Indoor environment assessment of energy retrofitting the UK school building stock in the context of socio-technical data crowdsourcing and modelling constraints

The UK non-domestic stock is responsible for 18% of UK carbon emissions and school buildings comprise 10% of the non-domestic stock by floorspace. Energy retrofit opportunities could be identified and tracked by policymakers and engineers through aggregated feedforward of stock-level performance from building stock models. However time and capital constraints of gathering nationwide fabric and operational datasets restricts efficacy of performance analysis. Building users could provide such datasets at scale through data crowdsourcing, by compensating users through feedback of relevant individualised building performance. However, successful utilisation of such an approach requires better understanding of the design of feedback and feedforward in terms of how it motivates and informs building users and policy makers/engineers respectively. In addition, constraints of data gathering and requirements of building stock modelling, which crowdsourcing will inform, need to be identified. Feedback and feedforward design was synthesised from nine stakeholder sessions. Crowdsourced data quality was analysed based on 181 records within the existing CarbonBuzz platform, while availability of fabric and operational datasets was derived from 139 responses to a newly created schools questionnaire. For input and output modelling requirements, a UK classroom stock model was generated. Scenario and parameter analysis incorporating 195 archetypes, 24 pairwise retrofit and indoor environment quality (IEQ) improvement scenarios and seven energy and IEQ performance criteria, was carried out. Provision of information on the correlation of energy performance with attainment and learning environment was identified as important for feedback and feedforward design. Building disaggregation was deemed desirable for decision making, despite low completeness of disaggregated datasets within CarbonBuzz, and could be addressed through providing individualised school campus layouts for reference. Updates to existing geometry and fabric datasets, archetypes and building operation rules have also been identified. Stock modelling demonstrated increasing effectiveness of night-time ventilation in preventing overheating as retrofit is applied. While mitigation of impact of classroom orientation on overheating can be achieved through IEQ improvement measures, geometry impacts on attainment are unaffected by retrofit or IEQ improvement. Including individualised glazing ratio and terrain features in future modelling were also identified as critical for feedback of energy and IEQ performance. This research will inform the design of a modelling platform for schools to evaluate stock-level retrofit strategies

Correlations between rail wear rates and operating conditions in a commercial railroad

The rail wear rates per traffic unit (mm/MTon) in the curves of a 4.5 km-long commercial line over a period of 9 years were measured and related to specific operation conditions. The rail corrugation was analyzed using a Corrugation Analysis Trolley (CAT) and visual inspection was carried out in order to identify the defects in the railroad. Since Rolling Contact Fatigue (RCF), artificial abrasion and corrugation were found to be the most important issues the grinding procedures used during maintenance of the railroad were evaluated to assess their effectiveness on removing the defects from the rail surface. The results showed that the wear rates in the studied railroad were several times higher than those typically found in the literature, mainly as a consequence of inappropriate grinding regimes. White layer formation and only partial removal of cracks emerged as the most relevant drawbacks of rail grinding procedures

Axlebox accelerations: Their acquisition and time frequency characterisation for railway track monitoring purposes

[EN] Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.Project Funded by Geoconcept Co. Ltd. and the Spanish Ministry of Economy and Competitiveness. Ref. IDI-20110461.Salvador Zuriaga, P.; Naranjo Ornedo, V.; Insa Franco, R.; Teixeira, P. (2016). Axlebox accelerations: Their acquisition and time frequency characterisation for railway track monitoring purposes. Measurement. 82:301-312. doi:10.1016/j.measurement.2016.01.012S3013128

Benefits and Drawbacks of Pre-licensure Clinical Jobs in Undergraduate Nursing Students

This research study aims to identify the benefits and drawbacks of pre-licensure clinical jobs in undergraduate nursing students. Many nursing students are encouraged to have a job in clinical settings in order to gain more experience. Many undergraduate nursing students here at the University of Akron currently have jobs in the clinical setting and these are the student\u27s who we plan to use as our sample for this study. This study will be completed by spring of 2023, before graduation