Bayesian inference for thermal response test parameter estimation and uncertainty assessment

The effective ground thermal conductivity and borehole thermal resistance constitute information needed to design a ground-source heat pump (GSHP). In situ thermal response tests (TRTs) are considered reliable to obtain these parameters, but interpreting TRT data by a deterministic approach may result in significant uncertainties in the estimates. In light of the impact of the two parameters on GSHP applications, the quantification of uncertainties is necessary. For this purpose, in this study, we develop a stochastic method based on Bayesian inference to estimate the two parameters and associated uncertainties. Numerically generated noisy TRT data and reference sandbox TRT data were used to verify the proposed method. The posterior probability density functions obtained were used to extract the point estimates of the parameters and their credible intervals. Following its verification, the proposed method was applied to in situ TRT data, and the relationship between test time and estimation accuracy was examined. The minimum TRT time of 36 h recommended by ASHRAE produced an uncertainty of ~ ± 21% for effective thermal conductivity. However, the uncertainty of estimation decreased exponentially with increasing TRT time, and was ± 8.3% after a TRT time of 54 h, lower than the generally acceptable range of uncertainty of ± 10%. Based on the obtained results, a minimum TRT time of 50 h is suggested and that of 72 h is expected to produce sufficiently accurate estimates for most cases.This work was supported by the Japan Society for the Promotion of Science (JSPS) (KAKENHI, grant numbers 26709041 and P16074)

Alternative job search strategies in remote rural and peri-urban labour markets: the role of social networks

This paper examines the importance of informal methods (especially social networking) to the job search strategies used by unemployed people. It compares three areas: a small rural town; a larger, more sparsely populated, remote rural area; and a centrally-located, peri-urban labour market. The analysis is based first on survey research undertaken with 490 job seekers across the study areas. Emerging issues were then followed up during a series of twelve focus groups. The survey research showed that job seekers in the rural study areas were significantly more likely to use social networks to look for work. However, those who had experienced repeated or long-term periods out of work, the unskilled and young people were significantly less likely to use such networks. Focus groups confirmed the perceived importance of social networking to the job search process in rural areas, in contrast to the more marginal role such methods appear to play in peri-urban settings. For many rural job seekers, formal job search activities conducted through Jobcentres were seen as largely symbolic, lacking the practical value of social networking. These results suggest that service providers seeking to assist unemployed people in rural areas need to address the problems faced by many disadvantaged job seekers who are currently caught between their lack of social network relations and the absence of local public employment service facilities in more remote communities

Exergy analysis of a hybrid ground-source heat pump system

In contrast to energy analysis, the analysis of exergy allows the evaluation of the quality of different energy flows and enables a comprehensive assessment of inefficiencies within a system and its individual components by accounting for exergy consumption. While exergy analysis methods have been applied to a variety of conventional and renewable energy supply systems, there is still a lack of knowledge regarding the exergy flows and exergy efficiency of hybrid ground-source heat pump systems with a supplementary boiler. In this study, we develop a thermodynamic model for each subsystem in a hybrid heating and cooling system of an existing building by applying the concept of cool and warm exergy. A comparison of the exergy consumption of the hybrid system in heating and cooling reveals that there are significant differences regarding the components that attribute most to the overall exergy consumption in the system. Due to these differences the true exergy performance of the system in heating mode (~30%) is twice as high as for cooling mode (~15%), while the natural exergy performance is considerably better in cooling mode (~26% to ~3%). Potential measures to enhance the exergy performance based on changes in the operational settings of the system and the improvement of the building envelope were found to have a more significant effect on heating performance than on cooling performance. In general, measures that affect the amount of thermal energy delivered by the system appear to be more effective than changes to the operational settings of energy supply systems

Bayesian inference of structural error in inverse models of thermal response tests

For the design of ground-source heat pumps (GSHPs), two design parameters, namely the ground thermal conductivity and borehole thermal resistance are estimated by interpreting thermal response test (TRT) data using a physical model. In most cases, the parameters are fitted to the measured data assuming that the chosen model can fully reproduce the actual physical response. However, two significant sources of error make the estimation uncertain: random error from experiments and structural bias error that describes the discrepancy between the model and actual physical phenomena. Generally, these two error sources are not evaluated separately. As a result, the suitability of selected models to correctly infer parameters from TRTs are not well understood. In this study, the Bayesian calibration framework proposed by Kennedy and O'Hagan is employed to estimate the GSHP design parameters and quantify the random and structural errors in the inference. The calibration framework enables us to examine structural errors in the commonly used infinite line source model arising due to the conditions in which the TRT takes place. Two in situ TRT datasets were used: TRT1, influenced by contextual disturbances from the outdoor environment, and TRT2, influenced by a strong groundwater flow caused by heavy rainfall. We show that the Bayesian calibration framework is able to quantify the structural errors in the TRT interpretation and therefore can yield more accurate estimates of design parameters with full quantification of uncertainties

Expressional regulation of PpDAM5 and PpDAM6, peach (Prunus persica) dormancy-associated MADS-box genes, by low temperature and dormancy-breaking reagent treatment

The present study investigated the expressional regulation of PpDAM5 and PpDAM6, two of the six peach (Prunus persica) dormancy-associated MADS-box genes, in relation to lateral bud endodormancy. PpDAM5 and PpDAM6 were originally identified as homologues of Arabidopsis SHORT VEGETATIVE PHASE/AGAMOUS-LIKE 24 identified in the EVERGROWING locus of peach. Furthermore, PpDAM5 and PpDAM6 have recently been suggested to be involved in terminal bud dormancy. In this study, seasonal expression analyses using leaves, stems, and lateral buds of high-chill and low-chill peaches in field conditions indicated that both genes were up-regulated during the endodormancy period and down-regulated with endodormancy release. Controlled environment experiments showed that the expression of both PpDAM5 and PpDAM6 were up-regulated by ambient cool temperatures in autumn, while they were down-regulated by the prolonged period of cold temperatures in winter. A negative correlation between expression levels of PpDAM5 and PpDAM6 and bud burst percentage was found in the prolonged cold temperature treatment. Application of the dormancy-breaking reagent cyanamide to endo/ecodormant lateral buds induced early bud break and down-regulation of PpDAM5 and PpDAM6 expression at the same time. These results collectively suggest that PpDAM5 and PpDAM6 may function in the chilling requirement of peach lateral buds through growth-inhibiting functions for bud break

Autoimmune and autoinflammatory mechanisms in uveitis

The eye, as currently viewed, is neither immunologically ignorant nor sequestered from the systemic environment. The eye utilises distinct immunoregulatory mechanisms to preserve tissue and cellular function in the face of immune-mediated insult; clinically, inflammation following such an insult is termed uveitis. The intra-ocular inflammation in uveitis may be clinically obvious as a result of infection (e.g. toxoplasma, herpes), but in the main infection, if any, remains covert. We now recognise that healthy tissues including the retina have regulatory mechanisms imparted by control of myeloid cells through receptors (e.g. CD200R) and soluble inhibitory factors (e.g. alpha-MSH), regulation of the blood retinal barrier, and active immune surveillance. Once homoeostasis has been disrupted and inflammation ensues, the mechanisms to regulate inflammation, including T cell apoptosis, generation of Treg cells, and myeloid cell suppression in situ, are less successful. Why inflammation becomes persistent remains unknown, but extrapolating from animal models, possibilities include differential trafficking of T cells from the retina, residency of CD8(+) T cells, and alterations of myeloid cell phenotype and function. Translating lessons learned from animal models to humans has been helped by system biology approaches and informatics, which suggest that diseased animals and people share similar changes in T cell phenotypes and monocyte function to date. Together the data infer a possible cryptic infectious drive in uveitis that unlocks and drives persistent autoimmune responses, or promotes further innate immune responses. Thus there may be many mechanisms in common with those observed in autoinflammatory disorders

The impact of inpatient suicide on psychiatric nurses and their need for support

<p>Abstract</p> <p>Background</p> <p>The nurses working in psychiatric hospitals and wards are prone to encounter completed suicides. The research was conducted to examine post-suicide stress in nurses and the availability of suicide-related mental health care services and education.</p> <p>Methods</p> <p>Experiences with inpatient suicide were investigated using an anonymous, self-reported questionnaire, which was, along with the Impact of Event Scale-Revised, administered to 531 psychiatric nurses.</p> <p>Results</p> <p>The rate of nurses who had encountered patient suicide was 55.0%. The mean Impact of Event Scale-Revised (IES-R) score was 11.4. The proportion of respondents at a high risk (≥ 25 on the 88-point IES-R score) for post-traumatic stress disorder (PTSD) was 13.7%. However, only 15.8% of respondents indicated that they had access to post-suicide mental health care programmes. The survey also revealed a low rate of nurses who reported attending in-hospital seminars on suicide prevention or mental health care for nurses (26.4% and 12.8%, respectively).</p> <p>Conclusions</p> <p>These results indicated that nurses exposed to inpatient suicide suffer significant mental distress. However, the low availability of systematic post-suicide mental health care programmes for such nurses and the lack of suicide-related education initiatives and mental health care for nurses are problematic. The situation is likely related to the fact that there are no formal systems in place for identifying and evaluating the psychological effects of patient suicide in nurses and to the pressures stemming from the public perception of nurses as suppliers rather than recipients of health care.</p

Initial results from Phase 2 of the international urban energy balance model comparison

Urban land surface schemes have been developed to model the distinct features of the urban surface and the associated energy exchange processes. These models have been developed for a range of purposes and make different assumptions related to the inclusion and representation of the relevant processes. Here, the first results of Phase 2 from an international comparison project to evaluate 32 urban land surface schemes are presented. This is the first large-scale systematic evaluation of these models. In four stages, participants were given increasingly detailed information about an urban site for which urban fluxes were directly observed. At each stage, each group returned their models' calculated surface energy balance fluxes. Wide variations are evident in the performance of the models for individual fluxes. No individual model performs best for all fluxes. Providing additional information about the surface generally results in better performance. However, there is clear evidence that poor choice of parameter values can cause a large drop in performance for models that otherwise perform well. As many models do not perform well across all fluxes, there is need for caution in their application, and users should be aware of the implications for applications and decision making