Adaptive Fitness Research Articles

Adaptive Control of Tumor Growth.

Cancer treatment optimizations select the most optimum combinations of drugs, sequencing schedules, and appropriate doses that would limit toxicity and yield an improved patient quality of life. However, these optimizations often lack an adequate consideration of cancer's near-infinite potential for evolutionary adaptation to therapeutic interventions. Adapting cancer therapy based on monitored tumor burden and clonal composition is an intuitively sound approach to the treatment of cancer as an inherently complex and adaptive system. The adaptation would be driven by clinical outcome setpoints embodying the aims to thwart therapeutic resistance and maintain a long-term management of the disease or even a cure. However, given the nonlinear, stochastic dynamics of tumor response to therapeutic interventions, adaptive therapeutic strategies may at least need a one-step-ahead prediction of tumor burden to maintain their control over tumor growth dynamics. The article explores the feasibility of adaptive cancer treatment driven by tumor state feedback assuming cell adaptive fitness to be the underlying source of phenotypic plasticity and pathway entropy as a biomarker of tumor growth trajectory. The exploration is undertaken using deterministic and stochastic models of tumor growth dynamics.

A Digital Twin for Detecting Liquid-Liquid Interface in Containers

This study introduces a digital twin (DT) architecture for detecting liquid-liquid interfaces in containers, aimed at improving real-time monitoring and optimising the separation process in the petroleum industry. The research employs the DT concurrent to the physical twin to enhance the system performance by integrating response error measures with knowledge of industrial processes. The DT uses ultrasonic sensors to collect data and a high-fidelity numerical wave propagation model for an adaptive fitting algorithm to update the interface responses. Both experimental studies and numerical simulations confirm the ability of the DT to accurately determine liquid levels by analysing interfacial responses from ultrasonic signal features in transmission mode. Cross-validation and error metrics validate the DT’s adaptability and accuracy in interface detection, even with undersampled data.

A comparison of the population ecology of 4 Cryptomys hottentotus subspecies

Abstract Selection pressures underpinning the evolution of mammalian sociality and body mass variation have spurred great interest for several decades. Because they inhabit a wide range of geographic localities and habitats, African mole-rat subspecies of Cryptomys hottentotus present a unique opportunity to further our understanding of intra- and interspecific variation of sociality and body mass in mammalian species. We compared the demographics and body masses among 4 C. hottentotus subspecies: the Mahali mole-rat, C. h. mahali; highveld mole-rat, C. h. pretoriae; Natal mole-rat, C. h. natalensis; and common mole-rat, C. h. hottentotus within the context of their respective microclimates and evolutionary history. We propose that all ancestral C. hottentotus subspecies were arid-adapted and thus formed large colonies (as found today in the Mahali mole-rat). However, as the subspecies dispersed to occupy habitats of varying aridity and temperature across South Africa, selection for a particular colony size range in each subspecies arose to provide an adaptive fitness benefit to survive in its habitat. Consequently, the Mahali mole-rat—which remained in a warm and arid environment—retained the largest mean colony size, followed by the Natal mole-rat, which—even though inhabiting a hyper-mesic environment—selected for increased colony sizes to offset the energy requirement of thermoregulation (through huddling) in the cooler montane environments they prefer. The common mole-rat and the highveld mole-rat possessed the smallest mean colony sizes, likely the result of inhabiting a mesic and warm environment. At the same time, body mass variation in the C. hottentotus complex is likely linked to colony size variation, with subspecies having the largest colonies possessing the lowest individual body mass.

A control strategy for improving the accuracy of lateral tracking of autonomous vehicles

Lateral tracking is a fundamental technology for autonomous vehicles, particularly in narrow and complex scenes such as parking lots and alleys, where precise control is critical. To enhance the efficacy and precision of the traditional Pure Pursuit algorithm (PP) in the lateral tracking of low-speed autonomous vehicles, this study proposes a continuous adaptive piecewise fitting method based on the cubic Bézier curve to smooth the reference path, followed by a search for the look-ahead point based on the geometric relationship between the vehicle and the reference path. An improved search algorithm based on vehicle speed and road curvature is used to identify the look-ahead point. In case of significant changes in the reference path curvature, the initially obtained look-ahead point is modified. The proposed Improve Pure Pursuit Method (IPP) is then used to calculate the steering angle of the front wheel. To validate the proposed method, the Carsim/Simulink co-simulation model and the autonomous vehicle platform are established. The simulation and real-vehicle tests demonstrate that compared with the pure pursuit algorithm based on Vehicle speed control (VPP) and Fuzzy Logic control pure Pursuit (FPP), IPP performs better in complex low-speed lateral tracking scenes. It reduces the issue of cutting corners when the vehicle enters a corner and enhances lateral tracking accuracy.

Fast Ground Segmentation Method Based on Lidar Point Cloud

A ground segmentation method based on line fitting of adjacent points was proposed for accurate and real-time segmentation of non-ground information from the LiDAR point cloud. Firstly, the point cloud is divided into several ordered regions depending upon the distribution characteristics of the LiDAR’s concentric circles. Then, the Euclidean distance between adjacent points and the spatial geometric features of ground point clouds is used for adaptive line fitting of ground point clouds. Finally, the ground points are divided by the distance between the adjacent points and the outer points of the line. The experiment was conducted using a real car and the KITTI open-source dataset. The approach presented in this research substantially enhances the accuracy of ground segmentation while ensuring real-time performance.

An ex-ante method to verify commercial U.S. nuclear power plant decommissioning cost estimates

There are billions of dollars at stake in the US nuclear power plant decommissioning market. Approximately 92 nuclear power plants are still operating but will come offline and need to be decommissioned over the next few decades. The Nuclear Regulatory Commission (NRC) mandates that the operators of these plants set money aside in segregated funds to finance decommissioning work. However, it is hard for external stakeholders to verify the cost estimations, which ultimately determine how much operators are required to save. In this paper, we develop a method to validate the existing cost models and calculate a contingency empirically for these models. We extend Reference Class Forecasting methods using adaptive kernel fitting and the Wilks’ formula. Based on this method, and assuming a social tolerance for potential cost overruns of 20%, we calculate a new contingency of 48% of the estimated radiological decommissioning cost. After a “stress test” of the current decommissioning trust funds of operating reactor sites, we find that 48% of reactors have sufficient funding—in many cases substantially more than required—and could therefore finance the potential scale of overrun. However, we find that 28 plants would fall short on average $211 million. Still, overruns at every plant are not a foregone conclusion because—while overruns are probable, based on past experience, the actual scale and frequency is not known. Nevertheless, our results add further evidence to the mounting call for the NRC to revise its cost models in light of new information.

Designing and Constructing a Tool for Safety Culture Evaluation in a Processing Industry Based on Factor Analysis

Objectives: This study aims to develop and create a tool based on factor analysis to assess safety culture in the processing industry. Methods: This study was conducted in the petrochemical industry in 2021. The questionnaires were distributed among 312 employees, supervisors, and managers, and 308 of them were completed. The validity of the questionnaire was assessed by content and construct validity (confirmatory factor analysis), and its reliability was determined by calculating the internal consistency coefficient (Cronbach’s α) and conducting a pilot study. Confirmatory factor analysis was used to examine the relationship between the dimensions of safety culture and the dimensions themselves. Results: The results showed that 62.4% of participants were under 45 years old and 71.2% of them had more than 10 years of work experience. The obtained content validity index (CVI) and content validity ratio (CVR) were 0.88 and 0.91, respectively. The results of exploratory factor analysis (EFA) revealed that six questions were assigned to each dimension of safety culture in the questionnaire. The results of confirmatory factor analysis (CFA) with a P of 0.001 and the overall goodness index of root mean square error of approximation (RMSEA) of 0.07 and the goodness indices of adaptive fit, including good fit index (GFI), comparative fit index (CFI), and normal fit index (NFI) were 0.95, 0.97, and 0.95, respectively. Discussion: Based on the results, a safety culture assessment instrument was developed, including 10 dimensions and 60 items. The results of factor analysis showed that the built-in instrument is highly useful to assess safety culture. In addition, these results showed that safety culture has the strongest relationship with the priority of focusing on health, safety, and environment (HSE) and the weakest relationship with the dimension of collaboration and involvement.

The standard problem.

This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. Beginning with the notion of common models, the framework describes the general standard problem-the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system. The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time. This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.

Vitis labrusca genome assembly reveals diversification between wild and cultivated grapevine genomes.

Wild grapevines are important genetic resources in breeding programs to confer adaptive fitness traits and unique fruit characteristics, but the genetics underlying these traits, and their evolutionary origins, are largely unknown. To determine the factors that contributed to grapevine genome diversification, we performed comprehensive intragenomic and intergenomic analyses with three cultivated European (including the PN40024 reference genome) and two wild North American grapevine genomes, including our newly released Vitis labrusca genome. We found the heterozygosity of the cultivated grapevine genomes was twice as high as the wild grapevine genomes studied. Approximately 30% of V. labrusca and 48% of V. vinifera Chardonnay genes were heterozygous or hemizygous and a considerable number of collinear genes between Chardonnay and V. labrusca had different gene zygosity. Our study revealed evidence that supports gene gain-loss events in parental genomes resulted in the inheritance of hemizygous genes in the Chardonnay genome. Thousands of segmental duplications supplied source material for genome-specific genes, further driving diversification of the genomes studied. We found an enrichment of recently duplicated, adaptive genes in similar functional pathways, but differential retention of environment-specific adaptive genes within each genome. For example, large expansions of NLR genes were discovered in the two wild grapevine genomes studied. Our findings support variation in transposable elements contributed to unique traits in grapevines. Our work revealed gene zygosity, segmental duplications, gene gain-and-loss variations, and transposable element polymorphisms can be key driving forces for grapevine genome diversification.

Marginal gap and internal fit of 3D printed versus milled monolithic zirconia crowns

BackgroundThis study aimed to evaluate and compare the marginal gap using two different methods and the internal fit of 3D printed and zirconia crowns.Methods3Y-TZP zirconia crowns (n = 20) were manufactured using subtractive milling (group M) and 3D printed (group P). The marginal gap was measured at 60 points using vertical marginal gap technique (VMGT). On the other hand, the silicone replica technique (SRT) was used to evaluate the internal fit and was divided into 4 groups: marginal gap, cervical gap, axial gap, and occlusal gap where the thickness of light impression was measured at 16 references. The numerical data was tested for normality using Shapiro–Wilk's test. They were found to be normally distributed and were analyzed using an independent t-test.ResultsUsing VMGT, group P had significantly higher mean marginal gap values of 80 ± 30 µm compared to group M = 60 ± 20 µm (p < 0.001). Also, with the SRT, the marginal gap of group P (100 ± 10 µm) had significantly higher values compared to group M (60 ± 10 µm). The internal fit showed significant difference between the tested groups except for Axial Gap.ConclusionsAlthough milled crowns showed better results. The 3D printed zirconia crowns offer clinically acceptable results in terms of marginal adaptation and internal fit. Both VMGT and SRT are reliable methods for the assessment of the marginal gap.

Innovative Exercise in a New Normal

Innovative exercise is an important aspect of a healthy lifestyle. The availability of various resources has made it easier for people to find activities that they enjoy and that meet their fitness goals. In addition to traditional exercises like running, weightlifting, and yoga, there are also new, exciting forms of exercise such as virtual reality fitness, aerial yoga, and trampoline workout classes. Technology has also played a major role in making exercise more accessible and convenient. For example, there are fitness apps that allow users to track their progress, set goals, and participate in virtual workout classes from the comfort of their own homes. Wearable fitness devices, such as fitness trackers and smartwatches, make it easy to monitor physical activity and track progress in real-time. In addition to these technological advancements, there is also an increased focus on making fitness facilities more inclusive and accessible to all. This includes the incorporation of accessible equipment, sensory-friendly environments, and adaptive fitness programs for individuals with disabilities. Overall, the trend towards innovative exercise is not only providing people with new and exciting ways to stay active, but it is also helping to address the challenges posed by the pandemic and other emerging health threats. By continuing to evolve and adapt to changing circumstances, the fitness industry is playing an important role in promoting health and wellness for people around the world.

A new near-infrared spectral transformation method for quantitative analysis

The existing near-infrared spectrum preprocessing methods are limited in improving the performance of prediction models for quantitative analysis because they are hard to extract furthermore valid information from the complex near-infrared spectra. This paper proposes a new near-infrared spectral transformation method based on the Gaussian-curve-fitting—adaptive full-rank fitting integral (AFFI) transformation, which can automatically adapt to different spectral shapes and fully display the useful information hidden in the waveforms. In this method, the optimal bandwidths of the Gaussian functions are determined by correlation analysis between waveform and bandwidths; the heights of the Gaussian functions are obtained by constructing and solving the curve-fitting equations; the transformation is realized by integrating these Gaussian functions. In the experiments, the variation of the Pearson correlation coefficient between the near-infrared spectral bands and quantitative analysis object is used to demonstrate the effectiveness of the proposed method. Finally, the partial least squares (PLS) method was used to build prediction models of quantitative analysis. The experimental results of two different near-infrared spectral data sets show that the proposed method has obvious advantages with some existing pretreatment and transformation methods.

A Variational Synthesis of Evolutionary and Developmental Dynamics.

This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from-and influence-each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain-and are constrained by-fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses-and identify points of contact with related mathematical formulations of evolution.

An ensemble learning-based multi-population evolutionary framework for multi-scenario multi-objective optimization problems

Multi-scenario multi-objective optimization problems (MSMOPs) are a topic of considerable interest in the field of optimization. The MSMOP contains a set of multi-objective optimization problems arising from varying operating conditions, with the goal of determining a set of communal compromise solutions. However, there are few universal methods available for MSMOPs. This paper presents a general method incorporating transfer learning for MSMOPs. First, a multi-scenario ensemble framework that transfers knowledge between scenarios is developed to combine arbitrary multi-objective evolutionary algorithms, where a scenario-based comprehensive evaluation indicator is developed for combination with base learners. Then, an adaptive decomposition-based multi-objective evolutionary algorithm with a bi-layer selection (EADaBS) is proposed and embedded within the framework as a base learner. EADaBS incorporates an adaptive fitness assignment in its first layer to facilitate exploration, and density measurement in its second layer to ensure exploitation. A rebalancing operator is also designed to aid the population towards the Pareto front. Finally, a multitude of experiments is conducted to verify the effectiveness and efficiency of the proposed algorithms. Two three-scenario multi-objective optimization problems are designed and utilized as test problems. The experimental results clearly demonstrate that the proposed framework outperforms existing state-of-the-art algorithms. In conclusion, this research provides new insights into the solution of MSMOPs.

Evaluation of Internal Adaptation and Marginal Fit of Onlays Fabricated Using Computer-Aided Design (CAD)-Computer-Aided Manufacturing (CAM) and Three-Dimensional Printing Techniques: An In Vitro Study.

Background This in-vitro study aimed to evaluate the internal adaptation, marginal fit, and applicability of digital intraoral impression techniques for onlays fabricated using computer-aided design (CAD)-computer-aided manufacturing (CAM) and three-dimensional (3D) printing techniques using a stereomicroscope and micro-CT scan. Methodology A total of 20 extracted mandibular first molars were selected for this study. The teeth were then divided into two groups. Onlay cavities were prepared involving the mesiobuccal cusp of the mandibular first molar in both groups. After preparation, both blocks were sent to the laboratory for fabrication of onlays using digital impressions (Shinning 3D scanner). Once the onlays were fabricated using CAD-CAM and 3D printing, a replica technique with monophase medium body impression material was used to assess the marginal fit and internal adaptation. The accuracy of internal adaptation was evaluated and compared using a stereomicroscope at 20× magnification. Measurements were taken at proximal margins, the inner axial wall, and the occlusal cavosurface area according to the Molin and Karlsson criteria. The same samples of both groups were studied for marginal fit using a micro-CT scan and values were recorded. The data collected were statistically analyzed using an independent Student's t-test. Results Independent Student's t-test results demonstrated that the mean thickness values of the material in the CAD-CAM group at occlusal cavosurface area, proximal area, and axial area were significantly higher when compared to the 3D printing group at p <0.001 and 0.005, respectively. Conclusions Internal adaptation and marginal fit of 3D-printed onlays were significantly lower than CAD-CAM onlays whereas the accuracy of 3D-printed onlays was significantly better than CAD-CAM onlays.

Structure-guided virtual restoration for defective silk cultural relics

The digital restoration of damaged silk relics is a type of blind restoration problem that relies on limited prior information. Existing image restoration techniques face significant difficulties in repainting missing patterns of both structural and content. Silk cultural relics are prone to decay, mold, moth, fading, pollution, and adhesion due to long-term exposure to external factors such as temperature, humidity, and microorganisms. Thus, restoring cultural relic patterns is crucial for archeological and design industries. Virtual restoration, based on image inpainting technology, can tackle this problem. However, existing inpainting algorithms are not effective in defective areas lacking a structural trend and only relying on prior information. Moreover, the unique and irregular patterns of silk cultural relics pose a further challenge. To address these issues, we propose a structure-guided virtual restoration method. Concretely, to maintain global image consistency, we introduce an adaptive curve fitting algorithm to reconstruct missing structural lines. We then establish a new priority function to improve the filling order of patches to be repaired, and an adaptive selection of the sample block size is adopted based on the sparsity of the structure. Our method outperforms existing techniques by reconstructing the structural lines according to the pattern design, thus guiding image inpainting to avoid erroneous fillings and block effects. Results show that the restored images achieved an average SSIM of 0.977 and an average PSNR of 39.16. The proposed method for virtual restoration, utilizing curve fitting and image inpainting guided by the artifact's structure, has demonstrated significant improvements in the preservation of structure continuity and faithful reproduction of original patterns on the silk cultural relics.

Hybrid reliability-based sequential optimization for PID vibratory controller design considering interval and fuzzy mixed uncertainties

Vibration control is an important problem in practical engineering. Optimal control is widely used, but the uncertainties in practical engineering may lead to the failure of the system. To reach a balance of safety and energy-consuming, taking the widely-used proportional-integral-differential control as an example, a framework for hybrid reliability-based sequential optimization for vibratory controller design considering interval and fuzzy mixed uncertainties is proposed in this paper. Firstly, the fuzzy uncertainties are transformed into interval uncertainties according to the cut-level strategy. Then the response interval under a certain membership can be calculated by the subinterval collocation method, which can guarantee the accuracy of the calculated response interval. In this process, to choose proper memberships as cut-levels and improve efficiency, the adaptive fitting method based on the differences of areas is adopted. Next, taking interval and fuzzy uncertainties into consideration, a novel non-probabilistic reliability evaluation is performed based on the area ratio, which is composed of upper and lower boundaries of the minimum value of the extreme state function. Finally, the sequential optimization is adopted to improve the efficiency of optimization, in which the constraint is changed according to the proposed non-probabilistic area-ratio reliability. Two engineering examples are presented to demonstrate the accuracy and practicability of this framework.

Glitch subtraction from gravitational wave data using adaptive spline fitting

Transient signals of instrumental and environmental origins (‘glitches’) in gravitational wave data elevate the false alarm rate of searches for astrophysical signals and reduce their sensitivity. Glitches that directly overlap astrophysical signals hinder their detection and worsen parameter estimation errors. As the fraction of data occupied by detectable astrophysical signals will be higher in next generation detectors, such problematic overlaps could become more frequent. These adverse effects of glitches can be mitigated by estimating and subtracting them out from the data, but their unpredictable waveforms and large morphological diversity pose a challenge. Subtraction of glitches using data from auxiliary sensors as predictors works but not for the majority of cases. Thus, there is a need for nonparametric glitch mitigation methods that do not require auxiliary data, work for a large variety of glitches, and have minimal effect on astrophysical signals in the case of overlaps. In order to cope with the high rate of glitches, it is also desirable that such methods be computationally fast. We show that adaptive spline fitting, in which the placement of free knots is optimized to estimate both smooth and non-smooth curves in noisy data, offers a promising approach to satisfying these requirements for broadband short-duration glitches, the type that appear quite frequently. The method is demonstrated on glitches drawn from three distinct classes in the Gravity Spy database as well as on the glitch that overlapped the binary neutron star signal GW170817. The impact of glitch subtraction on the GW170817 signal, or those like it injected into the data, is seen to be negligible.

Adaptive plasticity and fitness costs of endangered, nonendangered, and invasive plants in response to variation in nitrogen and phosphorus availabilities.

Global change drivers such as eutrophication and plant invasions will create novel environments for many plant species. Through adaptive trait plasticity plants may maintain their performance under these novel conditions and may outcompete those showing low-adaptive trait plasticity. In a greenhouse study, we determined if plasticity in traits is adaptive or maladaptive in endangered, nonendangered, and invasive plant species in response to variation of nitrogen (N) and phosphorus (P) availability (N:P ratios 1.7, 15, and 135) and whether plastic trait responses are adaptive and/or costly for fitness (i.e., biomass). Species choice comprised 17 species from three functional groups (legumes, nonlegume forbs, and grasses), either classified as endangered, nonendangered, or invasive. After 2 months, plants were harvested and nine traits related to carbon assimilation and nutrient uptake were measured (leaf area, SLA, LDMC, SPAD, RMR, root length, SRL, root surface area, and PME activity). We found more traits responding plastically to variation in P than in N. Plasticity only created costs when P was varied. Plasticity in traits was mostly adaptively neutral toward fitness, with plasticity in three traits being similarly adaptive across all species groups: SPAD (as a measure of chlorophyll content, adaptive to N and P limitation), leaf area, and root surface area (adaptive to P limitation). We found little differences in trait plasticity between endangered, nonendangered, and invasive species. Synthesis. Along a gradient from N limitation, balanced N:P supply, and P limitation, we found that the type of fluctuating nutrient (i.e., if N or P is varied) is decisive for the adaptive value of a trait. Variation in P availability (from balanced supply to P limitation) created both a stronger reduction in fitness as well as created plasticity costs in more traits than variation in N availability (from balanced supply to N limitation). However, the patterns observed in our study may change if nutrient availability is altered, either by nutrient inputs or by a shift in nutrient availabilities, for example, by decreasing N input as foreseen by European Legislation, but without simultaneously decreasing P input.

Accuracy in the Marginal Adaptation and/or Internal Adaptation of Full-coverage Fixed Prostheses Made with Digital Impressions and Conventional Impressions: A Systematic Review

Aim: The study aimed to verify compliance of the systematic reviews with the requirements established by the scientific community and demonstrate the validity and reliability of the systematic reviews conducted on the accuracy (marginal adaptation and/or internal adaptation) of the full-coverage fixed prostheses made with digital impressions versus conventional impressions. Methods: A search was performed for systematic reviews in three electronic databases, PubMed, Scopus, and Web of Science, as well as in the gray literature. In the search strategy, medical subject heading (MeSH) words were used in PubMed, and free terms were used for the titles and abstracts of each article. Each keyword was separated by the Boolean operator OR and later combined with the Boolean operator AND. Six systematic reviews were included for qualitative synthesis. To assess the methodological quality of the included systematic reviews, the AMSTAR 2 tool was used. Results: The search yielded 131 studies, of which 78 remained after removing duplicates. The title and abstract of each chosen study were assessed, and 22 articles were included for full-text reading. Finally, six studies were included, of which three studies were considered to have low confidence, while the other three were considered to have critically low confidence. In addition, the six SRs evaluated the adaptation or marginal fit, while only three studies measured internal adaptation. Conclusion: The use of digital impressions in single fixed prostheses maintains a marginal level within the limit of clinical acceptability; however, the methodological quality of systematic reviews is poor, according to the AMSTAR 2 tool.