This study aims to investigate if a relationship can be established between measured indoor conditions and student performance in classroom settings. Ten classrooms in five Victorian schools in ...Australia were selected to monitor indoor conditions and measure student attention and concentration performance using a neuropsychological assessment, d2 Test of Attention. Correlation analysis revealed that the student performance parameters, particularly TN (reaction time, speed) and CP (accuracy), established a low to moderate correlation with most of the indoor condition parameters except CO2 concentration level. An exploratory stepwise multiple regression analysis identified that the common predictors of TN are relative humidity (RH), mean radiant temperature (MRT) and PM2.5 and the common predictors of CP are MRT and PM2.5. Interestingly, relative humidity (RH) and CO2 concentration level are the important predictors of both TN and CP among the seven environmental variables in the hierarchical multiple regression model when controlling the non-environmental variables such as student age and school terms. As thermal comfort related variables such as air temperature, air velocity and MRT were correlated with school terms due to seasonal changes, they contributed to the shared variance along with school terms in the regression model. Understanding the unique and shared contribution of the indoor condition parameters to student performance can help to develop strategies to improve school building performance.
•Establishing the relationship between indoor conditions and student performance.•Common predictors of TN (reaction time, speed) and CP (accuracy) are MRT and PM2.5•RH and CO2 are the important predictors when controlling age and school terms.•Understanding the contribution of the indoor conditions to student performance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power ...density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we ...argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.
Free-convection heat transfer from vertical surfaces is widely encountered in engineering applications, yet the role played by surface alterations in the heat transfer process and their practical ...effectiveness are still points of confusion. In this work, buoyancy-driven flows over periodically ribbed vertical plates of different surface micro-textures are investigated, mainly based on an asymptotic homogenization model through which the expensive resolution of the velocity and thermal fields within the inter-rib regions is bypassed, by imposing equivalent effective boundary conditions at a virtual plane surface. Efficiency of the homogenized simulations in detecting macroscopic behavior of the Nusselt number is first assessed, compared with full feature-resolving simulations in which the effects of complex flow patterns, near and within wall corrugations, on the local Nusselt number are captured. Second, the validated model is used to construct a database of numerical results describing deviations of the average Nusselt number over different ribbed surfaces, relative to a corresponding smooth surface. Under the conditions investigated, it is found that surface roughening generally deteriorates heat transfer from vertical surfaces, with slight enhancement for geometries characterized by low thermal slip, for example, rectangular ribs of narrow inter-rib spaces. Finally, a multiple-regression analysis is conducted to formulate a correlation describing effects of the thermal-slip coefficient, the number of ribs, and the Grashof number on the surface-averaged Nusselt number; accuracy of the proposed correlation is attested via further validation. This paper aims to call attention of the heat transfer society to the ability of the homogenization approach to considerably alleviate the computational requirements for relevant simulations and, thus, to significantly accelerate parametric optimization studies.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Formaldehyde has substantial adverse impacts on human health, and formaldehyde exposure primarily occurs in indoor environments. This study investigated the infiltration rates and indoor formaldehyde ...concentrations in 5 climate zones of China. In the winter, apartments in northern China suffer from higher indoor formaldehyde concentrations than apartments in southern China. The median indoor formaldehyde concentrations were 56 μg/m3 (25%, 75%: 38, 91 μg/m3) in northern China and 40 μg/m3 (25%, 75%: 30, 61 μg/m3) in southern China. There is a clear decrease in indoor formaldehyde concentrations in China. We also studied the relationships of the indoor air temperature, years of decoration, infiltration rate and source characteristic with formaldehyde concentrations in closed conditions. A multiple regression model that related these factors to the formaldehyde concentrations in closed conditions was constructed (R2=0.75). The optimal curve for the suitable combination of temperature and infiltration rate to maintain low formaldehyde concentrations with the lowest cost was calculated for northern and southern China. By comparing the optimal curve and the state point of each city, we can infer the suitable tendency of indoor temperature and infiltration rate for each city. In Tianjin and Shenyang, apartments are overheating, thus causing a high percentage of some homes to have formaldehyde concentrations above the Chinese national standard. In Shanxi, Xinjiang, Yunnan and the Yangtze River Delta, the infiltration rate should be increased to some extent to achieve better indoor air quality. In Hunan, Hubei and Chongqing, indoor temperatures could be increased to improve indoor thermal comfort.
•There is a clear decrease in indoor formaldehyde concentrations in China.•We constructed a multiple regression model to estimate indoor formaldehyde concentrations in closed conditions.•The optimal curve to maintain low formaldehyde concentrations was calculated for Northern and Southern China.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Rice contamination by cadmium (Cd) poses a serious threat to human health, which has attracted widespread concerns in China. It is imperative to determine major soil factors influencing the ...accumulation of Cd in rice and develop prediction models to derive the threshold concentration of Cd in soil for rice food safety. In this study, the bioavailability, accumulation, and transfer of Cd in the 18 typical paddy soil-rice systems with a wide range of soil properties was investigated using pot experiments. The regression-based models incorporated with total or extractable Cd and soil properties were constructed to predict Cd content of rice grain. Pot experimental results indicated that rice showed a high accumulation potential for Cd, while rice grains grown in acid soils displayed larger Cd contents than those in neutral and alkaline soils. The pH and MnO content were major soil factors influencing the Cd accumulation of rice. Multiple regression models based on the total Cd, extractable Cd, pH, and MnO content in soils could well describe the Cd content in rice grain. Measured Cd content of rice grains from field samples demonstrated that the empirical models could quantitatively predict the Cd content of rice grains. The threshold concentrations of Cd in soils for rice food safety could be back-calculated by both EDTA-extractable and total Cd contents in soils. The EDTA-extractable Cd in soils could use as an indication to derive the threshold concentrations of Cd for rice food safety. In conclusions, multiple regression models proved reliable and practical in predicting Cd accumulation in rice grain. These empirical models could well predict the content of Cd in rice grain and deduce soil Cd threshold criteria. These results could help to quantitatively evaluate the health risk of Cd accumulation in rice crop and provide a useful reference for safe production of rice.
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•Pot experiments with 18 paddy soils was conducted to study the accumulation of Cd in rice.•The EDTA-extractable Cd well reflected phytoavailability of Cd in paddy soils.•The pH and MnO content were main soil factors determining Cd content in rice grains.•Multiple regression models could well predict the accumulation of Cd in rice grains.•Threshold concentration of soil Cd for rice safety could deduce from prediction model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
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