A multi-energy system optimisation software for advance process control using hypernetworks and a micro-service architecture

This paper describes a multi-energy system optimisation software, “Sustainable Energy Management System” (SEMS), developed as part of a Siemens, Greater London Authority and Royal Borough of Greenwich partnership in collaboration with the University of Nottingham, Nottingham Trent University and Imperial College London. The software was developed for application at a social housing estate in Greenwich, London, as part of the Borough’s efforts to retrofit the energy systems and building fabric of its housing stock. Its purpose is to balance energy across vectors and networks through day-ahead forecasting and optimisations that can be interpreted as control outputs for energy plant such as a water source heat pump, district heating pumps and values, power switchgear, gas boilers, a thermal store, electric vehicle chargers and a photovoltaic array. The optimisation objectives are to minimise greenhouse gas emissions and operational cost. The tool uses Hypernetwork Theory based orchestration coupled with a microservice architecture. The distributed nature of the design ensures flexibility and scalability. Currently, microservices have been programmed to forecast domestic heating demand, domestic electricity demand, electric vehicle demand, solar photovoltaic generation, ground temperature, and to run a day-ahead energy balance optimisation. This paper presents the results from both domestic heat and electricity demand forecasting, as well as the overall design and integration of the software with a physical system. The works build on that of O’Dwyer, et al. (2020) who developed a preliminary energy management software and digital twin. Their work acts as a foundation for this real-world commercialisation-ready program that integrates with physical assets

Portable single-beam cesium zero-field magnetometer for magnetocardiography

Optically pumped magnetometers (OPMs) are becoming common in the realm of biomagnetic measurements. We discuss the development of a prototype zero-field cesium portable OPM and its miniaturized components. Zero-field sensors operate in a very low static magnetic field environment and exploit physical effects in this regime. OPMs of this type are extremely sensitive to small magnetic fields, but they bring specific challenges to component design, material choice, and current routing. The miniaturized cesium atomic vapor cell within this sensor has been produced through integrated microfabrication techniques. The cell must be heated to 120°C for effective sensing, while the sensor external faces must be skin safe ≤40 ° C making it suitable for use in biomagnetic measurements. We demonstrate a heating system that results in a stable outer package temperature of 36°C after 1.5 h of 120°C cell heating. This relatively cool package temperature enables safe operation on human subjects which is particularly important in the use of multi-sensor arrays. Biplanar printed circuit board coils are presented that produce a reliable homogeneous field along three axes, compensating residual fields and occupying only a small volume within the sensor. The performance of the prototype portable sensor is characterized through a measured sensitivity of 90 fT / Hz in the 5 to 20 Hz frequency band and demonstrated through the measurement of a cardiac magnetic signal

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Waste-to-Resource Transformation:Gradient Boosting Modeling for Organic Fraction Municipal Solid Waste Projection

Food and garden waste are important components of organic fraction municipal solid waste (OFMSW), representing carbon and nutrient rich resources composed of carbohydrates, lipid, protein, cellulose, hemicellulose, and lignin. Despite progressive diversion from landfill, over 50% of landfilled MSW is biodegradable, causing greenhouse gas emissions. In conventional waste management value chains, OFMSW components have been regarded as byproducts as opposed to promising resources with energy and nutrient values. Full exploitation of waste resources calls for a value chain transformation toward proactive resource recovery and waste commoditization. This requires robust projection of OFMSW composition and supply variability. Gradient boosting models are developed here using historical socio-demographic, weather, and waste data from U.K. local authorities. These models are used to forecast garden and food OFMSW generation for each of the 327 U.K. local authorities. The developed methods perform particularly well in forecasting garden waste due to a greater link to measurable environmental variables. The research highlights the key influences in waste volume prediction and demonstrates the difficulty in transferring models to local authorities without training data. The predictive performance and spatial granularity of model projections offer a promising approach to inform decision-making on future waste recovery facilities and OFMSW commoditization

Assessing Coastal Flood Risk in a Changing Climate for Dublin, Ireland

With increasing urban expansion and population growth, coastal urban areas will be increasingly affected by climate change impacts such as extreme storm events, sea level rise and coastal flooding. To address coastal inundation risk for impact studies, integrated approaches accounting for flood hazard modelling, exposure and vulnerability of human and environmental systems are crucial. In this study, we model the impacts of sea level rise on coastal inundation depth for County Dublin, the most extensively urbanized area in Ireland, for the current period and for 2100 under two Representative Concentration Pathways RCP 4.5 and 8.5. A risk-centred approach has been considered by linking the information on coastal flood-prone areas to the exposure of the urban environment, in terms of potential future land cover changes, and to the socioeconomic vulnerability of the population. The results suggest significant challenges for Dublin city and the surrounding coastal areas, with an increase of around 26% and 67% in the number of administrative units considered at very high risk by the end of the century under a RCP 4.5 and 8.5, respectively. This study aims to contribute to existing coastal inundation research undertaken for Ireland by (i) providing a first-level screening of flooding hazards in the study area, (ii) demonstrating how land cover changes and socioeconomic vulnerability can contribute to the level of experienced risk and (iii) informing local authorities and at-risk communities so as to support them in the development of plans for adaptation and resilience

Assessing Coastal Flood Risk in a Changing Climate for Dublin, Ireland

With increasing urban expansion and population growth, coastal urban areas will be increasingly affected by climate change impacts such as extreme storm events, sea level rise and coastal flooding. To address coastal inundation risk for impact studies, integrated approaches accounting for flood hazard modelling, exposure and vulnerability of human and environmental systems are crucial. In this study, we model the impacts of sea level rise on coastal inundation depth for County Dublin, the most extensively urbanized area in Ireland, for the current period and for 2100 under two Representative Concentration Pathways RCP 4.5 and 8.5. A risk-centred approach has been considered by linking the information on coastal flood-prone areas to the exposure of the urban environment, in terms of potential future land cover changes, and to the socioeconomic vulnerability of the population. The results suggest significant challenges for Dublin city and the surrounding coastal areas, with an increase of around 26% and 67% in the number of administrative units considered at very high risk by the end of the century under a RCP 4.5 and 8.5, respectively. This study aims to contribute to existing coastal inundation research undertaken for Ireland by (i) providing a first-level screening of flooding hazards in the study area, (ii) demonstrating how land cover changes and socioeconomic vulnerability can contribute to the level of experienced risk and (iii) informing local authorities and at-risk communities so as to support them in the development of plans for adaptation and resilience

Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (T/Hz), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM’s operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500 fT/Hz to 109fT/Hz. The flexibility and efficiency of the ML approaches can be utilised to benchmark SERF OPM sensor hardware improvements, such as cell geometry, alkali species and sensor topologies

A new species,

The Dactylosomatidae Jakowska and Negrelli, 1955 are one of four families belonging to adeleorinid coccidia and comprise the genera Babesiosoma Jakowska and Nigrelli, 1956 and Dactylosoma Labbé, 1894. These blood protozoa occur in peripheral blood of lower vertebrates, and are commonly reported parasitising amphibians. The present study describes Dactylosoma piperis n. sp. from the pepper frog Leptodactylus labyrinthicus (Spix, 1824) (Anura: Leptodactylidae), collected in 2018 at the municipality of Araguaiana, Mato Grosso State, Brazil, based on morphology of intra-erythrocytic trophozoite, primary and secondary merogonic stages and a molecular analysis (partial 18S rDNA). Dactylosoma piperis n. sp. forms a well-supported clade with other Dactylosomatidae. This is the first molecular characterization of a species of Dactylosoma from a Brazilian anuran