Incremental Technique Research Articles

Incremental Concolic Testing of Register-Transfer Level Designs

Concolic testing is a scalable solution for automated generation of directed tests for validation of hardware designs. Unfortunately, concolic testing fails to cover complex corner cases such as hard-to-activate branches. In this article, we propose an incremental concolic testing technique to cover hard-to-activate branches in register-transfer level (RTL) models. We show that a complex branch condition can be viewed as a sequence of easy-to-activate events. We map the branch coverage problem to the coverage of a sequence of events. We propose an efficient algorithm to cover the sequence of events using concolic testing. Specifically, the test generated to activate the current event is used as the starting point to activate the next event in the sequence. Experimental results demonstrate that our approach can be used to generate directed tests to cover complex corner cases in RTL models while state-of-the-art methods fail to activate them.

Finite-step approximately bi-similar symbolic model for switched systems

Obtaining an approximately bi-similar symbolic model for a continuous-state system is a crucial step in symbolic control. Symbolic control aims to design switching logic to achieve temporal logic specifications. Most existing approaches to approximate bi-simulation are developed over infinite time horizons. However, many control tasks are set within finite periods in practical scenarios. Hence, it makes sense to consider the behavior of trajectories within finite-time horizons. Therefore, this paper introduces two new notions: finite-time uniform incremental stability (FUI-stability) and finite-step approximate bi-simulation. Furthermore, a FUI-stability condition for switched system is derived using the multiple incremental Lyapunov-like functions technique. A symbolic model is constructed for switched system by the state space grid technique. Subsequently, a sufficient condition for finite-step approximate bi-simulation between the finite-time uniformly incrementally stable (FUI-stable) switched system and the constructed symbolic model is derived. Finally, the obtained results are illustrated by two numerical examples.

A Lightweight Semi-supervised Eye Semantic Segmentation Method Based on Iterative Learning

Eye semantic segmentation has a significant role in promoting biometric recognition. Most works improve the accuracy through designing complicated feature learning modules that usually require a high computation cost and are prone to over-fit. In this work, we propose a lightweight eye semantic segmentation method based on iterative learning, which only requires a small amount of labeled eye image data under visible light conditions. To be specific, we first propose a pseudo-label filtering strategy for eye semantic segmentation, which leverages a semi-supervised incremental learning technique to solve the problem of label scarcity. Besides, an attention-guided lightweight high-representation module is designed to boost precision while ensuring efficiency. Experiments on the Ubiris dataset show that the proposed method achieves state-of-the-art performance, with a mIoU of 86.18%.

The first composite increment: Dependency on placement technique for interaction behavior with maturing dentin-adhesive bond at pulpal floors in deep occlusal cavities – A comprehensive review

Abstract Introduction: When the traditional incremental technique is used for direct placement of composite resin in a deep occlusal cavity, each increment (2 mm or thicker) is individually cured where it undergoes hardening and shrinkage. As increments are bonded to all cavity walls, a constrained shrinkage develops in tooth, composite, and/or interfaces. In the first composite increment, this constrained shrinkage generates tensile stresses which are distributed in a nonuniform pattern within that increment, resulting in premature stressing of the dentin bond of the hybrid layer at the pulpal floor before it reaches full maturation. This premature stressing leads to initiation of interfacial debonding and propagation to form a microgap. This behavior is associated with persistent postoperative sensitivity and tooth pain, over the time, which subsequently results in restoration failure. Aims: This paper provides a comprehensive review on the first composite increment and its dependency on placement technique for the interaction behavior with the maturing dentin-adhesive bond at pulpal floors in deep occlusal cavities. Materials and Methods: The dental database was searched, and 59 articles were collected and included in this review, spanning the years from 1984 to 2023. Results and Discussion: Three biomimetic direct restorative techniques were reported in the literature for incrementally restoring large occlusal cavities. These techniques are the decoupling with time, the decoupling with fiber, and the decoupling with split-increment. They all aim at minimizing the generated shrinkage stresses in the first composite increment for protecting the developing dentin bond of the hybrid layer until it reaches full maturation and thus preventing the initiation and propagation of interfacial defects at pulpal floors of deep occlusal cavities. Finally, the restoration is completed by placing and curing successive increments of 1.5 mm each to fill the cavity; their number depends on the cavity depth. These increments can each be cured immediately following placement. Conclusions: In deep occlusal cavities, the interaction behavior between the first composite increment and the maturing dentin-adhesive bond at the pulpal floor depends on the technique used for the increment placement. This behavior is either favorable with the direct biomimetic techniques or unfavorable with the traditional incremental technique. With the direct biomimetic techniques, no premature shrinkage stressing of the dentin bond is induced at the pulpal floor. This prevents the initiation of interfacial debonding and propagation to form microgaps and results in the absence of postoperative sensitivity and persistent tooth pain.

Improved Incremental Conductance MPPT Technique Designed to Addressing Drift Problem in a Photovoltaic System

Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Comparison of incremental encoder digital signal processing techniques for theinduction motor flux-torque vector control systems

The articlepresents the results of a study on the effectiveness of varioustechniques forsynchronous reference frame angular position numerical calculation in flux-torque vector control system of induction motor.Investigation was caried out taking into account the discrete nature of the angular speedsignal obtained using an incremental encoder. In this workfor investigation by simulation useda direct torque vector control system, which, in the presence of an ideal rotor angular speedsignal, ensures direct asymptotic field orientation, asymptotic tracking of torque-flux referencetrajectories, as well as asymptotic decouplingtorque and flux subsystems. The parameters of the inductionmotor and encoder used in the study correspond to those existing in traction electromechanical systems of city trolleybuses. It is shown that the discrete nature of the angular speedsignal, which usedin synchronous reference frameposition equation of flux-torque vector control systems, introduces field orientation errors and leads to current and torque ripples, which in a real system increase acoustic noise and can cause mechanical vibrations and resonance phenomena. An analysis of possible ways to reduce the influence of the speed signal discreteness on flux-torque control is performed, and a method for practical implementation of the synchronous reference frame angular position numerical calculationis proposed. This method allows ensuring conditions for more precisefield orientation and, by using an additional filter for the angular speedsignal, reducing the level of current and torque ripplesto negligibly small values without affecting the field orientation processes. The proposed solution can be used in the development of high dynamicflux-torque vector control systems for inductionmotors using incremental encoders, including for electric vehicles.

New radiofrequency ablation procedure for selective reduction in complicated monochorionic twin or triplet pregnancy using multistep, incremental expansion technique.

Radiofrequency ablation (RFA) is the preferred approach for selective reduction in complex monochorionic (MC) multiple pregnancies owing to the ease of operation and minimal invasiveness. To optimize the RFA technique and reduce the risk of adverse pregnancy outcome resulting from the heat-sink effect of RFA therapy, we used an innovative RFA method, in which an electrode needle was expanded incrementally and stepwise. This study aimed to assess the efficacy and safety profile of this novel multistep incremental expansion RFA method for selective fetal reduction in MC twin and triplet pregnancies. This was a single-center retrospective cohort study of all MC multiple pregnancies undergoing RFA between March 2016 and October 2022 at our center. The multistep RFA technique involved the use of an expandable needle, which was gradually expanded during the RFA procedure until cessation of umbilical cord blood flow was achieved. The needle used for the single-step RFA method was fully extended from the start of treatment. In total, 132 MC multiple pregnancies underwent selective reduction using RFA, including 50 cases undergoing multistep RFA and 82 cases undergoing single-step RFA. The overall survival rates were not significantly different between the multistep and single-step RFA groups (81.1% vs 72.3%; P = 0.234). Similarly, the rates of preterm prelabor rupture of the membranes within 2 weeks after RFA, procedure-related complications, spontaneous preterm delivery and pathological findings on cranial ultrasound, as well as gestational age at delivery and birth weight, did not differ between the two groups. However, there was a trend towards a prolonged procedure-to-delivery interval following multistep RFA compared with single-step RFA (median, 109 vs 99 days; P = 0.377). Moreover, the fetal loss rate within 2 weeks after RFA in the multistep RFA group was significantly lower than that in the single-step RFA group (10.0% vs 24.4%; P = 0.041). The median ablation time was shorter (5.3 vs 7.8 min; P < 0.001) and the median ablation energy was lower (10.2 vs 18.0 kJ; P < 0.001) in multistep compared with single-step RFA. There were no significant differences in neonatal outcomes following multistep vs single-step RFA. Overall survival rates were similar between the two RFA methods. However, the multistep RFA technique was associated with a lower risk of fetal loss within 2 weeks after RFA. The multistep RFA technique required significantly less ablation energy and a shorter ablation time compared with single-step RFA in selective fetal reduction of MC twin and triplet pregnancies. Additionally, there was a trend towards a prolonged procedure-to-delivery interval with the multistep RFA technique. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

An Incremental Mutual Information-Selection Technique for Early Ransomware Detection

Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber–Physical Systems. This study addresses the need to detect early ransomware behavior when only limited data are available. A major step for training such a detection model is choosing a set of relevant and non-redundant features, which is challenging when data are scarce. Therefore, this paper proposes an incremental mutual information-selection technique as a method for selecting the relevant features at the early stages of ransomware attacks. It introduces an adaptive feature-selection technique that processes data in smaller, manageable batches. This approach lessens the computational load and enhances the system’s ability to quickly adapt to new data arrival, making it particularly suitable for ongoing attacks during the initial phases of the attack. The experimental results emphasize the importance of the proposed technique in estimating feature significance in limited data scenarios. Such results underscore the significance of the incremental approach as a proactive measure in addressing the escalating challenges posed by ransomware.

Mechanisms of Waterflood Inefficiency: Analysis of Geological, Petrophysical and Reservoir History, a Field Case Study of FWU (East Section)

The petroleum reservoir represents a complex heterogeneous system that requires thorough characterization prior to the implementation of any incremental recovery technique. One of the most commonly utilized and successful secondary recovery techniques is waterflooding. However, a lack of sufficient investigation into the inherent behavior and characteristics of the reservoir formation in situ can result in failure or suboptimal performance of waterflood operations. Therefore, a comprehensive understanding of the geological history, static and dynamic reservoir characteristics, and petrophysical data is essential for analyzing the mechanisms and causes of waterflood inefficiency and failure. In this study, waterflood inefficiency was observed in the Morrow B reservoir located in the Farnsworth Unit, situated in the northwestern shelf of the Anadarko Basin, Texas. To assess the potential mechanisms behind the inefficiency of waterflooding in the east half, geological, petrophysical, and reservoir engineering data, along with historical information, were integrated, reviewed, and analyzed. The integration and analysis of these datasets revealed that several factors contributed to the waterflood inefficiency. Firstly, the presence of abundant dispersed authigenic clays within the reservoir, worsened by low reservoir quality and high heterogeneity, led to unfavorable conditions for waterflood operations. The use of freshwater for flooding exacerbated the adverse effects of sensitive and migratory clays, further hampering the effectiveness of the waterflood. In addition to these factors, several reservoir engineering issues played a significant role in the inefficiency of waterflooding. These issues included inadequate perforation strategies due to the absence of detailed hydraulic flow units (HFUs) and rock typing, random placement of injectors, and uncontrolled injected fresh water. These external controlling parameters further contributed to the overall inefficiencies observed during waterflood operations in the east half of the reservoir. A detailed understanding of the mechanistic factors of inefficient waterflood operation will provide adequate insights into the development of the improved recovery technique for the field.

Enhancing SOC estimation accuracy via incremental learning techniques for second-life batteries

The available options for retired batteries include recycling or finding a secondary application. Repurposing the batteries for applications like stationary systems or low-speed vehicles may present a viable alternative. However, Second-Life Batteries (SLB) entail unique challenges since a battery pack comprises cells with diverse resistance and capacity characteristics. It makes the operation of BMS less efficient as the battery undergoes degradation. In light of these challenges, this study introduces an application of Incremental Learning in conjunction with a Random Forest model employing twenty estimators to predict a battery’s state of charge (SOC) at various levels of state of health. The proposed model was trained using the first three cycles and applied in real time during subsequent discharges. After each discharge cycle, the model underwent further training to refine its understanding of the underlying problem. Compared to other models, the implementation is more straightforward because the model learns with the data and does not require cell rest, actual capacity, or a battery model, such as known models in the literature. To validate the model, it has been applied for sixty discharge cycles for first-life batteries (Exp1), stationary (Exp2), and dynamic SLB systems (Exp3). The results demonstrated a consistently low RMSE SOC variation of less than 2% for Exp1 and Exp2 and less than 4% for Exp3, validating the use of it for first-life batteries and SLB with constant and dynamic loads.

Class similarity weighted knowledge distillation for few shot incremental learning

Few-shot class incremental learning illustrates the challenges of learning new concepts, where the learner can access only a small sample per concept. The standard incremental learning techniques cannot be applied directly because of the small number of samples for training. Moreover, catastrophic forgetting is the propensity of an Artificial Neural Network to fully and abruptly forget previously learned knowledge upon learning new knowledge. This problem happens due to a lack of supervision in older classes or an imbalance between the old and new classes. In this work, we propose a new distillation structure to tackle the forgetting and overfitting issues. Particularly, we suggest a dual distillation module that adaptably draws knowledge from two different but complementary teachers. The first teacher is the base model, which has been trained on large class data, and the second teacher is the updated model from the previous K-1 session, which contains the modified knowledge of previously observed new classes. Thus, the first teacher can reduce overfitting issues by transferring the knowledge obtained from the base classes to the new classes. While the second teacher can reduce knowledge forgetting by distilling knowledge from the previous model. Additionally, we use semantic information as word embedding to facilitate the distillation process. To align visual and semantic vectors, we used the attention mechanism of the embedding of visual data. With extensive experiments on different data sets such as Mini-ImageNet, CIFAR100, and CUB200, our model shows state-of-the-art performance compared to the existing few shot incremental learning methods.

Flexible multi-objective particle swarm optimization clustering with game theory to address human activity discovery fully unsupervised

Human activity recognition is a crucial field of study, but current approaches often require ground truth labels, which are not always available. We propose a new method called the Flexible Multi-Objective Particle swarm optimization clustering method based on Game theory (FMOPG), which can identify human activities without any supervision. Unlike traditional clustering methods that require an estimate of the number of clusters and are often inaccurate, FMOPG handles varying cluster numbers with an incremental technique, selecting clusters with good connectivity and separation. We enhance Particle Swarm Optimization (PSO) with mean-shift vectors for faster convergence and better handling of non-spherical clusters. Employing multi-objective optimization and Gaussian mutation, FMOPG provides a range of optimal solutions. We map the optimization problem to game theory to select the best solution based on different criteria. A smart grid-based method is proposed for population initialization, reducing variance and improving reliability. FMOPG outperforms state-of-the-art methods, improving clustering accuracy by 3.65%. Moreover, the incremental technique has improved clustering time by 71.18%.

Two-year evaluation of a nano-hybrid and a bulk-fill resin composite: a randomized, double-blind split-mouth clinical study

ObjectivesThe aim of this study was to compare the 2-year clinical performance of a bulk-fill composite resin and a nano-hybrid-filled composite resin in 6-12-year-old children in a split-mouth design.Materials and methodsThis randomized, split-mouth, and double-blind study was conducted on 89 patients aged 6–12 years with caries on bilateral mandibular first molars. In a split-mouth design, restorations of mandibular permanent molars were completed with nano-hybrid organically modified ceramic (ORMOCER)-based bulk-fill composite resin Admira Fusion x-tra (Voco GmbH, Cuxhaven, Germany) and nano-hybrid composite Grandio (Voco, Cuxhaven, Germany). Futurabond U single dose (Voco, Cuxhaven, Germany) was used with selective enamel etching. The clinical success of the restorations was evaluated using USPHS and FDI criteria at 6, 12, and 24-month follow-up controls.ResultsIn the 2-year follow-up, all restorations were clinically acceptable. Grandio was significantly worse than Admira Fusion x-tra in terms of surface luster and superficial change (p < 0.05). Surface staining and color match scores increased in Admira Fusion x-tra compared with Grandio significantly (p < 0.05).ConclusionsAlthough both materials showed acceptable clinical performance over 2 years, a significant difference was observed between the surface luster, surface staining, marginal adaptation, and staining of the nano-hybrid composite placed with the incremental technique and the bulk-fill ORMOCER-based composite resin.Clinical relevanceAs an alternative to nano-hybrid composite resins, using bulk-fill restorative materials, which can be indicated in the proper case, may contribute to shortening treatment procedures and increasing patient and physician comfort, leading to clinical success.

Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning

AbstractStructural seismic safety assessment is a critical task in maintaining the resilience of existing civil and infrastructures. This task commonly requires accurate predictions of structural responses under stochastic intensive ground accelerations via time‐costly numerical simulations. While numerous studies have attempted to use machine learning (ML) techniques as surrogate models to alleviate this computing burden, a large number of numerical simulations are still required for training ML models. Therefore, this study proposes a prior knowledge‐infused neural network (PKNN) for achieving efficient structural seismic response predictions and seismic safety assessment at a low computation cost. In this approach, first, the prior knowledge inherently within a theoretical dynamic model of a structure is infused into a neural network. Then, by utilizing the few‐shot incremental learning technique, this network would be further fine‐tuned by only a few numerical simulations. The resulting PKNN would be able to accurately predict the seismic response of a structure and facilitate seismic safety assessment. In this study, the PKNN's accuracy and computational efficiency are validated on a typical frame structure. The results revealed that the proposed PKNN could be used for accurately predicting the structural seismic responses and assessing the structural safety under a low computational cost.

Employing Incremental Learning for the Detection of Multiclass New Malware Variants

Background/Objectives: The study aims to achieve two main objectives. The first is to reliably identify and categorize malware variations to maintain the security of computer systems. Malware poses a continuous threat to digital information and system integrity, hence the need for effective detection tools. The second objective is to propose a new incremental learning method. This method is designed to adapt over time, continually incorporating new data, which is crucial for identifying and managing multiclass malware variants. Methods: This study utilised an incremental learning technique as the basis of the approach, a type of machine learning whereby a system retains previous knowledge and builds upon the information from the newly acquired data. Particularly, this method is suitable for tackling mutating character of malware dangers. The researchers used various sets of actual world malwares for evaluating the applicability of these ideas which serves as an accurate test environment. Findings: The findings of the research are significant. We utilizing 6 different datasets, which included 158,101 benign and malicious instances, the method demonstrated a high attack detection accuracy of 99.34%. Moreover, the study was successful in identifying a new category of malware variants and distinguishing between 15 different attack categories. These results underscore the effectiveness of the proposed incremental learning method in a real-world scenario. Novelty: This research is unique because of the novel use of a tailored incremental learning technique for dealing with dynamic threat environment of malwares. However, with a new threat they cannot be so well adapted using traditional machine learning methods. On the other hand, the technique put forward in this paper facilitates continuous learning that can be modified to match different types of malicious software as they develop. The ability to evolve and adapt is an important addition to current cybersecurity practices that include malware identification and classification. Keywords: Cybersecurity, Malware Detection, Incremental learning

Assessment of Microhardness of Bulk-Fill Class II Resin Composite Restorations Performed by Preclinical Students: An In Vitro Study

Abstract Objectives A comparable performance between bulk-fill composites (BFCs) and progressively inserted conventional resin composite (CRC) has been observed in previous studies. However, a significant number of dental institutions in India continue to employ incremental techniques for RC restorations during preclinical studies. But as BFCs are gaining popularity, they may soon be a part of the curriculum for dental students. The aim of this study was to assess the microhardness and the polymerization efficiency of bulk-fill composites versus CRC in class II slot preparations restored by second-year dental students on ivorine mandibular first molar teeth with high-intensity (HI) and low-intensity (LI) light-curing units using the standard mesial slot preparation technique. Material and Methods Fifty preclinical dental students of second year of a dental college were recruited for the study on a voluntary basis. During their preclinical instructions, all participants were taught and made to practice the handling and curing techniques for two-surface RC restorations for 4 months. Each student was asked to perform four RC restorations: CRC-HI, CRC-LI, BFC-HI, and BFC LI. Assessment of microhardness was done using the Vickers microhardness (VMH) test. Statistical analysis One-way and multivariate analysis of variance and Bonferroni 's post hoc test tests were used for data analysis. Results The results showed a significant decrease in the VMH readings in the horizontal axis, progressing from the uppermost to the lowermost positions (p &gt; 0.05). In all the examined groups, the VMH values at the deepest reading locations were found to be higher than 80% of the values observed at the occlusal surface reading locations. Statistically significant associations were seen between the RC type and the light source used in the VMH readings conducted (p &gt; 0.05). However, no significant correlation was identified between the type of light source and the vertical VMH readings (p &gt; 0.05). Conclusion Instructing dental students to place RC restorations using CRC or BFC materials together with both HI and LI light-curing protocols is safe and can be considered for preclinical studies.

Tikhonov Regularization for the Fully Coupled Integral Method of Incremental Hole-Drilling

BackgroundThe unit pulse integral method is used extensively with the incremental hole-drilling residual stress measurement technique. The ASTM E837 standard, which applies only to isotropic materials, recommends the use of Tikhonov regularization to reduce instability when many depth increments are used. In its current formulation, Tikhonov regularization requires the decoupling of stress, as is possible for isotropic materials. The fully coupled integral method is needed for residual stress determination in layered composite laminates and is currently employed without Tikhonov regularization. This causes greater sensitivity to measurement errors and consequently large stress uncertainties. An approximate method of applying Tikhonov regularization exists for biaxial composites, but is not applicable to more complex laminates.ObjectiveExtend Tikhonov regularization to the fully coupled integral method to improve residual stress determination in composite laminates.MethodsThis work investigates the use of the approximate and fully coupled regularization approaches in an angle ply composite laminate of [+45/-45/0/90]s construction. Experimental validation in a [0/+45/90/-45]s laminate is also presented where the regularized fully coupled integral method is compared to the series expansion method that includes all in-plane stress and strain directions simultaneously in a least-squares solution.ResultsThe regularized integral method produces comparable results to those of series expansion while requiring twelve times less FE computation to calculate the compliances. The optimal degree of regularization is also more convenient to determine than the optimal combination of series order required by series expansion.ConclusionsThe new method is easily applied and should find wide application in the measurement of residual stresses in composite laminates.

Dentin adhesion of bulk-fill composites and universal adhesives in class I-cavities with high C-factor

ObjectivesThe aim of this study was to compare the dentin adhesion of bulk-fill composites in high C-factor class I-cavities before and after thermocycling to a control group using incremental layering technique. MethodsA standardized class I-cavity was prepared into 195 human molars, then different universal adhesives were applied either in self-etch or etch & rinse mode, and the cavity was filled according to each materials application protocol. The material combinations used were a conventional layered composite as control, the respective bulk-fill product, two other bulk-fill composites made by different manufacturers, with one of them being tested using two different polymerization times. Furthermore, one thermoviscous bulk-fill composite and one self-adhesive restorative were examined of which the latter can only be applied in self-etch mode. In each group the dentin adhesion to the cavity bottom was measured using microtensile bond strength test initially (24 h water storage) and after thermocycling. All results were statistically analyzed using STATA 17.0. ResultsThe statistical analysis showed significant differences between the control and the experimental groups (p < 0.001). The highest mean bond strength before (14.8 ± 10.7 MPa) and after aging (14.2 ± 11.5 MPa) was measured for the etch & rinse-control group. Among the bulk-fill groups, the etch & rinse technique consistently showed higher bond strengths. Bond strength of groups with shortened polymerization did not exceed 2.1 MPa. The bond strength of the self-adhesive restoration material was low before and after thermocycling (2.7 MPa/ 0.0 MPa). Groups with low bond strength values showed a high number of pre-testing-failures. ConclusionsBulk-fill materials used in high C-factor class I-cavities showed lower bond strength during self-etch application. The same applies for a shortened polymerization regime, which cannot be recommended for high C-factor cavities. Clinical significanceToday, a large variety of materials and application techniques can be used when placing an adhesive restoration. Whether new instead of established procedures should be applied in high C-factor cavities has to be critically assessed, as they are a demanding scenario for adhesive restorations.

Clinical Evaluation of Two Different Nanohybrid Composite Restorations Using FDI Criteria – A Randomized Clinical Trial

Background: Several approaches were introduced in placing the composite resin into the prepared cavity. Some researchers recommend using an incremental technique. The material is gradually placed in 2 mm or less thick layers, resulting in better curing by adequate light penetration and better composite resin polymerization. However, disadvantages like voids and the time taken to complete the procedure are lengthier due to the time consumption in placement and polymerization of each increment. Time-saving restorative materials are an ongoing demand for posterior restorations. A newer resin-based composite, bulk-fill composites, have been introduced to speed up the restoration process by enabling up to 4–5 mm thick increments to be cured in one step, thus skipping the time-consuming layering process. Materials and Methods: The randomized clinical trial was conducted on 40 patients with 80 restorations. Randomization was followed by using a random number table. Patients with at least two class I or class II restorations were selected. One tooth is restored with bulk-fill followed by capping with ceram x, and the other with bulk-fill followed by Filtek Z250. All the patients were recalled after 6 months and 12 months. Two examiners evaluated restorations using Federation Dentaire International (FDI) criteria at baseline, 3, 6, and 12 months. Results: The collected data from the participants were analyzed using SPSS software. There was no statistically significant difference observed between the two restorations placed with two nanohybrid composites (ceram x and Filtek Z250). Conclusion: The study concludes that bulk-fill resin composites capped with nanohybrid composites show excellent clinical performance equal to conventional composites and are beneficial to the clinician in reducing the time. Smart dentin replacement has a self-leveling capacity, showing better marginal adaptation and thus helping reduce secondary caries.