Analyzing the Contribution of Green Buildings Towards Circular Economy in Sri Lanka

The circular economy concept is crucial in moving forward with sustainable development in any country. It has been identified that implementing CE (Circular Economy) in the built environment has various benefits towards the environment, society as well as the economy. Although this approach has various benefits, its implementation in Sri Lanka is still at a premature stage. Green rating systems are used to qualitatively assess the building’s performance regarding sustainability aspects of the built environment. It remains unclear whether green rating systems in Sri Lanka provide an appropriate guide towards implementing the CE concept. Hence, this paper aims to analyze the contribution provided by green ratings in Sri Lanka towards the implementation of CE. Two pilot case studies and semi-structured interviews were carried out among industry experts who have hands-on experience in green rating systems and work experience in green building projects. Findings indicate that Green building projects have implemented various kinds of sustainable features to obtain a rating. Nevertheless, the practice of these features are not adequately reflecting the implementation CE concept due to numerous barriers to implement CE in Sri Lankan context. The study concludes that in Sri Lanka, the green rating system does not adequately contribute towards the implementation of CE

How accurate is an LCD screen version of the Pelli–Robson test?

Purpose: To evaluate the accuracy and repeatability of a computer-generated Pelli–Robson test displayed on liquid crystal display (LCD) systems compared to a standard Pelli–Robson chart. Methods: Two different randomized crossover experiments were carried out for two different LCD systems for 32 subjects: 6 females and 10 males (40.5 ± 13.0 years) and 9 females and 7 males (27.8 ± 12.2 years), respectively, in the first and second experiment. Two repeated measurements were taken with the printed Pelli–Robson test and with the LCDs at 1 and 3 m. To test LCD reliability, measurements were repeated after 1 week. Results: In Experiment 1, contrast sensitivity (CS) measured with LCD1 resulted significantly higher than Pelli–Robson both at 1 and at 3 m of about 0.20 log 1/C in both eyes (p < 0.01). Bland–Altman plots showed a proportional bias for LCD1 measures. LCD1 measurements showed reasonable repeatability: ICC was 0.83 and 0.65 at 1 and 3 m, respectively. In Experiment 2, CS measured with LCD2 resulted significantly lower than Pelli–Robson both at 1 and at 3 m of about 0.10 log 1/C in both eyes (p < 0.01). Bland–Altman plots did not show any proportional bias for LCD2 measures. LCD2 measurements showed sufficient repeatability: ICC resulted 0.51 and 0.65 at 1 and 3 m, respectively. Conclusions: Computer-generated versions of Pelli–Robson test, displayed on LCD systems, do not provide accurate results compared to classic Pelli–Robson printed version. Clinicians should consider that Pelli–Robson computer-generated versions could be non-interchangeable to the printed version

Current state of the evidence on community treatments for people with complex emotional needs: a scoping review

Background Improving the quality of care in community settings for people with ‘Complex Emotional Needs’ (CEN—our preferred working term for services for people with a “personality disorder” diagnosis or comparable needs) is recognised internationally as a priority. Plans to improve care should be rooted as far as possible in evidence. We aimed to take stock of the current state of such evidence, and identify significant gaps through a scoping review of published investigations of outcomes of community-based psychosocial interventions designed for CEN. Methods We conducted a scoping review with systematic searches. We searched six bibliographic databases, including forward and backward citation searching, and reference searching of relevant systematic reviews. We included studies using quantitative methods to test for effects on any clinical, social, and functioning outcomes from community-based interventions for people with CEN. The final search was conducted in November 2020. Results We included 226 papers in all (210 studies). Little relevant literature was published before 2000. Since then, publications per year and sample sizes have gradually increased, but most studies are relatively small, including many pilot or uncontrolled studies. Most studies focus on symptom and self-harm outcomes of various forms of specialist psychotherapy: most result in outcomes better than from inactive controls and similar to other specialist psychotherapies. We found large evidence gaps. Adaptation and testing of therapies for significant groups (e.g. people with comorbid psychosis, bipolar disorder, post-traumatic stress disorder, or substance misuse; older and younger groups; parents) have for the most part only reached a feasibility testing stage. We found little evidence regarding interventions to improve social aspects of people’s lives, peer support, or ways of designing effective services. Conclusions Compared with other longer term mental health problems that significantly impair functioning, the evidence base on how to provide high quality care for people with CEN is very limited. There is good evidence that people with CEN can be helped when specialist therapies are available and when they are able to engage with them. However, a much more methodologically robust and substantial literature addressing a much wider range of research questions is urgently needed to optimise treatment and support across this group

Mesosphere/lower thermosphere prevailing wind model

The mesosphere/lower thermosphere (MLT) wind data from the 46 ground-based (GB) MF and meteor radar (MR) stations, located at the different latitudes over the globe, and the space-based (SB) HRDI data were used for constructing of the empirical global climatic 2-D prevailing wind model at 80-100 km heights for all months of the year. The main data set is obtained during 1990-2001 period. It is shown that the three datasets (MF, MR, HRDI) are mainly well correlated. However, a certain systematic bias between the GB and SB data at 96 km exists, as well as that between the MF and MR data higher 88 km. Simple correction factors are proposed to minimize these biases. The 2-D distant-weighted least-square interpolation procedure for some arbitrary collection of points was used for drawing model contour plots. The model is available in the computer readable form and may be used for construction of the new CIRA model. © 2004 COSPAR. Published by Elsevier Ltd. All rights reserved

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages