The global COVID-19 pandemic has highlighted the importance of indoor air quality and ventilation to mitigate the spread of respiratory viral infections. Schools, in particular, represent a vulnerable environment with high occupancy rates, prolonged exposure times and often inadequately ventilated rooms. This paper evaluates the functionality of different natural and retrofitted mechanical ventilation strategies in this context. An experimental setup, combining empirical measurements with building performance simulation and analytical risk analysis was used to assess key performance characteristics, including the energetic performance, thermal comfort, indoor air quality and the airborne infection risk of SARS-CoV-2. The results of this study underscore the need for a holistic approach to ventilation design in schools, taking into consideration the balance between energy performance, carbon emissions, thermal comfort, indoor air quality and associated health factors. We demonstrate that the risk of one or more long-range airborne infections, with the SARS-CoV-2 Omicron variant, can be reduced by >50% through appropriate use of natural, mechanical or hybrid ventilation in a classroom setting. Analytical modelling demonstrates that this risk can be further reduced, by an order of magnitude, through the use of FFP2 masks.

Enhanced energy conservation strategies often involve tightly controlled ventilation flow rates. However, stra-tegies that don't carefully consider ventilation rates can, in certain contexts, result in inadequate ventilation, with increased risks of poor indoor environmental quality and user acceptance. These design challenges are often exacerbated in non-domestic buildings with highly dynamic occupancy patterns. This study used computational fluid dynamics, supported by field measurements, to investigate the relationship between zonal supply air strategies and thermal comfort in the George Davies Centre, Leicester University, which is the largest non -domestic certified Passivhaus building in the UK. Ventilation strategies involving mechanical ventilation oper-ating with heat recovery turned on and off, and natural ventilation systems were investigated in relation to their ability to maintain thermal comfort in an auditorium space characterised by high internal heat gains and tiered seating. The results show that, depending on the selected thermal comfort criterion, a thermally comfortable environment could be achieved when incoming air is in the range of 9-26 degrees C for mechanical ventilation with heat recovery and 17-29 degrees C for natural ventilation. These temperatures are referred to as 'limiting operating tem-peratures' in the paper. The work showed that in a temperate climate, thermal comfort could be maintained, for up to 80% of the year, using mixed mode ventilation, without space conditioning, in combination with intelligent design and control strategies. Operating in natural ventilation mode also provided increased fresh air supply capacity, a finding which is particularly relevant in the context of mitigating airborne viral transmission.

Rising global temperatures and more frequent heatwaves due to climate change have led to a growing body of research and increased policy focus on how to protect against the adverse effects of heat. In cold and temperate Europe, dwellings have traditionally been designed for cold protection rather than heat mitigation. There is, therefore, a need to understand the mechanisms through which indoor overheating can occur, its effects on occupants and energy consumption, and how we can design, adapt, and operate buildings during warm weather to improve thermal comfort and reduce cooling energy consumption. This paper brings together experts in overheating from across Europe to explore 10 key questions about the causes and risks from overheating in residential settings in Central and Northern Europe, including the way in which we define and measure overheating, its impacts, and its social and policy implications. The focus is not on summarising literature, but rather on identifying the evidence, key challenges and misconceptions, and limitations of current knowledge. Looking ahead, we outline actions needed to adapt, including the (re)design of dwellings, neighbourhoods, and population responses to indoor heat, and the potential shape of these actions. In doing so, we illustrate how heat adaptation is a multi-faceted challenge that requires urgent and coordinated action at multiple levels, but with feasible solutions and clear benefits for health and energy.

The outbreak of the COVID-19 viral pandemic in 2020 reopened the discussion about indoor air quality in educational buildings. This paper presents the results of a pilot study with the aim of evaluating the user perception of four different ventilation scenarios in the context of COVID-19 prophylaxis, including a mechanical extract system, recently developed by the Max Planck Institute, that can be easily retrofitted into naturally ventilated spaces. The findings of this pilot study highlight the importance of comprehensively assessing new ventilation strategies from a multi-factorial perspective and will inform a follow-up study in a nearby school on a much larger scale. The results helped us in reshaping the approach and refining the survey. The initial findings indicate that local draughts, appliance noise and perceptions of risk can dominate the user's acceptance of such systems.

The rapid escalation of the COVID-19 pandemic has highlighted the importance of efficient ventilation systems in reducing the risk of airborne transmission of the SARS-CoV-2 virus. This study evaluates the energetic performance and viral transmission characteristics of a low-cost mechanical extract ventilation system in comparison to alternative strategies in an educational context. The results show a significant improvement in indoor air quality combined with energy savings relative to natural ventilation strategies.

The Erasmus+ project, entitled ‘Digital Erasmus – a roadmap to using building performance simulation to achieve resilient design’ (DesRes), seeks to transform the learning experience of students in built environment disciplines using a continuous digital learning cycle. Three universities play a part in this project: Graz University of Technology (TU Graz), Delft University of Technology (TU Delft) and the University of Strathclyde (UoS), each developing and delivering a module to complete the learning experience in building simulation. This paper describes the aims and learning objectives associated with the workshops taught at TU Graz as part of the module dedicated to energy monitoring. These workshops tackle the complexity of working with large data sets, which commonly arise from energy monitoring research. In particular, the workshops aim to provide a practical understanding of how to identify, handle, reshape, clean up and evaluate important summary statistics from incomplete data sets. These are fundamental skills in building simulation where model validation and calibration are increasingly commonplace.

Energy services are central components for reducing the energy consumption of buildings. In the context of a long-term transformation towards sustainable energy systems, it is crucial that the building occupiers remain at the centre of such services. This paper presents a new technological conceptual framework for Next Generation Energy Services" which involves a combination of the following technologies: Virtual Reality (VR) physical simulation and Internet of Things (IoT) platforms. The paper demonstrates the concept of this framework on the use case "Human Aspects in Buildings" where variations in material properties can also show an educational value in conveying the principles of building physics using building performance simulation."

Advanced forecasting of impending low temperatures in dwellings could play a transformative role in predicting energy poverty in real-time, thereby helping to prevent excess winter morbidity and mortality. A novel recursive time series model combining AutoRegressive with eXogenous inputs was developed to provide multi-step ahead predictions of the wintertime internal temperatures of homes. A stepwise regression approach was adopted to automate the optimal model selection process based on the minimisation of the Akaike Information Criterion. The model was validated using three case study homes located in Loughborough, UK. Prediction intervals, at the 95% probability level, were used to define a credible interval for the forecasted temperatures at different time horizons during periods of cold weather. The AutoRegressive with eXogenous inputs model proved capable of producing reliable forecasts for 1, 3 and 6 h ahead, achieving Mean Absolute Errors below 1.38 degrees C for these horizons. The results showed that this model consistently outperformed the more complex AutoRegressive Moving Average with eXogenous inputs model. The study provides the first evidence of the potential for using time series forecasting as part of a high-resolution indoor Winter Early Warning Response System which could be used to identify homes at imminent risk of cold-related health impacts.

This work presents a redeveloped introductory course in Digital Systems Design, often considered a course on embedded systems and IoT in an undergraduate computer engineering program at a Canadian university. The course was moved from a theory-driven course to a team project-based course built on a framework of sustainable design, design for accessibility, and equity principles for deaf curling athletes. Students were guided through a modified design cycle, from conceptual design to a functional prototype and physical prototype. In addition, the course included a strong emphasis on allied topics for knowledge transfer, including business development, technology marketing, intellectual property protection, and moving toward commercialization.

This work presents a modelling and simulation study of academic hiring policies to address equity, diversity, and inclusion (EDI) in an engineering program. An agent-based modelling approach is used to investigate the comparative impacts of various EDI hiring interventions on outputs including the time associated with achieving target representation of underrepresented groups, average value (‘qualification score’) of new hires, and number of positions that the best qualified overrepresented group applicant applies for before being hired. The simulation results demonstrate that the time constants for cultural change are long even with proposals that may be considered radical. Also, the simulation results do not support a common argument that EDI initiatives will sacrifice excellence in faculty hiring.

With the help of building diagnostics, the causes and solutions to complex problems in buildings can be determined. In central and greater London, an increasing number of cases of chronic, year-round, overheating in buildings have been reported. We present three cases of unexpected temperatures in multi-storey residential buildings. Detailed analysis and modelling of these scenarios have led to an investigation of whether the way in which infiltration is currently modelled in building performance simulation may be exerting a pronounced effect on the results of overheating studies. An EnergyPlus model, of one of the dwellings in a multi-residential building in London, was created to investigate the influence of infiltration and exfiltration pathway assumptions on the prediction of overheating. The simulation results were compared to empirical data and show that the predicted indoor temperatures are highly sensitive to how the infiltration airflow network is modelled. The findings of this study have been used to provide practical guidance for modellers and building designers on critical aspects to consider when creating building performance simulation models to ensure more reliable outcomes. Practical application Overheating in buildings is an emerging topic of critical importance to the future of the built environment. The importance of understanding infiltration pathways in assessing and modelling overheating risks in flats and multi-residential buildings has been hitherto underestimated or simply ignored. In this paper, examples are given which highlight the need for a fuller understanding of internal air movement where accurate predictions of internal temperatures are required. At present, common building simulation practices and existing technical memorandum (TM) standards are masking the problem and do not provide a basis from which typical or worst-case scenarios can be adequately considered.

A novel application of semi-parametric Generalized Additive Models (GAMs) was developed to forecast elevated indoor temperatures. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that GAMs are capable of slightly improving the forecasting accuracy, but only at short horizons (3-6 hours ahead).

Prolonged overheating has severe consequences for the future habitability of buildings. Building Performance Simulation (BPS) is increasingly used to identify the propensity of buildings to overheat, however the reliability of this approach has been repeatedly questioned. A new overheating risk-assessment methodology, Technical Memorandum (TM)59 was developed by the Chartered Institution of Building Services Engineers (CIBSE), to address this problem by providing a consistent framework for the evaluation of overheating risks in new homes. To date, little empirical research has been carried out to validate this approach in comparison to real buildings. This study aims to bridge the gap between theory and praxis by investigating the potential challenges, limitations, and implications of implementing this standardised methodology. This was achieved by comparing BPS simulations, based on the application of TM59, with empirically measured data from three recently constructed energy-efficient flats located in London. The flats were monitored during the late autumn in order to assess their propensity to chronic year-round overheating, outside of the summer season. Distinct user scenarios, based on different modes of ventilation and window/shading operation, were analysed in relation to the CIBSE TM59 overheating thresholds. The results showed that the TM59 criteria were extremely difficult to satisfy. Under a mechanical ventilation assessment mode (with windows closed) 30–67% of the total occupied hours exceeded the overheating thresholds. This analysis has highlighted the need to further improve overheating methodologies, by considering the assessment of risks in discrete temporal bands as well as incorporating methods to assess mixed-mode purge-ventilation strategies.

In this exploratory study, we studied and qualitatively evaluated a prototype video data collection system to capture and analyze fish behavior in a small-scale indoor aquaculture operation. The research objective was to design and develop a hardware / software system that would have the potential to capture meaningful data from which to extract fish size, swim trajectory, and swim velocity, ultimately as information toward an assessment of fish health. The initial work presented in this paper discusses the development choices of the prototype system, including various combinations of lighting and camera positions both inside and outside of the aquaculture tanks, and several post-processing techniques to isolate fish in video, calibrate the distance from camera to fish through water, and infer fish trajectories and swim velocities. Preliminary results provided a qualitative assessment of such a system. Specific results on the system's ability to detect fishes' positions, trajectories, and velocities are presently limited to observational outcomes and descriptive statistics rather than large-scale quantitative analysis. The present work lays a foundation for a future commercially hardened system that would be required for the collection of larger datasets, which would in turn facilitate the future development of machine learning (ML) algorithms to begin to statistically correlate data to fish conditions and behaviors in near-real time.

Climate change projections indicate that the world's most populated regions will experience more frequent, intense and longer-lasting heatwave periods over the coming decades. Such events are likely to result in widespread overheating in the built environment, with a consequential increase in heat-related morbidity and mortality. In order to warn the population of such risks, Heat-Health Warning Systems (HHWSs) are being progressively adopted world-wide. Current HHWSs are, however, based solely on weather observations and forecasts and are unable to identify precisely where, when, or to what extent individual buildings (and their occupants) will be affected. In contrast, AutoRegressive models with eXogenous inputs (ARX) have been demonstrated to reliably forecast indoor temperatures in individual rooms using minimal data. Thus, the large-scale deployment of forecasting models could theoretically enable the development of a high-resolution indoor HHWS (iHHWS). In this study, ARX models were tested over the long-lasting UK heatwave of 2018 using hourly monitored dry-bulb temperature data from 25 rooms (12 living rooms and 13 bedrooms) in 12 dwellings, located within the London Urban Heat Island (UHI). The study investigates different approaches to improving the reliability of room-based heat exposure predictions at longer forecasting horizons. The effectiveness of the iHHWS system was assessed by evaluating the accuracy of predictions (using fixed and adaptive temperature thresholds) at different lead times (1, 3, 6, 12, 24, 48 and 72 h ahead). Compared to forecasted indoor temperatures, a Cumulative Heat Index (CHI) metric was shown to increase the reliability of heat-health warnings up to 24 h ahead.

Prolonged overheating can have serious cumulative effects on human health, resulting in heat exhaustion, heatstroke and even death. The frequency and severity of heatwaves will increase considerably in the future as a result of accelerating climatic changes compounded by increasing urbanisation. A recent overheating risk-assessment methodology, Technical Memorandum (TM)59: 2017 was developed by the Chartered Institution of Building Services Engineers (CIBSE) to address this problem, by providing a consistent framework for the evaluation of overheating risks in new homes. TM59 has for the first time highlighted the importance of including corridor heat transfer effects in the dynamic modelling of multi-residential dwellings. This paper investigates the strengths and limitations of current approaches to the modelling of corridors, based on a case study of three energy-efficient flats located in London. The results of modelling in accordance with TM59 guidance are compared with alternative approaches, using more realistic occupancy and weather information, and compared to empirically measured data. The findings of this study indicate that current practices in Building Performance Simulation (BPS) are likely to under-estimate the actual air temperatures in corridors. This study highlights the need for further research into the way in which corridors, flats and their interconnecting ventilation and heat transfer networks are commonly discretised in BPS models. (C) 2020 The Authors. Published by Elsevier B.V.

Scarcity of affordable energy efficient dwellings is a defining characteristic of the global housing crisis. In many countries this problem has been exacerbated by single objective cost-models which favour the homogeneous development of market tenures at the expense of delivering high-quality affordable homes. Despite the obvious environmental and fuel-poverty alleviation benefits of advanced energy performance standards, such as Passivhaus, they are often dismissed as an affordable housing solution due to elevated build-cost premiums. The present work attempts to reconcile this housing affordability - energy performance nexus by establishing a novel decision support framework for Passivhaus design using genetic multi-objective optimization. The use of constrained genetic algorithms coupled to the Passive House Planning Package software is shown to produce cost optimal designs which are fully compliant with the Passivhaus standard. The findings also reveal that the precise choice of Passivhaus certification criteria has significant impacts on overheating risks using future probabilistic climate data. This means that the design implications of using either the peak heating load or annual heating demand certification criteria must be temporally evaluated to ensure resilient whole-life design outcomes. In a typical UK context, the findings show that affordable Passivhaus dwelling construction costs can be reduced by up to 366 pound/m(2) (or 22% of build cost). Use of this evidence-based decision support tool could thereby enable local authorities and developers to make better-informed decisions in relation to cost optimal trade-offs between achieving advanced energy performance standards and the viability of large affordable housing developments.

BackgroundTraditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target interactions (DTIs). However, due to the high-dimensional nature of the data sets extracted from drugs and targets, traditional machine learning approaches, such as logistic regression analysis, cannot analyze these data sets efficiently. To overcome this issue, we propose LASSO (Least absolute shrinkage and selection operator)-based regularized linear classification models and a LASSO-DNN (Deep Neural Network) model based on LASSO feature selection to predict DTIs. These methods are demonstrated for repurposing drugs for breast cancer treatment.MethodsWe collected drug descriptors, protein sequence data from Drugbank and protein domain information from NCBI. Validated DTIs were downloaded from Drugbank. A new similarity-based approach was developed to build the negative DTIs. We proposed multiple LASSO models to integrate different combinations of feature sets to explore the prediction power and predict DTIs. Furthermore, building on the features extracted from the LASSO models with the best performance, we also introduced a LASSO-DNN model to predict DTIs. The performance of our newly proposed DNN model (LASSO-DNN) was compared with the LASSO, standard logistic (SLG) regression, support vector machine (SVM), and standard DNN models.ResultsExperimental results showed that the LASSO-DNN over performed the SLG, LASSO, SVM and standard DNN models. In particular, the LASSO models with protein tripeptide composition (TC) features and domain features were superior to those that contained other protein information, which may imply that TC and domain information could be better representations of proteins. Furthermore, we showed that the top ranked DTIs predicted using the LASSO-DNN model can potentially be used for repurposing existing drugs for breast cancer based on risk gene information.ConclusionsIn summary, we demonstrated that the efficient representations of drug and target features are key for building learning models for predicting DTIs. The disease-associated risk genes identified from large-scale genomic studies are the potential drug targets, which can be used for drug repurposing.

In the field of supply chain simulation, transport relations are often modeled as transport times using distributions. Considering long-distance transport relations, this is usually a suitable approach. But, for short-distance transports within large cities, delays depend on specific roads and the time of day. Some simulation tools offer geographical data for modeling actual roads. However, in order to model time-dependent transport times, additional data are needed. In this paper, we present an approach to tackle this problem. Road networks are derived from OpenStreetMap data (including traffic signals). In order to obtain the average speed of vehicles on an hourly basis, we conduct pre-simulation runs modeling the entire inner-city traffic. The respective vehicle rides are derived from trajectory data of cell phone users, where the assignment of users to cell phone tower sections is given for each hour of the day. First results for the city of Winnipeg are presented.