OBJECTIVE: This study aimed to assess the fit of interim crowns produced using DLP-based 3D printing with different manufacturing workflows-open and proprietary-versus milling technology.
METHODS: A total of 120 crowns were evaluated using the replica technique. The control group (Mill, n = 30) was manufactured via subtractive technology. Experimental groups were printed using a DLP printer (SprintRay Pro95). In the proprietary mode (SR100, n = 30), manufacturer resin was used with a 100-μm layer thickness (LT) and a splashing cleaning postprocessing. In the open mode, validated resin was used. Group B100 (n = 30) had a 100-μm LT, and group B50 (n = 30) had a 50-μm followed by postprocessing in an ultrasonic bath with full immersion in isopropyl alcohol. Kruskal-Wallis tests with Bonferroni correction was applied after normal analysis (α = 0.05).
RESULTS: Group B50 exhibited the best overall fit (123.87 ± 67.42 μm), which was comparable to the gold standard Milling group, which demonstrated the lowest marginal fit (p = 0.760). SR100 showed significantly poorer performance compared to Mill, B50, and B100 (p < 0.001).
CONCLUSIONS: 3D printed and milled interim crowns generally demonstrated clinically acceptable fit, with the exception of the SR100 group. Postprocessing notably influenced crown fit, with the open mode with total immersion in isopropyl alcohol being superior.
CONCLUSIONS: The present study demonstrates that the selection of an optimal manufacturing and postprocessing workflow results in superior fit for interim crowns. This enables dental professionals to evaluate protocols and ensure reliable outcomes with improved clinical outcomes in interim crown fabrication.

OBJECTIVE: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP).
METHODS: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated.
RESULTS: A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001).
CONCLUSIONS: The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver\'s safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in 1-min windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13534-023-00313-8.

Support removal is one of the thorny issues faced by laser powder bed fusion (LPBF). In particular, the efficient and safe removal of support structures from the thin-walled parts and obtaining high-quality surfaces still remains a challenge owing to their sensitivity to machining. An in-depth understanding of the material response behavior of LPBF thin-walled parts when removing support structures is necessary for overcoming this challenge. The work is divided into two parts: revealing the support removal mechanism and proposing a solution to improve the support machinability. First, the machinability of support structures on thin-walled parts with different thicknesses at different cutting depths was thoroughly investigated. Experimental investigation on cutting force, surface morphology, and deflection were carried out. The results show that cutting forces increase gradually at each cut owing to the tilt and collapse of support structures. The surface morphology is improved as the sample thickness increases but deteriorated as the cutting depth increases. Second, a novel solution of adding resin is proposed to improve the support machinability and good results have been achieved. The z-direction cutting forces for 0.3 and 0.4 mm thickness samples are reduced by 72.6% and 64.6%, respectively, and no deflection of the sample is observed after support removal. Moreover, finite element method simulations are established to further explain the support removal mechanism.

Current wood-plastic materials available for selective laser sintering (SLS) are limited and often suffer from low-quality and mechanical strength. In this study, a new composite of peanut husk powder (PHP)/polyether sulfone (PES) was developed for SLS additive manufacturing (AM). To use the biomass waste materials in AM technology, such as furniture and wood flooring, this composite based on agricultural waste is environmentally friendly, energy efficient, and low in production cost. SLS parts made from PHPC had good mechanical strength and excellent dimensional precision (DP). The thermal decomposition temperature of composite powder components and the glass transition temperatures of PES and various PHPC were determined first to prevent the PHPC parts from warping during sintering. Furthermore, the formability of PHPC powders in various mixing ratios was examined through single-layer sintering; and the density, mechanical strength, surface roughness, and DP of the sintered parts were measured. Particle distribution and microstructure of the powders and the SLS parts (both before and after breakage in mechanical tests) were inspected using scanning electron microscopy. According to the combined results, a ratio of PHP/PES = 10/90 (w/w) resulted in the best forming quality and mechanical strength compared with other ratios and pure PES. The measured density, impact strength, tensile strength, and bending strength for this PHPC are 1.1825 g/cm3, 2.12 kJ/cm2, 6.076 MPa, and 14.1 MPa, respectively. After wax infiltration, these parameters were further improved to 2.0625 g/cm3, 2.96 kJ/cm2, 7.476 MPa, and 15.7 MPa, respectively.

Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager-Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control.

A postprocessing technique for mixed finite element methods for the Cahn-Hilliard equation is developed and analyzed. Once the mixed finite element approximations have been computed at a fixed time on the coarser mesh, the approximations are postprocessed by solving two decoupled Poisson equations in an enriched finite element space (either on a finer grid or a higher-order space) for which many fast Poisson solvers can be applied. The nonlinear iteration is only applied to a much smaller size problem and the computational cost using Newton and direct solvers is negligible compared with the cost of the linear problem. The analysis presented here shows that this technique remains the optimal rate of convergence for both the concentration and the chemical potential approximations. The corresponding error estimate obtained in our paper, especially the negative norm error estimates, are non-trivial and different with the existing results in the literatures.