Evaluation of BIM-based LCA results for building design

Digital tools based on Building Information Modelling (BIM) provide the potential to facilitate environmental performance assessments of buildings. Various tools that use a BIM model for automatic quantity take-off as basis for Life Cycle Assessment (LCA) have been developed recently. This paper describes the first application of such a BIM-LCA tool to evaluate the embodied global warming potential (GWP) throughout the whole design process of a real building. 34 states of the BIM model are analysed weekly. The results show that the embodied GWP during the design phase is twice as high as for the final building. These changes can be mainly attributed to the designers\u27 approach of using placeholder materials that are refined later, besides other reasons. As such, the embodied GWP is highly overestimated and a BIM-based environmental assessment during the design process could be misleading and counterproductive. Finally, three alternatives to the established automatic quantity take-off are discussed for future developments

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS < 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population

VizieR Online Data Catalog: Planck Sunyaev-Zeldovich sources (PSZ2) (Planck+, 2016)

Three pipelines are used to detect SZ clusters: two independent implementations of the Matched Multi-Filter (MMF1 and MMF3), and PowellSnakes (PwS). The main catalogue is constructed as the union of the catalogues from the three detection methods. The completeness and reliability of the catalogues have been assessed through internal and external validation as described in section 4 of the paper. (5 data files)

BIM-based LCA throughout the Design Process with a Dynamic Approach

In modern times, as building designs become ever more complicated, it has become increasingly important to integrate environmental considerations into the choice of building materials by the designers. A promising possibility is an incorporation of building systems Life Cycle Assessment (LCA) data into Building Information Modelling (BIM) platforms. The implementation can support the spreading of environmental assessment and conscious choice of construction materials during the building design phase. This study aims to develop a real-time environmental assessment method applicable to all design phases by integrating a BIM-based approach with known LCA methodologies. The research focuses on two primary methods. The first one is a static approach where LCA is done with a conventional method at the end of each phase. The second one is a dynamic approach where the LCA is done in real time during the whole planning process. The performed approach illustrates several possible LCA workflows in BIM environment with the help of the Revit platform and Dynamo parametrization tool. The idea is to link BIM objects and their material properties with LCA databases to be able to extract the embodied energy directly from the data models. Extracting all the data from BIM-model and LCA-databases reduces the effort of inputting the repetitive information into LCA tools. Simplifying the LCA methodology will allow the building professional to use LCA assessment efficiently in their daily work. In conclusion, this work presents a way of integrating LCA with the help of BIM to the decision-making process of the planning based on automated and looped processes in order to boost sustainable development in the construction industry

A Hierarchical Multivariate Spatio-Temporal Model for Clustered Climate data with Annual Cycles

We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate depen- dencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation, and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955–2000

A Hierarchical Multivariate Spatio-Temporal Model for Clustered Climate data with Annual Cycles

We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate dependencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955-2000