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We herein report manufacturing of dental crowns made of 5-mol% yttria partially stabilized zirconia (5Y-PSZ) with desired mechanical properties, optical translucency and dimensional accuracy using digital light processing (DLP). To this end, all processing parameters were carefully controlled and optimized. First, 5Y-PSZ particles with a bimodal distribution were prepared via calcination of as-received granules and subsequent ball-milling and then used to formulate 5Y-PSZ suspensions with a high solid loading of 50 vol% required for high densification after sintering. Dispersant content was also optimized. To provide high dimensional accuracy, initial dimensions of dental crowns for 3D printing were precisely determined by considering increase and decrease in dimensions during photopolymerization and sintering, respectively. Photopolymerization time was also optimized for a given layer thickness of 50 μm to ensure good bonding between layers. A multi-step debinding schedule with a slow heating rate was employed to avoid formation of any defects. After sintering at 1500 °C for 2 h, 5Y-PSZ could be almost fully densified without noticeable defects within layers and at interfaces between layers. They had high relative densities (99.03 ± 0.39%) with a high cubic phase content (59.1%). These characteristics allowed for achievement of reasonably high mechanical properties (flexural strength = 625.4 ± 75.5 MPa and Weibull modulus = 7.9) and % transmittance (31.4 ± 0.7%). In addition, 5Y-PSZ dental crowns showed excellent dimensional accuracy (root mean square (RMS) for marginal discrepancy = 44.4 ± 10.8 μm and RMS for internal gap = 22.8 ± 1.6 μm) evaluated by the 3D scanning technique.

Mito-TEMPO is a well-known mitochondria-specific superoxide scavenger. However, the effect of Mito-TEMPO on porcine embryo development, to our knowledge, has not been studied yet. In the present study, porcine embryos were classified into two groups (G1 and G2) based on the cytoplasm lipid contents at the zygote stage. The development of blastocysts derived from G2 zygotes was reduced (G2:16.2 ± 7.9% vs G1: 26.5 ± 5.9%; 1.6-fold, p < 0.05) compared to those from G1 zygotes. In G2 embryos, the proportion of TUNEL-positive cells was also higher than that of G1 embryos. Superoxide in G2 embryos was significantly increased compared to that in G1 embryos. Mitochondrial membrane potential and ATP production were lower in G2 embryos than in G1 embryos. Phosphorylation of Drp1 at Ser 616 increased in G1 embryos during the cleavage stages compared to that in the zygote but was not significantly different in G2 embryos. Then, the effects of Mito-TEMPO were investigated in G2 embryos. Blastocyst formation rate (G2: 19.1 ± 5.1% vs G2 + Mito-TEMPO: 28.8 ± 4.0%; 1.5-fold, p < 0.05) and mitochondrial aggregation were recovered after superoxide reduction by Mito-TEMPO treatment. Thus, we showed that Mito-TEMPO improves blastocyst development by superoxide reduction in porcine embryos in vitro .

In the shipbuilding industry, the non-destructive testing for welding quality inspection is mainly used for the permanent storage of the testing results and the radio-graphic testing which can visually inspect the interior of the welded part. Experts are required to properly detect the test results and it takes a lot of time and cost to manually Interpret the radio-graphic testing image of the structure over 500 blocks. The algorithms that automatically interpret the existing radio-graphic testing images to extract features through image pre-processing and classify the defects using neural networks, and only partial automation is performed. In order to implement the feature extraction and classification in one algorithm and to implement the overall automation, this paper proposes a method of automatically detecting welding defect using Faster R-CNN which is a deep learning basis. We analyzed the data to learn algorithms and compared the performance improvements using data augmentation method to artificially increase the limited data. In order to appropriately extract the features of the radio-graphic testing image, two internal feature extractors of Faster R-CNN were selected, compared, and performance evaluation was performed.

Recent advances in species distribution models (SDMs) associated with artificial intelligence (AI) and increased volumes of available data for model variables have allowed reliable evaluation of the potential distribution of any species. A reliable SDM requires suitable occurrence records and variables with optimal model structures. In this study, we developed three different machine learning-based SDMs [MaxEnt, random forest (RF), and multi-layer perceptron (MLP)] to predict the global potential distribution of two invasive ants under current and future climates. These SDMs showed that the potential distribution of Solenopsis invicta would be expanded by climatic change, whereas it would not significantly change for Anoplolepis gracilipes. The models were compared using model performance metrics, and the optimal model structure and spatial projection were selected. The MaxEnt exhibited high performance, while the MLP model exhibited low performance, with the largest variation by climate change. Random forest showed the smallest potential distribution area, but it was robust considering the number of occurrence records and changes in model variables. All the models showed reliable performance, but the difference in performance and projection size suggested that optimal model selection based on data availability, model variables, study objectives, or an ensemble approach was necessary to develop a comprehensive SDM to minimize modeling uncertainty. We expect that this study will help with the use of AI-based SDMs for the evaluation and risk assessment of invasive ant species.

This study aimed to develop dental resin composites containing ZnO/SiO2 ceramic particles as an antimicrobial filler for producing provisional dental restorations using the lithography-based liquid crystal display (LCD) 3D printing technique. Three types of dental resin-ceramic composites with different filler contents (0 wt%, 5 wt%, and 10 wt%) were prepared to offer high antimicrobial efficacy. Printing parameters, particularly off-time, were optimized for each composition to achieve high-quality prints. Mechanical testing demonstrated increased hardness and elastic modulus. In addition, the 10 vol% composite exhibited a three-point flexural strength of 113.4 MPa, exceeding the 100 MPa requirement specified in ISO 4049:2019 for provisional dental materials. Antimicrobial testing showed a significant reduction in Streptococcus mutans colonies, with up to 84.4% decrease for the 10 vol% composite compared to the unfilled resin. A provisional 3-unit bridge was successfully printed using the 10 vol% composite, demonstrating practical applicability.

This study aimed to develop an innovative resin composite with anti-biofouling properties, tailored to prosthesis fabrication in dentistry using a digital light processing (DLP) 3D-printing technique. The resin composite was formulated using a blend of dental monomers, with the integration of 2-methacryloyloxylethyl phosphorylcholine (MPC) with anti-biofouling behavior and γ-MPS-treated silica-zirconia powder for simultaneous mechanical reinforcement. The overall characterization of the resin composite was carried out using various contents of MPC incorporated into the resin (0–7 wt%) for examining the rheological behavior, photopolymerization, flexural strength/modulus, microstructure and anti-biofouling efficiency. The resin composite demonstrated a significant reduction in bacterial adhesion (97.4% for E. coli and 86.5% for S. aureus) and protein adsorption (reduced OD value from 1.3 ± 0.4 to 0.8 ± 0.2) with 7 wt% of MPC incorporation, without interfering with photopolymerization to demonstrate potential suitability for 3D printing without issues (p < 0.01, and p < 0.05, respectively). The incorporation and optimization of γ-MPS-treated silica-zirconia powder (10–40 vol%) enhanced mechanical properties, leading to a reasonable flexural strength (103.4 ± 6.1 MPa) and a flexural modulus (4.3 ± 0.4 GPa) at 30 vol% (n = 6). However, a further increase to 40 vol% resulted in a reduction in flexural strength and modulus; nevertheless, the results were above ISO 10477 standards for dental materials.

In this paper, an axial slot-less permanent magnet synchronous motor (ASFPMSM) was designed to increase the power density. The iron core of the stator was replaced with block coils, and the stator back yoke was removed because 3D printing can provide a wide range of structures of the stator. The proposed model also significantly impacts efficiency because it can reduce iron loss. To meet size and performance requirements, coil thickness and number of winding layers in the block, the total amount of magnet, and pole/slot combinations were considered. The validity of the proposed model was proved via finite elements analysis (FEA).

According to the concept of smart postharvest management, an information and communication technology sensor–based monitoring system is required in the juicing process to reduce losses and improve process efficiency. Such technologies are considered economically burdensome and technically challenging for small-scale enterprises to adopt. From this perspective, this study aimed to develop a smart monitoring system for the juicing processes in small-scale enterprises and to identify the optimal operating conditions based on the monitoring data. The system developed is equipped with two weight sensors attached to the twin-screw juice extractor, allowing for the automatic measurement of the weight of the raw material and the resulting juice product. The measured data are automatically transmitted and stored on a computer. Additionally, the system was designed to remotely control the speeds of the juicing and feeding screws, which are the primary controlling factors of the twin-screw juicer. Juice yield and processing time were optimized using carrots and pears. The optimal juicing and feeding speeds for pear yield were found to be 167.4 rpm and 1557 rpm, respectively; carrots achieved an optimal yield at a juicing speed of 502.2 rpm and feeding speed of 1211 rpm. In contrast, the processing time was minimized at juicing–feeding speeds of 6–6 and 7–5 for pears and carrots, respectively. Consequently, it was challenging to determine the optimal conditions for simultaneously optimizing the yield and processing time. This also suggests that the juicing process is affected by the properties of the fruits and vegetables being processed. By developing a system capable of accumulating the data necessary for the digitization of postharvest management and food processing, this research offers a valuable platform for the smart monitoring and optimization of the juicing process.

The western conifer seed bug (WCSB) Leptoglossus occidentalis (Heidemann) (Heteroptera: Coreidae) is a pest insect that causes significant losses of coniferous trees worldwide. In this study, we sought to project the potential distribution of the WCSB based on dual CLIMEX modeling and random forest (RF) analysis to obtain basic data for WCSB monitoring strategies. The CLIMEX model, a semimechanistic niche model that responds to climate‐based environmental parameters, is a species distribution model that focuses on regional climatic suitability. Given that this model can be used to select areas that are likely to reflect the climatically favorable spread of species, which we initially used CLIMEX to evaluate the potential distribution of the WCSB. The RF algorithm was used to predict the potential occurrence of WCSB and to evaluate the relative importance of environmental variables for WCSB occurrence. Using the RF model, land cover was found to be the most important variable for classifying the presence/pseudo‐absence of the WCSB, with an accuracy of 77.1%. Climatic suitability for the WCSB was predicted to be 2.4‐fold higher in Southern Europe than in Western Europe, and the WCSB was predicted to occur primarily near coniferous forests. Given that CLIMEX and RF analyses yielded different prediction results, using the findings of both models may compensate for the shortcomings of these models when used independently. Consequently, to ensure greater prediction reliability, we believe that it would be beneficial to base predictions on the combined potential distribution data obtained using both modeling approaches. This study developed an ensemble model with a dual modeling of a mechanistic model (CLIMEX) and a machine learning model (random forest). The developed models showed a reliable prediction of the potential distribution of Leptoglossus occidentalis in Europe in response to climate change.

Activity biosensors have been used recently to measure and diagnose the physiological status of dairy cows. However, owing to the variety of commercialized activity biosensors available in the market, activity data generated by a biosensor need to be standardized to predict the status of an animal and make relevant decisions. Hence, the objective of this study was to develop a standardization method for accommodating activity measurements from different sensors. Twelve Holstein dairy cows were monitored to collect 12 862 activity data from four types of sensors over five months. After confirming similar cyclic activity patterns from the sensors through correlation and regression analyses, the gamma distribution was employed to calculate the cumulative probability of the values of each biosensor. Then, the activity values were assigned to three levels (i.e., idle, normal and active) based on the defined proportion of each level, and the values at each level from the four sensors were compared. The results showed that the number of measurements belonging to the same level was similar, with less than a 10% difference at a specific threshold value. In addition, more than 87% of the heat alerts generated by the internal algorithm of three of the four biosensors could be assigned to the active level, suggesting that the current standardization method successfully integrated the activity measurements from different biosensors. The developed probability-based standardization method is expected to be applicable to other biosensors for livestock, which will lead to the development of models and solutions for precision livestock farming.