Simulation education utilizing phantom and angle reference guide in pulmonary nodule CT localization.

Objective: The incidence of sub-centimeter pulmonary nodules has been increasing along with the use of low-dose computed tomography (LDCT) as a screening tool for early lung cancer detection. In our institution, pulmonary nodule computed tomography-guided localization (PNCL) is performed preoperatively with the laser angle guided assembly (LAGA), an angle reference device. This study aims to investigate the efficacy of postgraduate education in a phantom simulation of PNCL, with or without LAGA. Setting design: This prospective study was conducted in an academic hospital in Taiwan. Seven thoracic surgery residents and three experienced senior physicians were recruited to perform PNCL using a phantom simulation, with or without LAGA, for five nodules each and complete a questionnaire. Performance data were collected. χ2 tests, Mann-Whitney U test, univariate and multivariate linear regression were used for statistical analyses. Results: The confidence level increased from median 7[range 1, 9] to 8, range [6,9] (p = 0.001) before and after the simulation education course. The scores of enhanced PNCL ability and course satisfaction were as high as 8 [5,9], and 9 [7,9]. LAGA enabled broader puncture angles (with 27.5° [0°,80°]; without 14° [0°, 80°], p = 0.003), a lower puncture frequency (with 1 [1,4]; without 2 [1,5], p < 0.001), and a smaller angle deviation (with 3°[ 0°,8°]; without 5°[ 0°,19°], p = 0.002). Pleural depth in millimeters was associated with increased puncture frequency (0.019[0,010,0.028]) and procedure time (0.071'[ 0.018,0.123']. The PNCL-experienced physicians performed the procedure in less time (-2.854'[-4.646',1.061']. The traverse direction toward the mediastinum diminished the frequency (toward 1[ 1,3]; away 1 [1,5], p = 0.003) and time (toward 7.5'[2',18]'; away 9'[ 3',31'], p = 0.027). The learning curve did not improve procedure performance after ten PNCL simulation rounds. Conclusions: The phantom PNCL simulation education course increased the confidence level, enhanced residents' skill acquisition, and promoted learning satisfaction. The angle reference device helped improve the outcomes of the puncture frequency and reduced angle deviation.

Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks.

Equation-Informed Neural Networks (EINNs) are developed as an efficient method for extracting the coefficients of constitutive equations. Subsequently, numerical Bayesian Inference (BI) iterations were applied to estimate the distribution of these coefficients, thereby further refining them. We could generate coefficients optimally aligned with the targeted application scenario by carefully adjusting pre-processing mapping parameters and identifying dataset preferences. Leveraging graphical representation techniques, the EINNs formulation is implemented in temperature- and strain-rate-dependent hyperbolic Garofalo, Anand, and Chaboche constitutive models to extract the corresponding coefficients for lead-free SAC305 solder material. The performance of the EINNs-based extracted coefficients, obtained from experimental results of SAC305 solder material, is comparable to existing studies. The methodology offers the dual advantage of providing the coefficients' value and distribution against the training dataset.

Biomechanical evaluation of bridge span with three implant abutment designs and two connectors for tooth-implant supported prosthesis: A finite element analysis.

Background/purpose: Bridge stability under loading was influenced by bridge span with the connector and implant abutment design. Thus, the purpose of this study was to evaluate the effects of rigid and non-rigid connector designs and pontic connections of different abutment systems in the tooth-implant supported prosthesis (TISP) at different span distances on the biomechanical stress distribution of the overall system components. Materials and methods: For comparative analysis, rigid and non-rigid bridge connections were fitted with three implant abutment systems (one-piece, two-piece and three-piece), and five implant-to-natural tooth distance configurations (12 mm, 14 mm, 16 mm, 18 mm, and 20 mm) were provided. Results: The maximum stress between TISP components occurred at the distal side of crown margin of cement1 in rigid connector with one-piece group and the bottom of the crown3 in non-rigid connector with one-piece group, while the other groups were more concentrated at the junction between the mesial side of the implant collar and the abutment. In addition, neither the rigid nor non-rigid connector model showed that stress distribution increased proportionally with the bridge span distance. Conclusion: It was clinically recommended that if the implant with a shorter bridge distance of 12 mm from the natural tooth, the rigid connection of the three-piece abutment can be used as the TISP design. If the bridge distance was 18 mm longer, the non-rigid connection of the three-piece abutment could maintain the physiological movement of the natural tooth and avoid the excessive stress on the bone crest around the implant.

Biomechanical analysis of rigid and non-rigid connection with implant abutment designs for tooth-implant supported prosthesis: A finite element analysis.

BACKGROUND/PURPOSE: The design of the connectors and implant abutments could affect the stress distribution of the tooth-implant supported prosthesis (TISP) entire system after loading. Therefore, the purpose of this study was to investigate the stress distribution of the TISP in different connectors and different implant abutments after loading. MATERIALS AND METHODS: The TISP design used in this study was divided into six models. R1, R2 and R3 represented the tooth and the one-piece, two-piece and three-piece abutment implant system connected by a rigid connector, respectively, while NR1, NR2 and NR3 were the corresponding tooth-abutment implant systems connected by a non-rigid connector. A vertical occlusal load of 50 N was applied at a right angle on the 6 occlusal points of the occlusal surface. RESULTS: As a result, regarding the maximum average stress distribution, R1 and NR1 appeared on the implant fixture, and the other four models were on the implant abutment. On the other hand, regardless of the abutment implant system, the maximum von Mises stress generated by the rigid connector was greater than the corresponding non-rigid connector in the cortical bone around implant. In addition, the three-piece abutment implant system had lower von Mises stress than the one-piece and two-piece implant systems in the cortical bone. CONCLUSION: It is concluded that by adding a flexible non-rigid connector and three-piece abutment device design to TISP, the occlusal load of the implant was dispersed, and the stress could be gradually introduced into the relatively strong implant abutment.

Solder Joint Reliability Risk Estimation by AI-Assisted Simulation Framework with Genetic Algorithm to Optimize the Initial Parameters for AI Models.

Solder joint fatigue is one of the critical failure modes in ball-grid array packaging. Because the reliability test is time-consuming and geometrical/material nonlinearities are required for the physics-driven model, the AI-assisted simulation framework is developed to establish the risk estimation capability against the design and process parameters. Due to the time-dependent and nonlinear characteristics of the solder joint fatigue failure, this research follows the AI-assisted simulation framework and builds the non-sequential artificial neural network (ANN) and sequential recurrent neural network (RNN) architectures. Both are investigated to understand their capability of abstracting the time-dependent solder joint fatigue knowledge from the dataset. Moreover, this research applies the genetic algorithm (GA) optimization to decrease the influence of the initial guessings, including the weightings and bias of the neural network architectures. In this research, two GA optimizers are developed, including the "back-to-original" and "progressing" ones. Moreover, we apply the principal component analysis (PCA) to the GA optimization results to obtain the PCA gene. The prediction error of all neural network models is within 0.15% under GA optimized PCA gene. There is no clear statistical evidence that RNN is better than ANN in the wafer level chip-scaled packaging (WLCSP) solder joint reliability risk estimation when the GA optimizer is applied to minimize the impact of the initial AI model. Hence, a stable optimization with a broad design domain can be realized by an ANN model with a faster training speed than RNN, even though solder fatigue is a time-dependent mechanical behavior.

Molecular modeling of temperature dependence of solubility parameters for amorphous polymers.

A molecular modeling strategy is proposed to describe the temperature (T) dependence of solubility parameter (δ) for the amorphous polymers which exhibit glass-rubber transition behavior. The commercial forcefield "COMPASS" is used to support the atomistic simulations of the polymer. The temperature dependence behavior of δ for the polymer is modeled by running molecular dynamics (MD) simulation at temperatures ranging from 250 up to 650 K. Comparing the MD predicted δ value at 298 K and the glass transition temperature (T(g)) of the polymer determined from δ-T curve with the experimental value confirm the accuracy of our method. The MD modeled relationship between δ and T agrees well with the previous theoretical works. We also observe the specific volume (v), cohesive energy (U(coh)), cohesive energy density (E(CED)) and δ shows a similar temperature dependence characteristics and a drastic change around the T(g). Meanwhile, the applications of δ and its temperature dependence property are addressed and discussed.