Precision of fit of implant-supported screw-retained 10-unit computer-aided-designed and computer-aided-manufactured frameworks made from zirconium dioxide and titanium: an in vitro study

OBJECTIVE To analyze the precision of fit of implant-supported screw-retained computer-aided-designed and computer-aided-manufactured (CAD/CAM) zirconium dioxide (ZrO) frameworks. MATERIALS AND METHODS Computer-aided-designed and computer-aided-manufactured ZrO frameworks (NobelProcera) for a screw-retained 10-unit implant-supported reconstruction on six implants (FDI positions 15, 13, 11, 21, 23, 25) were fabricated using a laser (ZrO-L, N = 6) and a mechanical scanner (ZrO-M, N = 5) for digitizing the implant platform and the cuspid-supporting framework resin pattern. Laser-scanned CAD/CAM titanium (TIT-L, N = 6) and cast CoCrW-alloy frameworks (Cast, N = 5) fabricated on the same model and designed similar to the ZrO frameworks were the control. The one-screw test (implant 25 screw-retained) was applied to assess the vertical microgap between implant and framework platform with a scanning electron microscope. The mean microgap was calculated from approximal and buccal values. Statistical comparison was performed with non-parametric tests. RESULTS No statistically significant pairwise difference was observed between the relative effects of vertical microgap between ZrO-L (median 14 μm; 95% CI 10-26 μm), ZrO-M (18 μm; 12-27 μm) and TIT-L (15 μm; 6-18 μm), whereas the values of Cast (236 μm; 181-301 μm) were significantly higher (P < 0.001) than the three CAD/CAM groups. A monotonous trend of increasing values from implant 23 to 15 was observed in all groups (ZrO-L, ZrO-M and Cast P < 0.001, TIT-L P = 0.044). CONCLUSIONS Optical and tactile scanners with CAD/CAM technology allow for the fabrication of highly accurate long-span screw-retained ZrO implant-reconstructions. Titanium frameworks showed the most consistent precision. Fit of the cast alloy frameworks was clinically inacceptable.

Complications with computer-aided designed/computer-assisted manufactured titanium and soldered gold bars for mandibular implant-overdentures: short-term observations

BACKGROUND Implant-overdentures supported by rigid bars provide stability in the edentulous atrophic mandible. However, fractures of solder joints and matrices, and loosening of screws and matrices were observed with soldered gold bars (G-bars). Computer-aided designed/computer-assisted manufactured (CAD/CAM) titanium bars (Ti-bars) may reduce technical complications due to enhanced material quality. PURPOSE To compare prosthetic-technical maintenance service of mandibular implant-overdentures supported by CAD/CAM Ti-bar and soldered G-bar. MATERIALS AND METHODS Edentulous patients were consecutively admitted for implant-prosthodontic treatment with a maxillary complete denture and a mandibular implant-overdenture connected to a rigid G-bar or Ti-bar. Maintenance service and problems with the implant-retention device complex and the prosthesis were recorded during minimally 3-4 years. Annual peri-implant crestal bone level changes (ΔBIC) were radiographically assessed. RESULTS Data of 213 edentulous patients (mean age 68 ± 10 years), who had received a total of 477 tapered implants, were available. Ti-bar and G-bar comprised 101 and 112 patients with 231 and 246 implants, respectively. Ti-bar mostly exhibited distal bar extensions (96%) compared to 34% of G-bar (p < .001). Fracture rate of bars extensions (4.7% vs 14.8%, p < .001) and matrices (1% vs 13%, p < .001) was lower for Ti-bar. Matrices activation was required 2.4× less often in Ti-bar. ΔBIC remained stable for both groups. CONCLUSIONS Implant overdentures supported by soldered gold bars or milled CAD/CAM Ti-bars are a successful treatment modality but require regular maintenance service. These short-term observations support the hypothesis that CAD/CAM Ti-bars reduce technical complications. Fracture location indicated that the titanium thickness around the screw-access hole should be increased.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.

Approach to integrate product conceptual design information into a computer-aided design system

Commercial computer-aided design systems support the geometric definition of product, but they lack utilities to support initial design stages. Typical tasks such as customer need capture, functional requirement formalization, or design parameter definition are conducted in applications that, for instance, support ?quality function deployment? and ?failure modes and effects analysis? techniques. Such applications are noninteroperable with the computer-aided design systems, leading to discontinuous design information flows. This study addresses this issue and proposes a method to enhance the integration of design information generated in the early design stages into a commercial computer-aided design system. To demonstrate the feasibility of the approach adopted, a prototype application was developed and two case studies were executed.

Comparative study of auxetic geometrics by means of computer-aided design and engineering

Auxetic materials (or metamaterials) are those with a negative Poisson ratio (NPR) and display the unexpected property of lateral expansion when stretched, as well as an equal and opposing densification when compressed. Such geometries are being progressively employed in the development of novel products, especially in the fields of intelligent expandable actuators, shape morphing structures and minimally invasive implantable devices. Although several auxetic and potentially auxetic geometries have been summarized in previous reviews and research, precise information regarding relevant properties for design tasks is not always provided. In this study we present a comparative study of two-dimensional and three-dimensional auxetic geometries carried out by means of computer-aided design and engineering tools (from now on CAD–CAE). The first part of the study is focused on the development of a CAD library of auxetics. Once the library is developed we simulate the behavior of the different auxetic geometries and elaborate a systematic comparison, considering relevant properties of these geometries, such as Poisson ratio(s), maximum volume or area reductions attainable and equivalent Young's modulus, hoping it may provide useful information for future designs of devices based on these interesting structures.

Neural network controller for active demand side management with PV energy in the residential sector

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

Air pollution data classification by SOM Neural Network

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.

Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network

One of the biggest challenges that software developers face is to make an accurate estimate of the project effort. Radial basis function neural networks have been used to software effort estimation in this work using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The results show that radial basis function neural network have obtained less Mean Square Error than the regression method.