test brand is widely known: every year thousands of domestic and foreign firms become our customers. The certificates of the center enjoy well-deserved trust among consumers of products and are an indispensable attribute of many high-quality goods.

Our field of activity is testing and confirmation of conformity for certification and declaration of industrial products, including:

• metal products;
• building materials and products;
• non-metallic and polymeric materials and products from them;
• woodworking and furniture products;
• containers and packaging.

We are accredited by the Federal Service for Accreditation (Rosaccreditation) in the national accreditation system as:

included in the register of the Eurasian Economic Union (EAEU),

recognized in :

• in the Russian Voluntary Certification System Federation “FCS-Stroycertification”;
• Moscow system of voluntary certification in construction “Mosstroycertification”;
• Voluntary certification system INTERGAZCERT.

and continue to work:

• with mandatory confirmation of the conformity of products included in the product lists in accordance with the Decree of the Government of the Russian Federation of 01.12.2009 No. 982;
• upon voluntary confirmation of product conformity.

Our history

The history of the laboratories included in the Composite-Test Certification Center dates back to the 50s of the last century. The center was founded on the basis of research laboratories of NPO “Composite” – one of the largest scientific and production associations in the Soviet Union for the development of new materials in the rocket and space industry.

With the creation of the GOST R certification system in Russia, the Composite-Test Institute (still a part of NPO Kompozit) accredited a testing center in 1992, and in 1994 – a product certification body. In 1997, the Composite-Test Institute, in accordance with the requirements for independence to testing centers and certification bodies, became an independent legal entity, and in 2002 it was reorganized into the Composite-Test Certification Center.

Throughout this period up to 2015Composite-Test was accredited by Gosstandart (Rosstandart) and worked in the GOST R certification system. Composite-Test is accredited by the newly created Federal Service for Accreditation in the national accreditation system.

Center today

The personnel of the certification body and the testing center are highly qualified specialists with extensive experience both in confirmation of conformity during certification and declaration, and during testing.Among the specialists in the field of products there are two candidates of sciences, Honored Tester of the Russian Federation, Honored Machine Builder of the Russian Federation.

The normative base of the Center has several thousand units of standards, technical specifications, other normative documents, including technical regulations, and is constantly updated by a special service.

The testing laboratories of the Center are equipped with modern measuring instruments, analytical and testing equipment, including unique ones, which provide the ability to conduct various types of tests across the entire range of products declared in the field of accreditation.The premises of the testing laboratories meet modern requirements and allow, where necessary, tests of rather large-sized structures, including using the existing load-bearing floor and load-bearing wall for strength tests.

Independence and impartiality

The independence and impartiality of the Center’s activities is ensured and guaranteed by a set of developed and implemented measures: accreditation of the certification body and testing center, which could lead to a conflict of interests between the Center and applicants during testing and confirmation of product conformity;

The “Composite-Test” Certification Center does not provide consulting services to applicants for the performance of work to confirm the conformity of products included in the accreditation scope of the “Composite-Certificate” Product Certification Body and the “Composite-Test” Testing Center.

The “Composite-Test” certification center provides services for testing and confirming the conformity of products to all interested persons, organizations and enterprises, regardless of their organizational and legal form, form of ownership and location.

Why choose the Composite-Test Certification Center?

We pay great attention to studying the needs of customers, creating the most comfortable conditions for them in the process of working with the center. For this purpose, the central office is located on one of the central streets of Korolev, Moscow region, and is easily accessible.

Information about “Composite-Test” and the services it provides are published in advertising publications, reference books and bulletins. On the pages of our site you can find useful information about certification, declaration and testing, here you can also make an application for a specific job or ask us a question you are interested in.

In accordance with the requirements of GOST ISO / IEC 17025-2019 and GOST R ISO / IEC 17065-2012 and the Accreditation Criteria set out in the Order of the Ministry of Economic Development dated 26.10.2020 No. 707, the Center has developed and operates quality management systems that ensure strict implementation of all procedures and stages of work during testing and confirmation of conformity, and obtaining objective and reliable results.

All these events have allowed our center to form the image of a modern company with an individual corporate identity, to create a climate of trust in it from clients.

The Composite- Test Certification Center is a certification partner not only for many well-known large industrial enterprises and trading companies, but also for a large number of companies representing small and medium-sized businesses in many regions of Russia, near and far abroad.