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Particular attention will be addressed to the waste pre-treatment, to the bioplastic formulation and to its processing, as well as to the mechanical and physical properties of the obtained materials.The rise of three-dimensional (3D) printing technology has changed the face of dentistry over the past decade. 3D printing is a versatile technique that allows the fabrication of fully automated, tailor-made treatment plans, thereby delivering personalized dental devices and aids to the patients. It is highly efficient, reproducible, and provides fast and accurate results in an affordable manner. With persistent efforts among dentists for refining their practice, dental clinics are now acclimatizing from conventional treatment methods to a fully digital workflow to treat their patients. Apart from its clinical success, 3D printing techniques are now employed in developing haptic simulators, precise models for dental education, including patient awareness. In this narrative review, we discuss the evolution and current trends in 3D printing applications among various areas of dentistry. We aim to focus on the process of the digital workflow used in the clinical diagnosis of different dental conditions and how they are transferred from laboratories to clinics. A brief outlook on the most recent manufacturing methods of 3D printed objects and their current and future implications are also discussed.In membrane processes, a spacer is known to play a key role in the mitigation of membrane fouling. In this study, the effect of electric polarization on a graphene-blended polymer spacer (e.g., poly(lactic acid), PLA) for organic fouling on membrane surfaces was investigated. A pristine PLA spacer (P-S), a graphene-blended spacer (G-S), and an electrically polarized graphene-blended spacer (EG-S) were successfully fabricated by 3D printing. Organic fouling tests were conducted by the 5-h filtration of CaCl2 and a sodium alginate solution through commercially available membranes, which were placed together with the fabricated spacers. Membranes utilizing P-S, G-S, and EG-S were characterized in terms of the fouling amount on the membrane surface and fouling roughness. Electrostatic forces of EG-S provided 70% less and 90% smoother fouling on the membrane surface, leading to an only 14% less water flux reduction after 5 h of fouling. The importance of nanomaterial blending and polarization was successfully demonstrated herein.Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. selleck compound The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.Debate remains regarding the utility of mechanical axis alignment as a predictor of durability after total knee arthroplasty (TKA). Our study aimed to assess the effects of coronal alignment on implant durability, clinical outcomes, and radiographic results with a single fixed-bearing TKA design. All patients undergoing primary cemented TKA of a single design (Stryker Triathlon) from 2005-2007 with >10 years of follow-up and available pre-operative and post-operative hip-knee-ankle radiographs were included (n = 89). Radiographs were measured to determine coronal alignment and assessed for loosening. Mean preoperative mechanical axis alignment was -6° ± 6.7° (varus, range, -16°-23°), while mean post-operative alignment was -1° ± 2.7° (varus, range, -3°-15°). The aligned group was defined as knees with a post-operative mechanical axis of 0° ± 3° (n = 73) and the outlier group as those outside this range (n = 16). No patients underwent revision. Ten-year survivorship free from any reoperation was 99% and 100% in the aligned and outlier groups, respectively (p = 0.64). Knee Society scores improved significantly in both groups (p less then 0.001) and did not differ at final follow-up (p = 0.15). No knees demonstrated radiographic evidence of loosening. Post-operative mechanical axis alignment within 3° of neutral was not associated with improved implant durability, clinical outcomes, or radiographic results at 10 years following primary TKA.There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin Deep Learning Neural Network-Frequency Ratio (DLNN-FR), Deep Learning Neural Network-Weights of Evidence (DLNN-WOE), Alternating Decision Trees-Frequency Ratio (ADT-FR) and Alternating Decision Trees-Weights of Evidence (ADT-WOE).