Sprinting with prosthetic versus biological legs: insight from experimental data

Running-prostheses have enabled exceptional athletes with bilateral leg amputations to surpass Olympic 400 m athletics qualifying standards. Due to the world-class performances and relatively fast race finishes of these athletes, many people assume that running-prostheses provide users an unfair advantage over biologically legged competitors during long sprint races. These assumptions have led athletics governing bodies to prohibit the use of running-prostheses in sanctioned non-amputee (NA) competitions, such as at the Olympics. However, here we show that no athlete with bilateral leg amputations using running-prostheses, including the fastest such athlete, exhibits a single 400 m running performance metric that is better than those achieved by NA athletes. Specifically, the best experimentally measured maximum running velocity and sprint endurance profile of athletes with prosthetic legs are similar to, but not better than those of NA athletes. Further, the best experimentally measured initial race acceleration (from 0 to 20 m), maximum velocity around curves, and velocity at aerobic capacity of athletes with prosthetic legs were 40%, 1–3% and 19% slower compared to NA athletes, respectively. Therefore, based on these 400 m performance metrics, use of prosthetic legs during 400 m running races is not unequivocally advantageous compared to the use of biological legs.

Performance recovery of plasma actuators in wet conditions

Plasma actuators have been extensively studied for flow control applications. While these studies have been traditionally focused on characterizing their performances as flow control devices, the performance of plasma actuators under adverse conditions like light rain remains to be less explored. This paper seeks to study the effects of water adhesion from droplets directly sprayed on to a plasma actuator using thrust recovery as the performance metric. It was found in all tests that wet actuators quickly recover plasma glow, before gradually regaining performance comparable to the dry actuator. The measured thrust for the wet actuator after 5 seconds of operation recovered by 46% and 42% of the thrust of the dry actuator for 50.0-62.5 g/m2 and 125-150 g/m2 of sprayed water droplets, respectively. At 22.5 kVpp and 14 kHz, the highest thrust recovery was recorded at 84% of that of the dry actuator after 80 seconds of operation. For 17.5 kVpp and 14 kHz the wet thrust recovered by 79%, while for 22.5 kVpp and 10 kHz the wet thrust recovered by 68% of their dry counterpart in 80 seconds. For 17.5 kVpp and 14 kHz, the thrust almost fully recovered in comparison to the dry actuator after about 290 seconds of operation. These results indicate that both applied voltage and operating frequency plays a critical role in the performance recovery while the latter may have a stronger influence. Performance recovery for a wet serpentine shaped plasma actuator is also included for general applicability. The power data in all cases show that wet actuators consume more power which with time gradually approach the dry actuator power data. This because during the initial stages of operation, the rolling mean current of the wet actuator is higher than the dry actuator even though the ionization spikes of dry actuator is stronger.

Improving Performance of User Pair Using Reconfigurable Intelligent Surfaces

With the given scope for new use cases and the demanding needs of future 6th generation (6G) wireless networks, the development of wireless communications looks exciting. The propagation medium has been viewed as a randomly behaving entity between the transmitter and the receiver since traditional wireless technology, degrading the quality of the received signal due to the unpredictable interactions of the broadcast radio waves with the surrounding objects. On the other hand, network operators could now manipulate electromagnetic radiation to remove the negative impacts of natural wireless propagation due to the recent arrival of reconfigurable intelligent surfaces (RIS) in wireless communications. According to recent findings, the RIS mechanism benefits nonorthogonal multiple access (NOMA), which can effectively deliver effective transmissions. For simple design, of RIS-NOMA system, fixed power allocation scheme for NOMA is required. The main system performance metric, i.e., outage probability, needs to be considered to look at the efficiency and capability of transmission mode relying on RIS and NOMA schemes, motivated by the potential of these developing technologies. As major performance metrics, we derive analytical representations of outage probability, and throughput and an accurate approximation is obtained for the outage probability. Numerical results are conducted to validate the exactness of the theoretical analysis. It is found that increasing the higher number of reflecting elements in the RIS can significantly boost the outage probability performance, and the scenario with only the RIS link is also beneficial. In addition, it is desirable to deploy the RIS-NOMA since it is indicated that better performance compared with the traditional multiple access technique.

Understanding the Performance Guarantee of Physical Topology Design for Optical Circuit Switched Data Centers

As a first step of designing O ptical-circuit-switched D ata C enters (ODC), physical topology design is critical as it determines the scalability and the performance limit of the entire ODC. However, prior works on ODC have not yet paid much attention to physical topology design, and the adopted physical topologies either scale poorly, or lack performance guarantee. We offer a mathematical foundation for the design and performance analysis of ODC physical topologies in this paper. We introduce a new performance metric β(G ) to evaluate the gap between a physical topology G and the ideal physical topology. We develop a coupling technique that bypasses a significant amount of computational complexity of calculating β(G). Using β(G ) and the coupling technique, we study four physical topologies that are representative of those in literature, analyze their scalabilities and prove their performance guarantees. Our analysis may provide new guidance for network operators to design better physical topologies for their ODCs.

Utilization rate of the fleet: a novel performance metric for a novel shared mobility

Car-sharing systems have irrupted in our cities following the shared mobility paradigm. They have evolved the personal mobility market from product-based into service-oriented, which ultimately provides a positive impact on the city’s sustainability. Car sharing systems are a complex interactive service, whose dynamics can dramatically affect its operational viability. In order to better asses this viability, we must rely on data to produce novel metrics that characterize both the user behavior and the service performance. Up to date, research has focused on modeling the demand on the basis of the number of rentals that start within a specific time slot. However, this approach seems unable to provide a representative metric of the performance of a car-sharing system. In this paper, we propose a novel metric, the utilization rate of the fleet, which considers the precise number of vehicles within a fleet that are in service every minute of the day. From this basic metric, we derive a key performance indicator (KPI) to reflect the viability of any car-sharing system in economic and sustainability terms. We have applied this new metric and KPI to a dataset with 449 days of car2go data, collected in 10 European cities.

US2RO: Union of Superpoints to Recognize Objects

The creation of interactive virtual reality (VR) applications from 3D scanned content usually includes a lot of manual and repetitive work. Our research aim is to develop agents that recognize objects to enhance the creation of interactive VR applications. We trained partition agents in our superpoint growing environment that we extended with an expert function. This expert function solves the sparse reward signal problem of the previous approaches and enables to use a variant of imitation learning and deep reinforcement learning with dense feedback. Additionally, the function allows to calculate a performance metric for the degree of imitation for different partitions. Furthermore, we introduce an environment to optimize the superpoint generation. We trained our agents with 1182 scenes of the ScanNet data set. More specifically, we trained different neural network architectures with 1170 scenes and tested their performance with 12 scenes. Our intermediate results are promising such that our partition system might be able to assist the VR application development from 3D scanned content in near future.

Functional IT Complementarity and Hospital Performance in the United State: A Longitudinal Investigation

For healthcare practitioners and policymakers, one of the most challenging problems is understanding how to implement health information technology (HIT) applications in a way that yields the most positive impacts on quality and cost of care. We identify four clinical HIT functions which we label as order entry and management (OEM), decision support (DS), electronic clinical documentation (ECD), and results viewing (RV). We view OEM and DS as primary clinical functions and ECD and RV as support clinical functions. Our results show that no single combination of applications uniformly improves clinical and experiential quality and reduces cost for all hospitals. Thus, managers must assess which HIT interactions improve which performance metric under which conditions. Our results suggest that synergies can be realized when these systems are implemented simultaneously. Additionally, synergies can occur when support HIT is implemented before primary HIT and irrespective of the order in which primary HITs are implemented. Practitioners should also be aware that the synergistic effects of HITs and their impact on cost and quality are different for chronic and acute diseases. Our key message to top managers is to prioritize different combinations of HIT contingent on the performance variables they are targeting for their hospitals but also to realize that technology may not impact all outcomes.

A Beacon-based Approach for RF Source localization in Outdoor NLOS Environment for Search and Rescue Missions

Due to its low implementation cost, the combination of the Received Signal Strength (RSS) with the Angle of Arrival (AOA) measurements is one of the solutions for Radio Frequency (RF) source localization, especially in a Non-Line of Sight (NLOS) environment. It is critical to determine the search space for a person who is lost in rural areas where the mobile network is unavailable due to a lack of Base Tower Stations (BTS) in order to reduce search time. In this paper, we introduce a new beacon-based approach for RF source localization, where the RF signal is received in NLOS after 1-bounce reflection, by combining the information coming from both the RSS-AOA sensors and the beacons, which are used as helpers- that move along a determined path. The proposed approach relies on determining the reflector’s pose first, after which the RF source is localized. The work has been verified in simulation and the Root Mean Square Error (RMSE) is used as a performance metric for RF source localization. Results show that our proposed approach has the lowest RMSE among localization methods mentioned in the literature under the same conditions. HIGHLIGHTS A new beacon-based approach for RF source localization in Non-Line Of Sight (NLOS) condition A reflector’s pose is determined based on the signal received from beacons The reflector’s pose is used to determine the location of the RF source One bounce reflection is considered since the chance of receiving RF signal with more reflections is very low GRAPHICAL ABSTRACT

Technical note: PMR – a proxy metric to assess hydrological model robustness in a changing climate

The ability of hydrological models to perform in climatic conditions different from those encountered in calibration is crucial to ensure a reliable assessment of the impact of climate change on river regimes and water availability. However, most evaluation studies based on the differential split-sample test (DSST) endorsed the consensus that rainfall–runoff models lack climatic robustness. Models applied under climatologically different conditions typically exhibit substantial errors in streamflow volumes. In this technical note, we propose a new performance metric to evaluate model robustness without applying the DSST, and it can be performed with a single hydrological model calibration. The proxy for model robustness (PMR) is based on the systematic computation of model error on sliding sub-periods of the whole streamflow time series. We demonstrate that the PMR metric shows patterns similar to those obtained with the DSST for a conceptual model on a set of 377 French catchments. An analysis of the sensitivity to the length of the sub-periods shows that this length influences the values of the PMR and its equivalency with DSST biases. We recommend a range of a few years for the choice of sub-period lengths, although this should be context dependent. Our work makes it possible to evaluate the temporal transferability of any hydrological model, including uncalibrated models, at a very low computational cost.