Towards breath sensors that are self-powered by design.

Piezoelectric materials are widely used to generate electric charge from mechanical deformation or vice versa. These strategies are increasingly common in implantable medical devices, where sensing must be done on small scales. In the case of a flow rate sensor, a sensor's energy harvesting rate could be mapped to that flow rate, making it 'self-powered by design (SPD)'. Prior fluids-based SPD work has focused on turbulence-driven resonance and has been largely empirical. Here, we explore the possibility of sub-resonant SPD flow sensing in a human airway. We present a physical model of piezoelectric sensing/harvesting in the airway, which we validated with a benchtop experiment. Our work offers a model-based roadmap for implantable SPD sensing solutions. We also use the model to theorize a new form of SPD sensing that can detect broadband flow information.

Multicriteria analysis of the environmental and economic performance of circularity strategies for concrete waste recycling in Spain.

Construction and demolition waste (CDW) is identified by multiple circular economy (CE) policies as a key sector for implementing circularity strategies due to the high volume of waste produced and the large consumption of raw materials. However, CE is not widely applied in the sector because of the lack of solid estimations on its environmental and economic viability. The main aim of this study was to propose a set of methodological steps to identify the optimal circularity alternatives for CDW products based on a multicriteria analysis of their environmental and economic performance. This methodology is applied to evaluate concrete waste. In specific, high-grade applications of concrete waste were analyzed comprising the processing into recycled coarse aggregates (RCA) for their use in structural and non-structural concrete. Multiple scenarios with different RCA replacements (20%, 30% and 100%) and different types of sorting and recycling (on-site and off-site) were evaluated in accordance with the specific site conditions of the region of Catalonia, Spain. The Life Cycle Analysis methodology (LCA) was used to perform the environmental analysis, while a detailed cost analysis was conducted for the economic aspect. The multicriteria method VIKOR was used for the selection of alternatives considering three different criteria. The results of this study showed environmental and economic advantages of CE scenarios based on the use of RCA over conventional concrete, mainly due to the influence of landfilling and transport distances. RCA produced on-site showed a better performance than RCA from fixed plants.

Cardiac magnetic resonance in recovering COVID-19 patients. Feature tracking and mapping analysis to detect persistent myocardial involvement.

BACKGROUND: Post-COVID-19 patients may incur myocardial involvement secondary to systemic inflammation. Our aim was to detect possible oedema/diffuse fibrosis using cardiac magnetic resonance imaging (CMR) mapping and to study myocardial deformation of the left ventricle (LV) using feature tracking (FT). METHODS: Prospective analysis of consecutively recruited post-COVID-19 patients undergoing CMR. T1 and T2 mapping sequences were acquired and FT analysis was performed using 2D steady-state free precession cine sequences. Statistical significance was set to p < 0.05. RESULTS: Included were 57 post-COVID-19 patients and 20 healthy controls, mean age 59 ± 15 years, men 80.7%. The most frequent risk factors were hypertension (33.3%) and dyslipidaemia (36.8%). The contact-to-CMR interval was 81 ± 27 days. LV ejection fraction (LVEF) was 61 ± 10%. Late gadolinium enhancement (LGE) was evident in 26.3% of patients (19.3%, non-ischaemic). T2 mapping values (suggestive of oedema) were higher in the study patients than in the controls (50.9 ± 4.3 ms vs 48 ± 1.9 ms, p < 0.01). No between-group differences were observed for native T1 nor for circumferential strain (CS) or radial strain (RS) values (18.6 ± 3.3% vs 19.2 ± 2.1% (p = 0.52) and 32.3 ± 8.1% vs 33.6 ± 7.1% (p = 0.9), respectively). A sub-group analysis for the contact-to-CMR interval (<8 weeks vs ≥ 8 weeks) showed that FT-CS (15.6 ± 2.2% vs 18.9 ± 2.6%, p < 0.01) and FT-RS (24.9 ± 5.8 vs 33.5 ± 7.2%, p < 0.01) values were lower for the shorter interval. CONCLUSIONS: Post-COVID-19 patients compared to heathy controls had raised T2 values (related to oedema), but similar native T1, FT-CS and FT-RS values. FT-CS and FT-RS values were lower in post-COVID-19 patients undergoing CMR after < 8 weeks compared to ≥ 8 weeks.

EHDC: An Energy Harvesting Modeling and Profiling Platform for Body Sensor Networks.

Energy harvesting is a promising solution to the limited battery lifetimes of body sensor nodes. Self-powered sensor systems capable of quasi-perpetual operation enable the possibility of truly continuous monitoring of patients beyond the clinic. However, the discontinuous and dynamic characteristics of harvesting in real-world scenarios-and their implications for the design and operation of self-powered systems-are not yet well understood. This paper presents a mobile energy harvesting and data collection (EHDC) platform designed to provide a deeper understanding of energy harvesting dynamics. The EHDC platform monitors and records the instantaneous usable power generated by body-worn harvesters, while also collecting human activity and environmental data to provide a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: solar and thermoelectric. The platform was initially validated with benchtop tests and later with real-world deployments on two subjects. 7-h-long multimodal energy harvesting profiles were generated, and the environmental and behavioral data were used to expand upon previously developed Kalman filter based mathematical models for energy harvesting prediction. Results confirm the validity of the EHDC platform and harvesting models, establishing the potential for longer term monitoring of energy harvesting characteristics; thus, informing the design and operation of self-powered body sensor networks.