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This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model's predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model's high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas. [ABSTRACT FROM AUTHOR]

Discrepancies in reported values of exciton binding energy (Eb) for organic semiconductors (OSs) necessitate a comprehensive study. Traditionally, Eb is defined as the difference between the transport gap (Et) and the optical gap (Eopt). Here, the Eb values of PBnDT‐TAZ polymer variants are determined using two commonly employed methods: a combination of ultraviolet photoemission spectroscopy and low‐energy inverse photoemission spectroscopy (UPS‐LEIPS) and solid‐state cyclic voltammetry (CV). Eb values obtained by UPS‐LEIPS show low dispersion and no clear correlation with the polymer structure and thedielectric properties. In contrast, CV reveals a larger dispersion (200 meV‐1 eV) and an apparent qualitative Eb‐molecular structure correlation, as the lowest Eb values are observed for oligo‐ethylene glycol side chains. This discrepancy is discussed by examining the implications of the traditional definition of Eb. Additionally, the impact of both intrinsic and extrinsic factors contributing to the derived experimental values of Et is discussed. The differences in intrinsic and extrinsic factors highlight the context‐dependent nature of measurement when drawing global conclusions. Notably, the observed Eb trend derived from CV is not intrinsic to the pure materials but likely linked to electrolyte swelling and associated changes in dielectric environment, suggesting that high‐efficiency single‐material organic photovoltaics with low Eb may be possible via high dielectric materials. [ABSTRACT FROM AUTHOR]

Noise annoyance is a sensitive indicator of adverse noise effects and by itself means that noise affects people's quality of life. In fact, the acoustic environment has been neglected during the education of building engineers and architects relative to the thermal and lighting aspects addressed in education programs. This study aimed to investigate various non-acoustic factors on the annoyance caused by environmental noise in residential apartments. The surveys were conducted in Iran, the city of Tabriz in October and November 2019. This research is descriptive-analytical and the type of research is correlational and causal. The statistical population of this study has formed the residents of different towers of Aseman-e Tabriz Residential Complex, which was selected as the sample size by using 373 Cochran's formula. The way of selecting the sample was simple random sampling. The validity of the questionnaire has been confirmed by the professors and the reliability of the questionnaire has been obtained using Cronbach's special alpha formula for the variables in total equal to 0.857, and the variables of the situational section 0.902. Among all variables of the study, only four variables of fear with the 12.93% of variance changes, sensitivity to noise with the 11.85%, health issues of the residents with the 12.25%, satisfaction with the quality of construction, and insulation with the 12.53% were the main factors influencing. [ABSTRACT FROM AUTHOR]

Worship places are psychologically evolved in human's mind, because his unconscious mind manifests evolution differently in such places. Every religion has its concerning style, while Islam and Christianity represent two different blazes in two separate places. Church and mosque are very important worship places and designers try to create a spiritual sense of place for the audience. Whereas acoustic induces the sense to the audience much more better; this paper investigates an acoustic variable in worship places, at reverberation time was selected as main audio measurement parameter. Three mosques in Tabriz pertaining to Qajar dynasty were assessed and evaluated for their specific construction development patterns and deductible module of RT variable and the obtained results were compared with churches and mosques in Turkey, Spain, Portugal and so forth. Thus, the historical records were identified and obtained through descriptive- research analysis style of RT comparison, measurement tools and devices belonged to B&K, Denmark. Obtained results have availed numerical outputs and quality concepts adopted from numerical factors. We concluded that reverberation time of the mosques is lower than that of the churches, and acoustic quality of the mosques is more effective for speech intelligence, although church is a desirable place for music performance and mosque for speech respectively. [ABSTRACT FROM AUTHOR]