Predicting cooling energy demands of adaptive facades using artificial neural network

Adaptive Façades (AFs) have proven to be effective as a building envelope that can enhance energy effi- ciency and thermal comfort. However, evaluating the performance of these AFs using the current building performance simulation (BPS) tools is complex, time-consuming, and computationally intensive. These limitations can be overcome by using a machine learning (ML) model as a method to assess the AF system efficiently during the early design stage. This study presents an alternative approach using an Artificial Neural Network (ANN) model that can predict the hourly cooling loads of AF in significantly less time compared to BPS. To construct the model, a generative parametric simulation of office tower spaces with an AF shading system were simulated in terms of energy consumption using Honeybee add-on in Grass- hopper which are linked to EnergyPlus for training the ANN model. The prediction results showed a highly accurate model that can estimate cooling loads within seconds

Predicting incident solar radiation on building’s envelope using machine learning

The assessment of the impact of solar radiation on building envelopes has typically been achieved by using simulation software, which is time consuming and requires advanced computational knowledge. Given the increased complexity of large scale-projects and the demand for performative buildings, new innovative methods are required to assess the design efficiently. In this paper, we present an alternative and innovative approach to assessing solar radiation intensity on an office building envelope using two machine-learning (ML) models: Artificial Neural Network (ANN) and Decision Tree (DT). The experimental workflow of this paper consists of two stages. In the first stage, a generative parametric office tower and its urban context were designed and simulated using Grasshopper software to create a large synthetic dataset of the solar radiation that strikes the office room envelope with several types of analyses. In the second stage, the generated datasets were imported into two ML algorithms (ANN and DT) to create a model for training and testing. The comparison of these two ML models proved that input data types have a significant impact on the accuracy of the prediction and model selection. DT was found to be more accurate than ANN because the data is mostly categorical, which is the most suitable learning background for DT algorithms

Effects of home confinement on mental health and lifestyle behaviours during the COVID-19 outbreak:insights from the ECLB-COVID19 multicentre study

Although recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation have been suggested to generate a burden throughout the population. To provide scientific data to help identify risk factors for the psychosocial strain during the COVID-19 outbreak, an international cross-disciplinary online survey was circulated in April 2020. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement. The ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform and was promoted by thirty-five research organizations from Europe, North Africa, Western Asia and the Americas. Questions were presented in a differential format with questions related to responses “before” and “during” the confinement period. 1047 replies (54% women) from Western Asia (36%), North Africa (40%), Europe (21%) and other continents (3%) were analysed. The COVID-19 home confinement evoked a negative effect on mental wellbeing and emotional status (P < 0.001; 0.43 ≤ d ≤ 0.65) with a greater proportion of individuals experiencing psychosocial and emotional disorders (+10% to +16.5%). These psychosocial tolls were associated with unhealthy lifestyle behaviours with a greater proportion of individuals experiencing (i) physical (+15.2%) and social (+71.2%) inactivity, (ii) poor sleep quality (+12.8%), (iii) unhealthy diet behaviours (+10%), and (iv) unemployment (6%). Conversely, participants demonstrated a greater use (+15%) of technology during the confinement period. These findings elucidate the risk of psychosocial strain during the COVID-19 home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle AHCL)

Globally altered sleep patterns and physical activity levels by confinement in 5056 individuals:ECLB COVID-19 international online survey

Symptoms of psychological distress and disorder have been widely reported in people under quarantine during the COVID-19 pandemic; in addition to severe disruption of peoples' daily activity and sleep patterns. This study investigates the association between physical-activity levels and sleep patterns in quarantined individuals. An international Google online survey was launched in April 6th, 2020 for 12-weeks. Forty-one research organizations from Europe, North-Africa, Western-Asia, and the Americas promoted the survey through their networks to the general society, which was made available in 14 languages. The survey was presented in a differential format with questions related to responses "before" and "during" the confinement period. Participants responded to the Pittsburgh Sleep Quality Index (PSQI) questionnaire and the short form of the International Physical Activity Questionnaire. 5056 replies (59.4% female), from Europe (46.4%), Western-Asia (25.4%), America (14.8%) and North-Africa (13.3%) were analysed. The COVID-19 home confinement led to impaired sleep quality, as evidenced by the increase in the global PSQI score (4.37 +/- 2.71 before home confinement vs. 5.32 +/- 3.23 during home confinement) (p &lt; 0.001). The frequency of individuals experiencing a good sleep decreased from 61% (n = 3063) before home confinement to 48% (n = 2405) during home confinement with highly active individuals experienced better sleep quality (p &lt; 0.001) in both conditions. Time spent engaged in all physical-activity and the metabolic equivalent of task in each physical-activity category (i.e., vigorous, moderate, walking) decreased significantly during COVID-19 home confinement (p &lt; 0.001). The number of hours of daily-sitting increased by similar to 2 hours/days during home confinement (p &lt; 0.001). COVID-19 home confinement resulted in significantly negative alterations in sleep patterns and physical-activity levels. To maintain health during home confinement, physical-activity promotion and sleep hygiene education and support are strongly warranted.</p

Sleep Quality and Physical Activity as Predictors of Mental Wellbeing Variance in Older Adults during COVID-19 Lockdown:ECLB COVID-19 International Online Survey

Background. The COVID-19 lockdown could engender disruption to lifestyle behaviors, thus impairing mental wellbeing in the general population. This study investigated whether sociodemographic variables, changes in physical activity, and sleep quality from pre- to during lockdown were predictors of change in mental wellbeing in quarantined older adults. Methods. A 12-week international online survey was launched in 14 languages on 6 April 2020. Forty-one research institutions from Europe, Western-Asia, North-Africa, and the Americas, promoted the survey. The survey was presented in a differential format with questions related to responses "pre" and "during" the lockdown period. Participants responded to the Short Warwick-Edinburgh Mental Wellbeing Scale, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and the short form of the International Physical Activity Questionnaire. Results. Replies from older adults (aged &gt;55 years, n = 517), mainly from Europe (50.1%), Western-Asia (6.8%), America (30%), and North-Africa (9.3%) were analyzed. The COVID-19 lockdown led to significantly decreased mental wellbeing, sleep quality, and total physical activity energy expenditure levels (all p &lt; 0.001). Regression analysis showed that the change in total PSQI score and total physical activity energy expenditure (F-(2,F- 514) = 66.41 p &lt; 0.001) were significant predictors of the decrease in mental wellbeing from pre- to during lockdown (p &lt; 0.001, R-2: 0.20). Conclusion. COVID-19 lockdown deleteriously affected physical activity and sleep patterns. Furthermore, change in the total PSQI score and total physical activity energy expenditure were significant predictors for the decrease in mental wellbeing.</p

Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise.

BACKGROUND: Medical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of UK medical schools, including postgraduate performance, fitness to practise issues, specialty choice, preparedness, satisfaction, teaching styles, entry criteria and institutional factors. METHOD: Aggregated data were collected for 50 measures across 29 UK medical schools. Data include institutional history (e.g. rate of production of hospital and GP specialists in the past), curricular influences (e.g. PBL schools, spend per student, staff-student ratio), selection measures (e.g. entry grades), teaching and assessment (e.g. traditional vs PBL, specialty teaching, self-regulated learning), student satisfaction, Foundation selection scores, Foundation satisfaction, postgraduate examination performance and fitness to practise (postgraduate progression, GMC sanctions). Six specialties (General Practice, Psychiatry, Anaesthetics, Obstetrics and Gynaecology, Internal Medicine, Surgery) were examined in more detail. RESULTS: Medical school differences are stable across time (median alpha = 0.835). The 50 measures were highly correlated, 395 (32.2%) of 1225 correlations being significant with p < 0.05, and 201 (16.4%) reached a Tukey-adjusted criterion of p < 0.0025. Problem-based learning (PBL) schools differ on many measures, including lower performance on postgraduate assessments. While these are in part explained by lower entry grades, a surprising finding is that schools such as PBL schools which reported greater student satisfaction with feedback also showed lower performance at postgraduate examinations. More medical school teaching of psychiatry, surgery and anaesthetics did not result in more specialist trainees. Schools that taught more general practice did have more graduates entering GP training, but those graduates performed less well in MRCGP examinations, the negative correlation resulting from numbers of GP trainees and exam outcomes being affected both by non-traditional teaching and by greater historical production of GPs. Postgraduate exam outcomes were also higher in schools with more self-regulated learning, but lower in larger medical schools. A path model for 29 measures found a complex causal nexus, most measures causing or being caused by other measures. Postgraduate exam performance was influenced by earlier attainment, at entry to Foundation and entry to medical school (the so-called academic backbone), and by self-regulated learning. Foundation measures of satisfaction, including preparedness, had no subsequent influence on outcomes. Fitness to practise issues were more frequent in schools producing more male graduates and more GPs. CONCLUSIONS: Medical schools differ in large numbers of ways that are causally interconnected. Differences between schools in postgraduate examination performance, training problems and GMC sanctions have important implications for the quality of patient care and patient safety

The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning.

BACKGROUND: What subjects UK medical schools teach, what ways they teach subjects, and how much they teach those subjects is unclear. Whether teaching differences matter is a separate, important question. This study provides a detailed picture of timetabled undergraduate teaching activity at 25 UK medical schools, particularly in relation to problem-based learning (PBL). METHOD: The Analysis of Teaching of Medical Schools (AToMS) survey used detailed timetables provided by 25 schools with standard 5-year courses. Timetabled teaching events were coded in terms of course year, duration, teaching format, and teaching content. Ten schools used PBL. Teaching times from timetables were validated against two other studies that had assessed GP teaching and lecture, seminar, and tutorial times. RESULTS: A total of 47,258 timetabled teaching events in the academic year 2014/2015 were analysed, including SSCs (student-selected components) and elective studies. A typical UK medical student receives 3960 timetabled hours of teaching during their 5-year course. There was a clear difference between the initial 2 years which mostly contained basic medical science content and the later 3 years which mostly consisted of clinical teaching, although some clinical teaching occurs in the first 2 years. Medical schools differed in duration, format, and content of teaching. Two main factors underlay most of the variation between schools, Traditional vs PBL teaching and Structured vs Unstructured teaching. A curriculum map comparing medical schools was constructed using those factors. PBL schools differed on a number of measures, having more PBL teaching time, fewer lectures, more GP teaching, less surgery, less formal teaching of basic science, and more sessions with unspecified content. DISCUSSION: UK medical schools differ in both format and content of teaching. PBL and non-PBL schools clearly differ, albeit with substantial variation within groups, and overlap in the middle. The important question of whether differences in teaching matter in terms of outcomes is analysed in a companion study (MedDifs) which examines how teaching differences relate to university infrastructure, entry requirements, student perceptions, and outcomes in Foundation Programme and postgraduate training

Etude de l’adsorption des sucres dans les zéolithes : compréhension des mécanismes de séparation

Second generation biomass is mainly composed of saccharides building units that are a valuable renewable feedstock to produce high added-value chemicals. In order to obtain separate streams of each sugar (xylose, glucose, ...), the incorporation of a separation step is necessary in a biorefinery after lignocellulose fractionation. This work aims at studying and rationalizing the separation of lignocellulosic monosaccharides molecules (glucose and xylose) by adsorption. Thus, the well-known supercage of Faujasite-type zeolites is selected as a template to promote sugar separation. On the basis of this platform, an adsorbent library is constructed by means of cationic exchange of two structures presenting differentiated electrostatic strengths: NaX (Si/Al = 1.2) and NaY (Si/Al = 2.6). The library of adsorbents is characterized and “screened” for xylose/glucose separation. Thanks to the combination of experimental and theoretical approaches, several parameters are identified at the origin of the separation ability: 1) the extraframework cationic distribution and particularly the nature of the counterion have a large influence on the selectivity. BaX and BaY are good candidates for the separation where BaX is selective to xylose and BaY to glucose. 2) The solvation of sugars has an impact on the adsorption. As an example, the addition of ethanol to the feed (0 to 50 % wt) increases the adsorbed amounts of sugars in both BaX and BaY. The presence of ethanol in the feed mixture impacts as well the selectivities obtained from aqueous solutions. 3) Besides the solvation, the experimental results show a selectivity inversion for BaY when increasing the supercage filling. In the same trend, the microscopic study (force field calculations) reveals that the mentioned parameter has as well an impact on the selectivity. In an attempt to go beyond these microscopic findings, 13C MAS NMR reveals that pyranose forms are adsorbed with the same proportions as the liquid phase. In parallel, Density Functional Theory (DFT) calculations allow to identify the main adsorption modes of glucose and xylose. Thanks to these findings, different improvement axes are identified as candidates to optimize the separation performance by inducing sugar accommodations closer to the ideal key-lock mechanism.La biomasse de seconde génération est essentiellement composée de sucres qui constituent une matière première pour produire des molécules à haute valeur ajoutée. Pour obtenir des flux séparés de chaque sucre (xylose, glucose, …), l’incorporation d’une étape de séparation après le fractionnement de la lignocellulose est nécessaire dans une bioraffinerie. Ce travail a pour objectif d’étudier et de rationaliser la séparation de monosaccharides d’origine lignocellulosiques (glucose et xylose) par adsorption. Pour cela, la supercage d’une zéolithe de type faujasite est sélectionnée comme modèle pour promouvoir la séparation de sucres. Une bibliothèque d’adsorbants est construite en utilisant l’échange ionique de deux structures présentant des forces électrostatiques différentes: NaX (Si/Al = 1.2) et NaY (Si/Al = 2.6). La bibliothèque des adsorbants est caractérisée et testée pour la séparation de xylose/glucose. Grâce à la combinaison d’approches expérimentales et théoriques, plusieurs paramètres sont identifiés à l’origine du pouvoir de séparation: 1) la distribution cationique et en particulier la nature du cation compensateur ont une large influence sur la sélectivité. BaX et BaY sont de bons candidats pour la séparation où BaX est sélective vers le xylose et BaY sélective vers le glucose. 2) La solvatation des sucres a un impact sur l’adsorption. Par exemple, l’addition de l’éthanol à la charge (entre 0 et 50 % poids) augmente les quantités adsorbées des deux sucres dans BaX et BaY. La présence de l’éthanol dans le mélange impacte également les sélectivités obtenues à partir des solutions aqueuses. 3) Outre la solvatation, les résultats expérimentaux montrent une inversion de sélectivité pour BaY en augmentant le remplissage de la supercage. L’étude de modélisation à l’échelle microscopique (calculs champs de forces) confirme que le taux de remplissage a un impact sur la sélectivité. Afin d’obtenir davantage de détails structurels sur les modes d’adsorption, des expériences de 13C MAS NMR ont été menées et révèlent que seules les formes pyranoses sont adsorbées, avec des proportions similaires à celles de la phase liquide. En parallèle, les calculs de la Théorie de la Fonctionnelle de Densité (DFT) permettent d’identifier les principaux modes d’adsorption du glucose et xylose. Grâce à ces résultats, différents axes d’amélioration sont identifiés comme candidats pour optimiser les performances de séparation en induisant un placement proche du mécanisme clé-serrure

Generation of a large synthetic database of office tower’s energy demand using simulation and machine learning

Machine learning (ML) has proven to be an effective technique serving as a predictive surrogate model for evaluating the performance of buildings. This approach provides considerable benefits such as reduced processing time, simplified predictions and computational efficiency. This study presents an alternative approach using a decision tree (DT) model to predict the hourly cooling loads of adaptive façade (AF) in significantly less time than when applying building performance simulation (BPS). Due to the absence of real-world data, generative parametric modelling of a prototypical office tower with an adaptive façade shading system situated in an urban setting was carried out along with simulation of its energy demand using the Honeybee add-on for Rhino/Grasshopper software. The generated large synthetic datasets were fed in so as to train and test the decision tree model. The prediction results revealed an extremely accurate model capable of estimating cooling loads in a matter of seconds. The paper concludes by arguing that decision tree surrogate models can be effectively used by researchers and designers to assess their future adaptive façade design

Novel One-Step Process for the Production of Bioplastic from Rapeseed Press Cake

Crude rapeseed cake was employed as the starting material for the preparation of protein-based bioplastic films through a wet process. A simple exposure of the agricultural waste to formic acid realized at 40 °C for 15 min could afford a slurry ready for producing robust bioplastic films by casting without another plasticizer addition. After determining the optimal process conditions, all films and membranes were successively characterized by DSC and FT-IR spectroscopy. They were also tested for their water absorption capacity, tensile strength, and elongation at break performance. The respective surface morphology and elementary composition of the products were determined by FE-SEM/EDX. Some attempts to improve their intrinsic properties were performed by loading graphene oxide inside the biopolymer three-dimensional matrix