Investigating the potential of a novel low-energy house concept with hybrid adaptable thermal storage

In conventional buildings thermal mass is a permanent building characteristic depending on the building design. However, none of the permanent thermal mass concepts are optimal in all operational conditions. We propose a concept that combines the benefits of buildings with low and high thermal mass by applying hybrid adaptable thermal storage (HATS) systems and materials to a lightweight building. The HATS concept increases building performance and the robustness to changing user behavior, seasonal variations and future climate changes. Building performance simulation is used to investigate the potential of the novel concept for reducing heating energy demand and increasing thermal comfort. Simulation results of a case study in the Netherlands show that the optimal quantity of the thermal mass is sensitive to the change of seasons. This implies that the building performance will benefit from implementing HATS. Furthermore, the potential of HATS is quantified using a simplified HATS model. Calculations show heating energy demand reductions of up to 35% and increased thermal comfort compared to conventional thermal mass concepts

Exploring the Optimal Thermal Mass to Investigate the Potential of a Novel Low-Energy House Concept

In conventional buildings thermal mass is a permanent building characteristic depending on the building design. However, none of the permanent thermal mass concepts are optimal in all operational conditions. We propose a concept that combines the benefits of buildings with low and high thermal mass by applying hybrid adaptable thermal storage (HATS) systems and materials to a lightweight building. The HATS concept increases building performance and the robustness to changing user behavior, seasonal variations and future climate changes. In this paper the potential of the novel HATS concept is investigated by determining the sensitivity of the optimal thermal mass of a building to the change of seasons and to changing occupancy patterns. The optimal thermal mass is defined as the quantity of the thermal mass that provides the best building performance (based on a trade-off between the building performance indicators). Building performance simulation and multi-objective optimization techniques are used to define the optimal thermal mass of a case study in the Netherlands. Simulation results show that the optimal quantity of the thermal mass is sensitive to the change of seasons and occupancy patterns. This implies that the building performance will benefit from implementing HATS. Furthermore, the results show that using HATS reduces the heating energy demand of the case study with 26% and reduces weighted over- and underheating hours with 85%

Onderzoek naar het potentieel van hybride adaptieve thermische energieopslagsystemen voor laag-energiewoningen

In conventionele gebouwen is de thermische massa een permanente gebouweigenschap die afhankelijk is van het gebouwontwerp. Gebouwen met een permanente thermische massa zullen echter niet onder alle omstandigheden optimaal presteren. In dit artikel wordt daarom een gebouwconcept geintroduceerd dat de voordelen benut van zowel een thermisch licht als een thermisch zwaar gebouw. Het concept bestaat uit een lichtgewicht constructie met een hybride adaptieve thermische energieopslagcapaciteit (hybrid adaptable thermal storage, HATS). Dit zogenaamde HATS-concept verbetert de gebouwprestaties en vergroot de robuustheid voor veranderend gebruikersgedrag, seizoenswisselingen en klimaatveranderingen. Met behulp van gebouwsimulatie wordt in dit artikel het potentieel van het HATS-concept onderzocht en gekwantificeerd. Simulaties van een casestudy in Nederland laten zien dat de optimale hoeveelheid thermisch massa afhankelijk is van het gebruikersgedrag en seizoenswisselingen. Dit impliceert dat de gebouwprestaties van de casestudy zullen verbeteren door gebruik te maken van het HATS-concept. Verder is het potentieel gekwantificeerd met behulp van een vereenvoudigd HATS-model. Berekeningen van de casestudy met het HATS-model laten zien dat de warmtevraag met 35% afneemt, terwijl het thermisch comfort sterk verbetert vergeleken met de casestudy die gebruik maakt van een permanente thermische massa

The impact of loco-regional recurrences on metastatic progression in early-stage breast cancer: a multistate model

To study whether the effects of prognostic factors associated with the occurrence of distant metastases (DM) at primary diagnosis change after the incidence of loco-regional recurrences (LRR) among women treated for invasive stage I or II breast cancer. The study population consisted of 3,601 women, enrolled in EORTC trials 10801, 10854, or 10902 treated for early-stage breast cancer. Data were analysed in a multivariate, multistate model by using multivariate Cox regression models, including a state-dependent covariate. The presence of a LRR in itself is a significant prognostic risk factor (HR: 3.64; 95%-CI: 2.02-6.5) for the occurrence of DM. Main prognostic risk factors for a DM are young age at diagnosis (</=40: HR: 1.79; 95%-CI: 1.28-2.51), larger tumour size (HR: 1.58; 95%-CI: 1.35-1.84) and node positivity (HR: 2.00; 95%-CI: 1.74-2.30). Adjuvant chemotherapy is protective for a DM (HR: 0.66; 95%-CI: 0.55-0.80). After the occurrence of a LRR the latter protective effect has disappeared (P = 0.009). The presence of LRR in itself is a significant risk factor for DM. For patients who are at risk of developing LRR, effective local control should be the main target of therapy

Patient's needs and preferences in routine follow-up after treatment for breast cancer

The purpose of the study was to analyse the needs of women who participated in a routine follow-up programme after treatment for primary breast cancer. A cross-sectional survey was conducted using a postal questionnaire among women without any sign of relapse during the routine follow-up period. The questionnaire was sent 2-4 years after primary surgical treatment. Most important to patients was information on long-term effects of treatment and prognosis, discussion of prevention of breast cancer and hereditary factors and changes in the untreated breast. Patients preferred additional investigations (such as X-ray and blood tests) to be part of routine follow-up visits. Less satisfaction with interpersonal aspects and higher scores on the Hospital Anxiety and Depression Scale (HADS) scale were related to stronger preferences for additional investigation. Receiving adjuvant hormonal or radiotherapy was related to a preference for a more intensive follow-up schedule. There were no significant differences between patients treated with mastectomy compared to treated with breast-conserving therapy. During routine follow-up after a diagnosis of breast cancer, not all patients needed all types of information. When introducing alternative follow-up schedules, individual patients' information needs and preferences should be identified early and incorporated into the follow-up routine care, to target resources and maximise the likelihood that positive patient outcomes will result

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results