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Keywords Air quality parameters; Random forest; Machine learning-based estimation; Aspen hysys; Support vector machine; Environmental management operations Highlights * Relative humidity is crucial for air-based energy systems. * Environmental management-related operations are significantly affected by humidity. * Random forest model is presented to predict the relative humidity. * Aspen Hysys simulator is used for data extraction. * The mean absolute deviation of random forest model is 1.1%. Abstract The prediction of relative humidity is a challenging task because of its nonlinear nature. The machine learning-based prediction strategies have attained significant attention in tackling a broad class of challenging nonlinear and complex problems. The random forest algorithm is a well-proven machine learning algorithm due to its ease of training and implementation, as it requires minimal preprocessing. The random forest algorithm has hitherto not been employed for estimating air quality parameters, such as relative humidity. In this study, the random forest approach is implemented to estimate the relative humidity as a function of dry- and wet-bulb temperatures. A well-known commercial process simulator called Aspen HYSYS® V10 is linked with MATLAB® version 2019a to establish a data mining environment. The robustness of the prediction model is evaluated against varying wet-bulb depressions. There is high absolute deviation that indicates a lower prediction performance of the model against the higher wet-bulb depression i.e., ~20.0 [degree]C. The random forest model can predict relative humidity with a 1.1% mean absolute deviation compared to the values obtained through Aspen HYSYS. The performance of the RF estimation model is also compared with a well-known support vector regression model. The random forest model demonstrates 74.4% better performance than the support vector machine model for the problem of interest, i.e., relative humidity estimation. This study will significantly help the practitioners in efficient designing of air-dependent energy systems as well as in better environmental management through rigorous prediction of relative humidity. Author Affiliation: (a) School of Chemical Engineering, Yeungnam University, Gyeongsan, 712-749, Republic of Korea (b) Department of Computer Science, COMSATS University Islamabad (CUI), Lahore Campus, Defense Road, Off Raiwind Road, Lahore, Pakistan (c) Sustainable Development Study Center, Government College University, Lahore, 54000, Pakistan * Corresponding author. Article History: Received 28 January 2021; Revised 18 April 2021; Accepted 29 April 2021 Byline: Kinza Qadeer (a), Ashfaq Ahmad (b), Muhammad Abdul Qyyum [maqyyum@yu.ac.kr] (a,**), Abdul-Sattar Nizami (c), Moonyong Lee [mynlee@yu.ac.kr] (a,*)

Hydrological processes such as evaporation and evapotranspiration (ET) are affected by air humidity. Air humidity is commonly expressed as relative humidity (RH) or dew point temperature ([T.sub.dew]) and is required for the determination of evaporation from water or soil surfaces and ET from cropped areas. However, the availability of humidity data is sometimes an issue. In cases in which RH data are not available, air humidity on a daily basis can be estimated by using daily maximum and minimum temperatures along with the dew point temperature. Guidelines from an international organization recommend the use of a dew point temperature slightly less than the daily minimum temperature for daily computations, when its measurements are not available. In this study, the daily average dew point temperature ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]) was taken as k[degrees]C less than the daily minimum temperature. Thus, daily RH data (maximum, minimum, and mean RH) were generated from daily maximum and minimum temperatures for Roorkee Station (India) during 2012-2016. Four different values of k (k = 0, 1,2,3) were used for determining the actual vapor pressure values used in RH estimation. Daily RH values determined using different values of k were compared with observed RH data. It was observed that for the nonsummer months, having an average value of daily mean RH greater than 60% (January-March and July-December), k = 0 resulted in minimum error in RH estimation. However, during the summer months, having an average value of daily mean RH less than 60% (April-June), k = 3 provided relatively better estimates of RH. During the summer months, large errors occurred in the estimation of maximum and minimum RH values using this approach; hence, mean RH values that are relatively better estimated during these periods should be used for hydrological applications. Further, linear [T.sub.dew]-RH relationships (with and without parabolic correction) have been assessed to determine daily [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] values. These relationships have been found to provide more accurate estimates of [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and actual vapor pressure on a daily basis than the FAO-56 vapor pressure models when only daily [RH.sub.mean] data are available (instead of [RH.sub.max] and [RH.sub.min] data) along with daily temperature data. DOI: 10.1061/(ASCE)IR.1943-4774.0001225. Author keywords: Relative humidity; Dew; Point temperature; Vapor pressure.

Keywords High humidity; Atmospheric transparency; High-rise buildings; Non-reciprocal transmission; All-weather and all terrain Highlights * A non-reciprocal MIR window with pyramidal micro-structures is designed. * The non-reciprocal window allows to radiate heat to outer space while suppressing the incoming heat flux. * A temperature reduction limit of 110 [degrees]C is possible under a low-transparency sky. * A temperature reduction of 10 [degrees]C can be preserved under an unfavourable weather and surroundings condition. * Use of the non-reciprocal window promises a 45% energy savings for cooling during China's summer, around 180 TWh. Abstract Passive radiative cooling has a potential impact on global energy consumption, while it is vulnerable to weather and surroundings, hindering its practical applications. Here, we present a facile pyramidal micro-structure that can be formed on a mid-infrared (MIR) window for achieving non-reciprocal propagation whereby the incoming heat flux from low-transparency atmosphere and/or surrounding high-rise buildings is suppressed without limiting the outgoing radiation from radiative coolers. In an ideal case without solar absorption, convective, and conductive heat losses, a temperature reduction limit of 110 [degrees]C is simulated even under a low-transparency sky. For an unfavorable weather and surrounding obstruction condition, the radiative cooler integrated with the non-reciprocal MIR window can still reach a 10 [degrees]C temperature reduction, while a standalone radiative cooler fails the sub-ambient cooling. The effects of non-reciprocal MIR windows on China's average temperatures in summer are estimated, roughly corresponding to a 45% energy savings for cooling, around 180 TWh. This universal strategy would remove the bottlenecks of putting passive radiative cooling into practice worldwide even if local weather and surrounding terrain conditions are undesirable. Author Affiliation: (a) School of Physical Science and Technology, Ningbo University, Ningbo 315211, China (b) Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China * Corresponding authors. Article History: Received 23 April 2020; Revised 19 June 2020; Accepted 4 July 2020 Byline: Mingming Wei (a), Wanchun Wu (a), Dong Li (a), Hua Xu [xuhua@nbu.edu.cn] (a,*), Yuehui Lu [yhlu@nimte.ac.cn] (b,*), Weijie Song (b)

Keywords Indoor thermal comfort; PMV-PPD indices; Uncertainty propagation; Monte Carlo simulation; Sensitivity analysis Highlights * The Predicted Mean Vote approach of ISO 7730 has been reviewed and analysed. * The Monte Carlo analysis has been applied under different environmental conditions. * A sensitivity analysis was performed in order to define the role of the variables. * The uncertainty propagation method could improve PMV model application. Abstract Today, evaluation of thermohygrometric indoor conditions is one of the most useful tools for building design and re-design and can be used to determine energy consumption in conditioned buildings. Since the beginning of the Predicted Mean Vote index (PMV), researchers have thoroughly investigated its issues in order to reach more accurate results; however, several shortcomings have yet to be solved. Among them is the uncertainty of environmental and subjective parameters linked to the standard PMV approach of ISO 7730 that classifies the thermal environment. To this end, this paper discusses the known thermal comfort models and the measurement approaches, paying particular attention to measurement uncertainties and their influence on PMV determination. Monte Carlo analysis has been applied on a data series in a 'black-box' environment, and each involved parameter has been analysed in the PMV range from -0.9 to 0.9 under different Relative Humidity conditions. Furthermore, a sensitivity analysis has been performed in order to define the role of each variable. The results showed that an uncertainty propagation method could improve PMV model application, especially where it should be very accurate (-0.2 < PMV Author Affiliation: (a) DICAR-Dipartimento Ingegneria Civile Ambientale e Architettura, Universita degli Studi di Cagliari, Via Marengo 2, 09123, Cagliari, Italy (b) Dipartimento di Architettura, Universita degli Studi di Cagliari, Via Santa Croce 67, 09124, Cagliari, Italy * Corresponding author. Article History: Received 29 December 2017; Revised 24 May 2018; Accepted 2 June 2018 Byline: R. Ricciu [ricciu@unica.it] (a), A. Galatioto [alessandra.galatioto@unica.it] (a,*), G. Desogus [gdesogus@unica.it] (a), L.A. Besalduch [besalduch@unica.it] (b)

Crackers are plasticized by water at a glass transition temperature which rises with the decrease in water content. Gluten is responsible for the loss of crispness, when the moisture content increases. The dynamic mechanical thermal analysis is a useful method for the measurement of glass transition of food products. The glass transition diagram shows the optimal level of temperature and humidity necessary for maintaining the texture of products during storage and processing. Gluten controls the mechanical properties of crackers and other cereal ingredients.

Plant-inspired 'microbot' creeps forward on two legs. Plant-inspired 'microbot' creeps forward on two legs., Author Affiliations: This miniature robot's inchworm-style movement is powered by the wet paper beneath its feet. Credit: B. Shin et al./Sci. Robot. This miniature robot's inchworm-style movement is powered by [...]

The study of heterogenous reactions of oleic acid aerosol particles with ozone is presented. A relative humidity of less than 1% is used for this purpose.