Intimate contact development under LAFP-specific thermal and mechanical boundary conditions/interactions and the effect of process parameters are investigated. One-layer, unidirectional strips of CF/PEKK material were placed with different process parameters on a flat tool surface to create different intimate contact conditions. The concept of effective intimate contact, which is based on the resin content at the surface, is introduced and a methodology to measure it from surface micrographs is provided. Degree of effective intimate contact measured from the samples was compared with the existing intimate contact models. The temperature history in the compaction zone was estimated with a finite element model and pressure sensitive films were used to determine the compaction pressure. It is shown that in addition to the squeeze flow mechanism, which is the base for the current intimate contact models, through-thickness percolation flow of the resin needs to be considered to explain the effective intimate contact development.
Oral cancer is among the deadliest types of malignancy due to the late stage at which it is usually diagnosed, leaving the patient with an average five-year survival rate of less than 50%. The booming field of biosensing and point of care diagnostics can, in this regard, play a major role in the early detection of oral cancer. Saliva is gaining interest as an alternative biofluid for non-invasive diagnostics, and many salivary biomarkers of oral cancer have been proposed. While these findings are promising for the application of salivaomics tools in routine practice, studies on larger cohorts are still needed for clinical validation. This review aims to summarize the most recent development in the field of biosensing related to the detection of salivary biomarkers commonly associated with oral cancer. An introduction to oral cancer diagnosis, prognosis and treatment is given to define the clinical problem clearly, then saliva as an alternative biofluid is presented, along with its advantages, disadvantages, and collection procedures. Finally, a brief paragraph on the most promising salivary biomarkers introduces the sensing technologies commonly exploited to detect oral cancer markers in saliva. Hence this review provides a comprehensive overview of both the clinical and technological advantages and challenges associated with oral cancer detection through salivary biomarkers.
Sensors that can detect external stimuli and perceive the surrounding areas could offer an ability for soft biomimetic robots to use the sensory feedback for closed-loop control of locomotion. Although various types of biomimetic robots have been developed, few systems have included integrated stretchable sensors and interconnectors with miniaturized electronics. Here, we introduce a soft, stretchable nanocomposite system with built-in wireless electronics with an aim for feedback–loop motion control of a robotic earthworm. The nanostructured strain sensor, based on a carbon nanomaterial and a low-modulus silicone elastomer, allows for seamless integration with the body of the soft robot that can accommodate large strains caused by bending, stretching, and physical interactions with obstacles. A scalable, cost-effective, and screen-printing method manufactures an array of the strain sensors that are conductive and stretchable over 100% with a gauge factor over 38. An array of nanomembrane interconnectors enables a reliable connection between soft sensors and wireless electronics while tolerating the robot’s multimodal movements. A set of computational and experimental studies of soft materials, stretchable mechanics, and hybrid packaging provides the key design factors for a reliable, nanocomposite sensor system. The miniaturized wireless circuit, embedded in the robot joint, offers real-time monitoring of strain changes during the motions of a robotic segment. Collectively, the soft sensor system presented in this work shows great potential to be integrated with other flexible, stretchable electronics for applications in soft robotics, wearable devices, and human-machine interfaces.
All-electric and hybrid-electric aircraft are a future transport goal and a possible ‘green’ solution to increasing climate-related pressures for aviation. Ensuring the safety of passengers is of high importance, informed through appropriate reliability predictions to satisfy emerging flight certification requirements. This paper introduces another important consideration related to redundancy offered by multiplex electric motors, a maturing technology which could help electric aircraft manufacturers meet the high reliability targets being set. A concept design methodology is overviewed involving a symbolic representation of aircraft and block modelling of two important figures of merit, reliability, and efficiency, supported by data. This leads to a comparative study of green aircraft configurations indicating which have the most potential now, and in the future. Two main case studies are then presented: an electric tail rotor retrofitted to an existing turbine powered helicopter (hybrid) and an eVTOL aircraft (all-electric), demonstrating the impact of multiplex level and number of propulsion channels on meeting target reliabilities. The paper closes with a summary of the important contribution to be made by multiplex electric machines, well as the advancements necessary for green VTOL aircraft sub-systems, e.g., power control unit and batteries, to improve reliability predictions and safety further.
In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been used to assess the effect of ambient temperature on energy consumption and range, considering various reference driving cycles. The results are supported and validated using data available from an experimental campaign where the Nissan Leaf was driven to depletion across a broad range of winter ambient temperatures. The effect of ambient temperature and the consequent accessories consumption due to cabin heating are shown to be remarkable. For instance, in case of Federal Urban Driving Schedule (FUDS), simplified FUDS (SFUDS), and New European Driving Cycle (NEDC) driving cycles, the range exceeds 150 km at 20 °C, while it reduces to about 85 km and 60 km at 0 °C and −15 °C, respectively. Finally, a sensitivity analysis is reported to assess the impact of the hypotheses in the battery model and of making different assumptions on the regenerative braking efficiency.
Keeping cognitive stress at a healthy range can improve the overall quality of life: helping subjects to decrease their high levels of arousal, which will make them relaxed, and elevate their low levels of arousal, which could increase their engagement. With recent advances in wearable technologies, collected skin conductance data provides us with valuable information regarding ones’ cognitive stress-related state. In this research, we aim to create a simulation environment to control a cognitive stress-related state in a closed-loop manner. Toward this goal, by analyzing the collected skin conductance data from different subjects, we model skin conductance response events as a function of simulated environmental stimuli associated with cognitive stress and relaxation. Then, we estimate the hidden stress-related state by employing Bayesian filtering. Finally, we design a fuzzy control structure to close the loop in the simulation environment. Particularly, we design two classes of controllers: (1) an inhibitory controller for reducing cognitive stress and (2) an excitatory controller for increasing cognitive stress. We extend our previous work by implementing the proposed approach on multiple subjects’ profiles. Final results confirm that our simulated skin conductance responses are in agreement with experimental data. In a simulation study based on experimental data, we illustrate the feasibility of designing both excitatory and inhibitory closed-loop wearable-machine interface architectures to regulate the estimated cognitive stress state. Due to the increased ubiquity of wearable devices capable of measuring cognitive stress-related variables, the proposed architecture is an initial step to treating cognitive disorders using non-invasive brain state decoding.
Blood potassium concentration ([K+]) influences the electrocardiogram (ECG), particularly T-wave morphology. We developed a new method to quantify [K+] from T-wave analysis and tested its clinical applicability on data from dialysis patients, in whom [K+] varies significantly during the therapy. To elucidate the mechanism linking [K+] and T-wave, we also analysed data from long QT syndrome type 2 (LQT2) patients, testing the hypothesis that our method would have underestimated [K+] in these patients. Moreover, a computational model was used to explore the physiological processes underlying our estimator at the cellular level. We analysed 12-lead ECGs from 45 haemodialysis and 12 LQT2 patients. T-wave amplitude and downslope were calculated from the first two eigenleads. The T-wave slope-to-amplitude ratio (TS/A) was used as starting point for an ECG-based [K+] estimate (KECG). Leave-one-out cross-validation was performed. Agreement between KECG and reference [K+] from blood samples was promising (error: −0.09 ± 0.59 mM, absolute error: 0.46 ± 0.39 mM). The analysis on LQT2 patients, also supported by the outcome of computational analysis, reinforces our interpretation that, at the cellular level, delayed-rectifier potassium current is a main contributor of KECG correlation to blood [K+]. Following a comprehensive validation, this method could be effectively applied to monitor patients at risk for hyper/hypokalemia.
Currently, all space missions rely on propellants produced on Earth. In order to make space exploration and extraterrestrial habitation sustainable, technologies must be developed to enable propellant manufacturing off-planet. Extraterrestrial propellant production will drive down costs, increasing access to locations near and far from Earth. However, even though there is agreement that off-planet propellant manufacture is an important early step in driving down space mission costs, no minimum viable product (MVP) for in-situ propellant exists. This is primarily due to the difficulty of storage of typical in-situ derived propellants. The Moon is a near-term source of resources, and frozen polar water is one of the most available. Hydrolox is a high specific impulse fuel that can be produced from lunar water. However, it is challenging to handle because hydrogen and oxygen must be stored at cryogenic temperatures for use as a liquid propellent. While work is being undertaken to build a system capable of managing cryogenic propellant, such systems are likely to have mass and capital requirements and development timelines of more than a decade that exclude cryogens from upcoming lunar missions. Storable propellant options exist, but most require carbon or nitrogen, which are not widely available on the lunar surface. Furthermore, many, such as hydrazine, are highly toxic. Orbit Fab has undertaken a trade study of various propulsion systems proposed to date. One nontoxic, storable monopropellant stands out for near-term in-situ resource utilization (ISRU): high-concentration hydrogen peroxide (HTP), which can be made directly from pure water. HTP decomposes catalytically into oxygen and water vapor and provides sufficient specific impulse to power a lunar ascent vehicle. Few suppliers of HTP exist today, and those that do are centralized and expensive, stifling development and making it difficult to rely on as a propellant, even on Earth. Orbit Fab is developing a system that radically simplifies the production of hydrogen peroxide while increasing its availability. Such a system could enable in-situ HTP use within a few years. This study discusses the needs and requirements for ISRU propellant architectures. Emphasis is placed on supporting lunar missions in this decade. This work specifically focuses on a trade study of various ISRU propulsion architecture options which are presented and ranked on several criteria: complexity, ability to provide lunar ascent thrust, solar requirements, ISRU feasibility, and storability. Consideration is given not only to the scientific value of the propellant architecture, but also its potential for market viability. The benefits of storability in upcoming ISRU propellant architectures are explored, and this study concludes by considering R&D needs and expected requirements for a storable propellant MVP.
Robot-assisted minimally invasive surgery (RAMIS) is gaining widespread adoption in many surgical specialties, despite the lack of a standardized training curriculum. Current training approaches rely heavily on virtual reality simulators, in particular for basic psychomotor and visuomotor skill development. It is not clear, however, whether training in virtual reality is equivalent to inanimate model training. In this manuscript, we seek to compare virtual reality training to inanimate model training, with regard to skill learning and skill transfer. Using a custom-developed needle-driving training task with inanimate and virtual analogs, we investigated the extent to which N=18 participants improved their skill on a given platform post-training, and transferred that skill to the opposite platform. Results indicate that the two approaches are not equivalent, with more salient skill transfer after inanimate training than virtual training. These findings support the claim that training with real physical models is the gold standard, and suggest more inanimate model training be incorporated into training curricula for early psychomotor skill development.
The push for energy efficiency of industry processes is driving various efforts to analyze, simulate and optimize the underlying complex and large cyber-physical systems. While some efforts use co-simulation, we instead focus on an integrated hybrid approach that offers the ability to model hybrid components as a whole with all their aspects, based on Hybrid PDEVS. We describe modeling techniques that were developed for this purpose, and demonstrate the feasibility of the approach with a prototypical example. The proposed approach allows describing hybrid models on component level with improved reusability.