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Cohort multiple randomized controlled trial in pediatric asthma to assess the long- and short-term effects of eHealth interventions:protocol of the CIRCUS study
Get PDFBackground: Asthma is one of childhood’s most prevalent chronic conditions significantly impacting the quality of life. Current asthma management lacks real-time, objective, and longitudinal monitoring reflected by a high prevalence of uncontrolled asthma. Long-term home monitoring promises to establish new clinical endpoints for timely anticipation. In addition, integrating eHealth interventions holds promise for timely and appropriate medical anticipation for controlling symptoms and preventing asthma exacerbations. Objectives: This study aims to provide a pragmatic study design for gaining insight into longitudinal monitoring, assessing, and comparing eHealth interventions’ short- and long-term effects on improving pediatric asthma care. Design: The CIRCUS study design is a cohort multiple randomized controlled trial (cmRCT) with a dynamic cohort of 300 pediatric asthma patients. Methods: The study gathers observational and patient-reported measurements at set moments including patient characteristics, healthcare utilization, and asthma, clinical, and environmental outcomes. Participants are randomly appointed to the intervention or control group. The effects of the eHealth interventions are assessed and compared to the control group, deploying the CIRCUS outcomes. The participants continue in the CIRCUS cohort after completing the intervention and its follow-up. Results: This study was ethically approved by the Medical Research Ethics Committee (NL85668.100.23) on February 15th, 2024. Discussion: The CIRCUS study can provide a rich and unique dataset that can improve insight into risk factors of asthma exacerbations and yield new clinical endpoints. Furthermore, the effects of eHealth interventions can be assessed and compared with each other both short- and long-term. In addition, patient groups within the patient population can be discerned to tailor eHealth interventions to personalized needs on improving asthma management. Conclusion: In conclusion, CIRCUS can provide valuable clinical data to discern risk factors for asthma exacerbations, identify and compare effective scalable eHealth solutions, and improve pediatric asthma care. Trial registration: The protocol is registered at ClinicalTrials.gov (NCT06278662).</p
Versatile Kinematics-Based Constraint Identification Applied to Robot Task Reproduction
Get PDFIdentifying kinematic constraints between a robot and its environment can improve autonomous task execution, for example in Learning from Demonstration. Constraint identification methods in the literature often require specific prior constraint models, geometry or noise estimates, or force measurements. Because such specific prior information or measurements are not always available, we propose a versatile kinematics-only method. We identify constraints using constraint reference frames, which are attached to a robot or ground body and may have zero-velocity constraints along their axes. Given measured kinematics, constraint frames are identified by minimizing a norm on the cartesian components of the velocities expressed in that frame. Thereby, a minimal representation of the velocities is found, which represent the zero-velocity constraints we aim to find. In simulation experiments, we identified the geometry (position and orientation) of twelve different constraints including articulated contacts, polyhedral contacts, and contour following contacts. Accuracy was found to decrease linearly with sensor noise. In robot experiments, we identified constraint frames in various tasks and used them for task reproduction. Reproduction performance was similar when using our constraint identification method compared to methods from the literature. Our method can be applied to a large variety of robots in environments without prior constraint information, such as in everyday robot settings
Microbubble-enhanced cold plasma activation for efficient treatment of oil sands process-affected water:Exploring agricultural reuse potential
Get PDFThe effective treatment of oil sands process-affected water (OSPW) remains a critical environmental challenge due to the presence of persistent and toxic naphthenic acids (NAs). This study shows the potential of microbubble-enhanced cold plasma activation (MB-CPA) for the effective degradation of NAs in OSPW. In the MB-CPA system, two corona plasma discharge needle electrodes were placed in parallel above a water flow through a Venturi tube where microbubbles form spontaneously to rapidly transfer reactive oxygen and nitrogen species (RONS) generated from the discharge into water. Degradation efficiencies of isonipecotic acid (IA) and 5-phenylvaleric acid (PVA) in solution and in real OSPW were monitored by using surface-enhanced Raman spectroscopy (SERS) for sensitive quantification. Our results show that MB-CPA achieved near-complete degradation of model NAs within 90 min. When applied to real OSPW, plasma treatment reduced toxicity, enhanced nitrogen species availability. Our tests showed that the treated OSPW demonstrated a 30% higher germination rate and increased seedling growth compared to untreated water, attributed to the reduction of harmful NAs and the introduction of plant-usable nitrogen species. These findings demonstrate the potential of MB-CPA as a promising technology for sustainable wastewater treatment.</p
Editorial:Computer vision and AI in real-world applications: robustness, generalization, and engineering
Get PDFModelling as expert-guidance during teacher practitioner research
Get PDFStudies on teacher practitioner research suggest that experts can provide valuable guidance by modelling the research process within the school context. As it is unknown when and how modelling can promote teachers’ research, the present study evaluated several modelling practices in four teacher teams (N = 38). Two facilitators modelled research skills before, during and after teachers’ research tasks; a research disposition was modelled by questioning research decisions. Results showed that modelling was valuable for practitioner research: teachers appreciated the guidance and felt their research skills and research disposition had improved. Modelling of research skills during the task was deemed easier to comprehend than modelling before and after the tasks. Modelling of a research disposition can bring about tensions that–mainly in the initial stages of the research–cause frustrations in teachers. Based on these findings, directions for improving modelling are discussed.</p
Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis
Get PDFPurpose: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.Methods: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic.Results: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES).Conclusion: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets.Implications for Cancer Survivors: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial
Feedback digitalization preferences in online and hybrid classroom:Experiences from lockdown and implications for post-pandemic education
Get PDFPurpose: This research aims to explore digital feedback needs/preferences in online education during lockdown and the implications for post-pandemic education.Design/methodology/approach: An empirical study approach was used to explore feedback needs and experiences from educational institutions in the Netherlands and Germany (N = 247) using a survey method.Findings: The results showed that instruments supporting features for effortless interactivity are among the highly preferred options for giving/receiving feedback in online/hybrid classrooms, which are in addition also opted for post-pandemic education. The analysis also showed that, when communicating feedback digitally, more inclusive formats are preferred, e.g. informing learners about how they perform compared to peers. The increased need for comparative performance-oriented feedback, however, may affect students' goal orientations. In general, the results of this study suggest that while interactivity features of online instruments are key to ensuring social presence when using digital forms of feedback, balancing online with offline approaches should be recommended.Originality/value: This research contributes to the gap in the scientific literature on feedback digitalization. Most of the existing research are in the domain of automated feedback generated by various learning environments, while literature on digital feedback in online classrooms, e.g. empirical studies on preferences for typology, formats and communication channels for digital feedback, to the best of the authors’ knowledge is largely lacking. The findings and recommendations of this study extend their relevance to post-pandemic education for which hybrid classroom is opted among the highly preferred formats by survey respondents.</p
Asymmetric equilibrium states for melting and freezing in thermal convection
Get PDFA block of ice in a box heated from below and cooled from above can (partially) melt. Vice versa, a box of water with less heating from below or more cooling from above can (partially) re-solidify. This study investigates the asymmetric behaviours between such melting and freezing processes in this Rayleigh–Bénard geometry, focusing on differences in equilibrium flow structures, solid–liquid interface morphology, and equilibrium mean interface height. Our findings reveal a robust asymmetry across a range of Rayleigh numbers and top cooling temperature (i.e. hysteretic behaviour), where the evolution of freezing shows a unique ‘splitting event’ of convection cells that leads to a non-monotonic height evolution trend. To characterise the differences between melting and freezing, we introduce an effective Rayleigh number and the aspect ratio for the cellular structures, and apply the heat flux balance and the Grossmann–Lohse theory. Based on this, we develop a unifying model for the melting and freezing behaviour across various conditions, accurately predicting equilibrium states for both phase-change processes. This work provides insights into the role of convective dynamics in phase-change symmetry-breaking, offering a framework applicable to diverse systems involving melting and freezing.</p
A feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors
Get PDFSensorized insoles provide a tool for gait studies and health monitoring during daily life. For users to accept such insoles, they need to be comfortable and lightweight. Previous research has demonstrated that sensorized insoles can estimate ground reaction forces (GRFs). However, these insoles often assemble commercial components restricting design freedom and customization. Within this work, we incorporated four 3D-printed soft foam-like sensors to sensorize an insole. To test the insoles, we had nine participants walk on an instrumented treadmill. The four sensors behaved in line with the expected change in pressure distribution during the gait cycle. A subset of this data was used to identify personalized Hammerstein-Wiener (HW) models to estimate the 3D GRFs while the others were used for validation. In addition, the identified HW models showed the best estimation performance (on average root mean squared (RMS) error 9.3%, =0.85 and mean absolute error (MAE) 7%) of the vertical, mediolateral, and anteroposterior GRFs, thereby showing that these sensors can estimate the resulting 3D force reasonably well. These results were comparable to or outperformed other works that used commercial force-sensing resistors with machine learning. Four participants participated in three trials over a week, which showed a decrease in estimation performance over time but stayed on average 11.35% RMS and 8.6% MAE after a week with the performance seeming consistent between days two and seven. These results show promise for using 3D-printed soft piezoresistive foam-like sensors with system identification regarding the viability for applications that require softness, lightweight, and customization such as wearable (force) sensors.</p
Design of a bio-inspired foot based on the functional anatomy and biomechanics of the natural human foot
No full textBACKGROUND: Prosthetic foot users often face secondary physical complications such as lower back pain [1] and osteoarthritis in the hips and knees. Individuals with an amputation are particularly susceptible to knee osteoarthritis in their intact limb due to asymmetrical gait and resulting abnormal joint kinematics and kinetics [2]. Limited range of motion (ROM), comfort, and energy handling in the current prosthetic feet contribute to these issues, prompting a need for prosthetic foot designs that better mimic natural foot biomechanics.AIM: The aim of this study was to develop a bio-inspired foot which is based on the functional anatomy and biomechanical principles of the natural human foot, with a specific focus on enhancing ROM (flexibility and adaptability) and energy handling (storage and return).METHOD: A comprehensive literature review was conducted to understand the functional anatomy and biomechanics of the natural human foot. This knowledge was used to design a first prototype of the bio-inspired foot (Figure 1A), which was fabricated from PLA. Gait was simulated manually in a gait lab and motion tracking and force plate data were acquired to assess performance and functionality using different spring stiffnesses, thereby measuring e.g. ROM and energy handling.RESULTS: The bio-inspired foot demonstrated plantarflexion and dorsiflexion angles of 14 and 21 deg, respectively, during stance. Maximum ROM tests, with low spring stiffness configurations, showed 21 deg of plantarflexion and 23 deg of dorsiflexion in the talocrural joint. The talocalcaneal joint reached 9 deg of eversion and 13 deg of inversion, whereas the first MTP joint reached a maximum ROM of 18 deg. In addition, the bio-inspired foot demonstrated varying energy storage across spring stiffness configurations (Figure 1B & 1C).DISCUSSION AND CONCLUSION: This study resulted in the design of a bio-inspired foot with arches, plantar fascia, Achilles tendon and primary motion axes of the natural human foot by replicating the talocrural axis, talocalcaneal axis and MTP axes. The bio-inspired foot demonstrated notable ROM and storage of energy with adjustable spring stiffnesses for adaptability. Further research is needed to validate its performance under real-world conditions