Implementation Challenges of 3D Printing in Prosthodontics: A Ranking-Type Delphi

The reduction in cost and increasing benefits of 3D printing technologies suggest the potential for printing dental prosthetics. However, although 3D printing technologies seem to be promising, their implementation in practice is complicated. To identify and rank the greatest implementation challenges of 3D printing in dental practices, the present study surveys dentists, dental technicians, and 3D printing companies using a ranking-type Delphi study. Our findings imply that a lack of knowledge is the most crucial obstacle to the implementation of 3D printing technologies. The high training effort of staff and the favoring of conventional methods, such as milling, are ranked as the second and third most relevant factors. Investment costs ranked in seventh place, whereas the lack of manufacturing facilities and the obstacle of print duration ranked below average. An inclusive implementation of additive manufacturing could be achieved primarily through the education of dentists and other staff in dental practices. In this manner, production may be managed internally, and the implementation speed may be increased.

XGest: Enabling Cross-Label Gesture Recognition with RF Signals

Extensive efforts have been devoted to human gesture recognition with radio frequency (RF) signals. However, their performance degrades when applied to novel gesture classes that have never been seen in the training set. To handle unseen gestures, extra efforts are inevitable in terms of data collection and model retraining. In this article, we present XGest, a cross-label gesture recognition system that can accurately recognize gestures outside of the predefined gesture set with zero extra training effort. The key insight of XGest is to build a knowledge transfer framework between different gesture datasets. Specifically, we design a novel deep neural network to embed gestures into a high-dimensional Euclidean space. Several techniques are designed to tackle the spatial resolution limits imposed by RF hardware and the specular reflection effect of RF signals in this model. We implement XGest on a commodity mmWave device, and extensive experiments have demonstrated the significant recognition performance.

Motivations for the training effort of Romanian masters athletes

The aim of this study was to investigate the motivations of Romanian Masters athletes to train for endurance running in order to participate in half-marathon competitions. The research method used was the survey. This tool consisted of a questionnaire that was purposely developed for the present research. Out of the 111 experienced respondents practising running for 13 years on average, 61.8% are men and 38.2% are women. The results are different and are mainly focused on reaching a state of well-being (67%) for both men (67.2%) and women (66.7%); there are significant differences between the 35-44 and 55+ age categories (t = 2.776, p < 0.01). This motivation has contributed to maintaining and improving physical performance with aging. Women are more motivated than men to run for health benefits. In conclusion, we believe that paying attention tomotivation is important in terms of encouraging people of all ages to play outdoor sports, promoting a healthy lifestyle based on exercise and managing grassroots sport. The Masters athlete is a rich source of information regarding a person’s ability to maintain maximum physical performance and physiological function as they get older. The impressive capacity for physical performance and physiological functioning makes Masters athletes a model for society.

AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task

Sensor fusion has gained a great deal of attention in recent years. It is used as an application tool in many different fields, especially the semiconductor, automotive, and medical industries. However, this field of research, regardless of the field of application, still presents different challenges concerning the choice of the sensors to be combined and the fusion architecture to be developed. To decrease application costs and engineering efforts, it is very important to analyze the sensors’ data beforehand once the application target is defined. This pre-analysis is a basic step to establish a working environment with fewer misclassification cases and high safety. One promising approach to do so is to analyze the system using deep neural networks. The disadvantages of this approach are mainly the required huge storage capacity, the big training effort, and that these networks are difficult to interpret. In this paper, we focus on developing a smart and interpretable bi-functional artificial intelligence (AI) system, which has to discriminate the combined data regarding predefined classes. Furthermore, the system can evaluate the single source signals used in the classification task. The evaluation here covers each sensor contribution and robustness. More precisely, we train a smart and interpretable prototype-based neural network, which learns automatically to weight the influence of the sensors for the classification decision. Moreover, the prototype-based classifier is equipped with a reject option to measure classification certainty. To validate our approach’s efficiency, we refer to different industrial sensor fusion applications.

Passive Visible Light Sensing of Retroreflective Foils on a Moving Object for Indoor Application

We present a novel approach to perform passive visible light sensing of retroreflective foils mounted on a moving object by utilizing low-cost hardware combined with a self-developed, low complex software algorithm with minimal training effort for successful classification. Therewith, we show the feasibility of utilizing the visible light spectrum not only for illumination, but also to perform sensing tasks, which consequently will lead to less energy consumption, no need for active sensors on the moving object, and finally no necessity of wireless radio frequency communication between the object and the processing device.

NERINT STRATEGIC ANALYSIS

The acceleration of pace of international events and processesrequires a qualified analysis without the political-journalistic bias that often characterizes them. Thus, in addition to theoretical-analytical articles, we consider it necessary to publish a brief evaluation of important current events. To this end, the Brazilian Center for Strategy and International Relations (NERINT), member of the Center for International Studies on Government (CEGOV-UFRGS), launched the NERINT Strategic Analysis series, with the contribution of its specialized researchers and guests with thematic expertise.It will be published at the end of each volume of Austral: BrazilianJournal of Strategy and International Relations, starting with an assessment of Post-Trump Diplomacy, conflicts in Russia’s “near abroad” and the Strategic Lessons of World War II on its 75th anniversary. Since the 1990s, Itamaraty has been promoting the formation of qualified national academic personnel on themes and countries relevant to Brazilian diplomacy, business and defense. This training effort, through the promotion and funding of graduate courses, is paying off, and Brazil already has professors and researchers at an international level.

Improvement of the passing quality of an American football training machine

In American football, high quality training focused on catching is currently not done with passing machines due to their poor pass accuracy and precision. From a coach’s point of view, accurate and precise passing machines are needed to relieve the quarterback from too much training effort. The two aims of this study were to increase the precision of a passing machine and develop an accurate pass prediction model for it. To meet the two aims and provide evidence that a passing machine can be precise and accurate enough for high quality training, an automated passing machine was developed and two experiments were carried out. The results of the first experiment showed that the machine performs with a precision within ±1% of the throwing distance for 218 of the 225 passes. The second experiment resulted in a pass prediction model, which is based on 55 videos and a fitting approach using a neural network. The model estimates the machine configuration for a pass to a targeted point in space. In regard to precision and accuracy, the performance of the machine exceeds the performance of a skilled quarterback. This project improves the state of the art of passing machines for American football and opens possibilities for research in various fields like motion analysis for catches, hand-eye coordination and performance analysis of athletes.

Implementation of an online emergency education programme at a public university in Mexico

The Universidad Autónoma de Nuevo León (UANL) is a large public university situated in Monterrey, Mexico. Most of the programmes, including a pharmacy related programme (Químico Farmacéutico Biólogo/Chemist Pharmacist Biologist), were running at the university until the pandemic. A lockdown was put in place where it was established that schools and non-essential jobs should be carried out from home. Since then, the UANL has started a training effort to migrate all current classes to online emergency educational schemes. Microsoft Teams was designated as the main platform. Students and faculty members were trained in its use and, after one month, classes were successfully restarted on this platform. The chosen platform was used to create virtual classrooms, problem-based learning was encouraged, and videos and discussion panels were used especially in place of pre-COVID-19 planned laboratory classes. The semester ended with good results, but faculty member training continues and the adaptation to a better organised online programme is now running. Options to try to compensate the lack of in person laboratory classes are still being explored.

Reducing Underflow in Mixed Precision Training by Gradient Scaling

By leveraging the half-precision floating-point format (FP16) well supported by recent GPUs, mixed precision training (MPT) enables us to train larger models under the same or even smaller budget. However, due to the limited representation range of FP16, gradients can often experience severe underflow problems that hinder backpropagation and degrade model accuracy. MPT adopts loss scaling, which scales up the loss value just before backpropagation starts, to mitigate underflow by enlarging the magnitude of gradients. Unfortunately, scaling once is insufficient: gradients from distinct layers can each have different data distributions and require non-uniform scaling. Heuristics and hyperparameter tuning are needed to minimize these side-effects on loss scaling. We propose gradient scaling, a novel method that analytically calculates the appropriate scale for each gradient on-the-fly. It addresses underflow effectively without numerical problems like overflow and the need for tedious hyperparameter tuning. Experiments on a variety of networks and tasks show that gradient scaling can improve accuracy and reduce overall training effort compared with the state-of-the-art MPT.

Room-Level Fall Detection Based on Ultra-Wideband (UWB) Monostatic Radar and Convolutional Long Short-Term Memory (LSTM)

Timely calls for help can really make a difference for elders who suffer from falls, particularly in private locations. Considering privacy protection and convenience for the users, in this paper, we approach the problem by using impulse–radio ultra-wideband (IR-UWB) monostatic radar and propose a learning model that combines convolutional layers and convolutional long short term memory (ConvLSTM) to extract robust spatiotemporal features for fall detection. The performance of the proposed scheme was evaluated in terms of accuracy, sensitivity, and specificity. The results show that the proposed method outperforms convolutional neural network (CNN)-based methods. Of the six activities we investigated, the proposed method can achieve a sensitivity of 95% and a specificity of 92.6% at a range of 8 meters. Further tests in a heavily furnished lounge environment showed that the model can detect falls with more than 90% sensitivity, even without re-training effort. The proposed method can detect falls without exposing the identity of the users. Thus, the proposed method is ideal for room-level fall detection in privacy-prioritized scenarios.