Validation and Practical Applications of Performance in a 6-Min Rowing Test in the Danish Armed Forces.

Personnel of the Danish Armed Forces must complete a yearly basic physical fitness test consisting of a Cooper's 12-min run test (CRT) and four strength-related bodyweight exercises. However, there is no validated alternative to the CRT allowing injured or sailing personnel to conduct the yearly basic physical fitness test. Therefore, the aim of this study was to validate performance in a 6-min rowing ergometer test (6MRT) against CRT performance. Thirty-one individuals (M/F: 20/11, age: 34 ± 12 years) employed at the Danish Armed Forces completed testing on two independent days; (I) the CRT on an outdoor track and (II) a 6MRT with pulmonary measurements of breath-by-breath oxygen uptake. In addition, 5 participants (M/F: 4/1, age: 40 ± 10 years) completed re-testing of the 6MRT. No difference was observed between VO2max estimated from the CRT and measured during the 6MRT. Absolute VO2max correlated strongly (r = 0.95; p < 0.001) to performance in the 6MRT, and moderately (r = 0.80; p < 0.001) to performance in the CRT. Bodyweight (BW) and fat free mass (FFM) correlated stronger to performance in the 6MRT compared to the CRT. 6MRT re-testing yielded similar performance results. The 6MRT is a valid and reliable alternative to the CRT, allowing injured or sailing personnel of the Danish Armed Forces to complete the basic physical fitness test as required, albeit 6MRT performance demands must be made relative to bodyweight.

Detecting glass and metal in consumer trash bags during waste collection using convolutional neural networks.

We present a proof-of-concept method to classify the presence of glass and metal in consumer trash bags. With the prevalent utilization of waste collection trucks in municipal solid waste management, the aim of this method is to help pinpoint the locations where waste sorting quality is below accepted standards, making it possible and more efficient to develop tailored procedures that can improve the waste sorting quality in areas with the most urgent needs. Using trash bags containing various amounts of glass and metal, in addition to common waste found in households, we use a combination of sound recording and a beat-frequency oscillation metal detector as inputs to a machine learning algorithm to identify the occurrence of glass and metal in trash bags. A custom-built test rig was developed to mimic a real waste collection truck, which was used to test different sensors and build the datasets. Convolutional neural networks were trained for the classification task, achieving accuracies of up to 98%. These promising results support this method's potential implementation in real waste collection trucks, enabling location-specific and long-term monitoring of consumer waste sorting quality, which can provide decision support for waste management systems, and research on consumer behavior.