Characterization of Temperature Gradients According to Height in a Baroque Church by Means of Wireless Sensors

The baroque church of Saint Thomas and Saint Philip Neri (Valencia, Spain), which was built between 1727 and 1736, contains valuable paintings by renowned Spanish artists. Due to the considerable height of the central nave, the church can experience vertical temperature gradients. In order to investigate this issue, temperatures were recorded between August 2017 and February 2018 from a wireless monitoring system composed of 21 sensor nodes, which were located at different heights in the church from 2 to 13 m from the floor level. For characterizing the temperature at high, medium and low altitude heights, a novel methodology is proposed based on sparse Partial Least Squares regression (sPLS), Linear Discriminant Analysis (LDA), and the Holt-Winters method, among others, which were applied to a time series of temperature. This approach is helpful to discriminate temperature profiles according to sensor height. Once the vertical thermal gradients for each month were characterized, it was found that temperature reached the maximum correlation with sensor height in the period between August 10th and September 9th. Furthermore, the most important features from the time series that explain this correlation are the mean temperature and the mean of moving range. In the period mentioned, the vertical thermal gradient was estimated to be about 0.043 ∘C/m, which implies a difference of 0.47 ∘C on average between sensor nodes at 2 m from the floor with respect to the upper ones located at 13 m from the floor level. The gradient was estimated as the slope from a linear regression model using height and hourly mean temperature as the predictor and response, respectively. This gradient is consistent with similar reported studies. The fact that such gradient was only found in one month suggests that the mechanisms of dust deposition on walls involved in vertical thermal gradients are not important in this case regarding the preventive conservation of artworks. Furthermore, the methodology proposed here was useful to discriminate the time series at high, medium and low altitude levels. This approach can be useful when a set of sensors is installed for microclimate monitoring in churches, cathedrals, and other historical buildings, at different levels and positions.

New Lens to Reveal the Street Interface. A Morphological-Visual Perception Methodological Contribution for Decoding the Public/Private Edge of Arterial Streets

Streets have different forms that are not defined only by their partitions, furniture, and width, but also by their edges as vital features of their spatiality. The relationship between a street and a building impacts the street interface configurations, resulting in various topological characteristics. Thus, the street interface is a physical entity that is produced by the interrelationship between urban morphological elements (street and building), and the way it is formed and used affects the livability of the street. The methods used in the current study contribute to an empirical urban morphological–visual cognitive investigation of arterial street interface configurations, particularly on the ground floor level, to assess potential relations between variations in the physical configurations that influence pedestrian visual perception using mobile eye-tracking glasses. In conclusion, this study contributes to research into developing a spatial framework for arterial street liveability, addressing the pilot case study of Avenida da República in Lisbon.

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems.

THE EFFECT OF SPATIAL AND TEMPORAL FACTORS ON OCCUPANTS’ BLIND USE BEHAVIORS IN MULTIFAMILY RESIDENTIAL BUILDINGS

ABSTRACT Existing studies show that occupants’ behavior contribute to fluctuations in energy consumption of residential units within the same building configuration. Window blinds are one of the interior design elements that the occupants use to control indoor environmental conditions. The way that occupants adjust their blinds could affect the energy performance of buildings. Thus, the purpose of this research was to identify spatial and temporal explanatory variables that correlate with occupants’ use of the blinds and determine whether those variables relate to building design and surrounding sites. Data were collected by observing how occupants in apartment buildings located in a multifamily residential complex adjust their blinds. Descriptive statistics were used to define the effect of floor level, window orientation, day of observation, the hour of observation, and weather conditions on the blind status. In addition, a generalized linear mixed model was used to predict the effect of floor level and window orientation on the occupants’ adjustment of blinds. The results revealed that occupants’ use of the blinds correlated significantly with spatial factors, such as the apartment buildings’ floor level and windows’ orientation. Interesting blind use patterns were related to temporal factors, such as the day and hour of observation.

Case Report: Occupational therapy in a patient with an overgrowth syndrome that restricts movement

Background: Overgrowth syndromes are a heterogeneous group of conditions characterized by excessive body growth - localized or generalized - commonly associated with various malformities and an increased oncological risk. Case report: Here we present the case of a 59-years old man, employed in an office, who suffers from an asymmetric overgrowth of the lower limbs. Currently the patient presents malformations of the lower left limb (hip, knee and ankle), evident on the articular and periarticular level, where there are diffuse exostoses. This case discusses the main occupational concerns relating to the patient’s workspace at a high floor level that could create critical issues in the event of an emergency exodus. Given the impossibility of placing the patient in heavy manual activities, employment is limited to office activities. Adjustments were carried out at the patient’s workstation, and thus the patient has been recognized as fit to work. Increased frequency of breaks were prescribed in order to allow the physiological alternation of postures. Conclusions: In cases of overgrowth syndromes, the exact identification of the limitations presented by the patient and observations about ambulatory functions must be carefully evaluated in order to modulate the work environment.

Stress and deformation analysis of gob-side pre-backfill driving procedure of longwall mining: a case study

At present, non-pillar entry protection in longwall mining is mainly achieved through either the gob-side entry retaining (GER) procedure or the gob-side entry driving (GED) procedure. The GER procedure leads to difficulties in maintaining the roadway in mining both the previous and current panels. A narrow coal pillar about 5–7 m must be left in the GED procedure; therefore, it causes permanent loss of some coal. The gob-side pre-backfill driving (GPD) procedure effectively removes the wasting of coal resources that exists in the GED procedure and finds an alternative way to handle the roadway maintenance problem that exists in the GER procedure. The FLAC3D software was used to numerically investigate the stress and deformation distributions and failure of the rock mass surrounding the previous and current panel roadways during each stage of the GPD procedure which requires "twice excavation and mining". The results show that the stress distribution is slightly asymmetric around the previous panel roadway after the “primary excavation”. The stronger and stiffer backfill compared to the coal turned out to be the main bearing body of the previous panel roadway during the "primary mining". The highest vertical stresses of 32.6 and 23.1 MPa, compared to the in-situ stress of 10.5 MPa, appeared in the backfill wall and coal seam, respectively. After the "primary mining", the peak vertical stress under the coal seam at the floor level was slightly higher (18.1 MPa) than that under the backfill (17.8 MPa). After the "secondary excavation", the peak vertical stress under the coal seam at the floor level was slightly lower (18.7 MPa) than that under the backfill (19.8 MPa); the maximum floor heave and maximum roof sag of the current panel roadway were 252.9 and 322.1 mm, respectively. During the "secondary mining", the stress distribution in the rock mass surrounding the current panel roadway was mainly affected by the superposition of the front abutment pressure from the current panel and the side abutment pressure from the previous panel. The floor heave of the current panel roadway reached a maximum of 321.8 mm at 5 m ahead of the working face; the roof sag increased to 828.4 mm at the working face. The peak abutment pressure appeared alternately in the backfill and the coal seam during the whole procedure of "twice excavation and mining" of the GPD procedure. The backfill provided strong bearing capacity during all stages of the GPD procedure and exhibited reliable support for the roadway. The results provide scientific insight for engineering practice of the GPD procedure.

CFD simulation of HC-290 leakage from a split type room air conditioner

HC-290 is one of the alternatives to HCFC22 in room air-conditioners. With the phase-down schedules in Kigali Amendment, it is imperative to use HC-290. However, there are concerns about its flammability in the event of a leak from the AC indoor unit (IDU). IEC 60335-2-40 has been revised to extend the capacity ranges for A2L refrigerants and is being further revised with a focus on A2 and A3 refrigerants. In real-life situations, furniture and occupants are present inside the room and these may positively or negatively influence the dispersion of any leaked HC-290. CFD simulation of dispersion of leaked HC-290 has been carried out for a variety of scenarios. The variables include leakage rates, IDU installation heights, IDU blower ON or OFF and types of furniture. Furniture, in general, appears to promote mixing, thereby reducing HC-290 stratification. The maximum concentration occurs on the side where the leak is directed by the momentum of the jet from IDU. There is no significant effect on the concentration distribution by the type and size of furniture with legs, although solid furniture has some marginal effect due to reduction of room free volume. The average concentration of HC-290 in the room is slightly more when the solid furniture like cupboards are placed some distance away from the IDU. The highest concentration at 30 cm above floor level, is 44% of LFL when the blower is ON, while it is 62% when the blower is OFF.

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.