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Electrochemical techniques have been successfully applied in the past as non-destructive techniques to the cultural heritage field. In particular, linear polarisation resistance (LPR) and electrochemical impedance spectroscopy (EIS) have been employed for the onsite monitoring of corrosion on metallic works of art, providing valuable results. Such techniques have been successfully adapted from the industrial field for this particular kind of application, but a systematic evaluation of the influence of all experimental settings on the obtained results is still lacking: several factors and parameters can affect the results, and it is important to properly consider their influence for a reliable interpretation of data. Therefore, in this work, the influence of a series of experimental parameters was evaluated in order to obtain a reliable and time-effective setup by performing a series of tests on a bronze artefact. Several variables were considered, with particular attention to those affecting the reproducibility and reliability of the measurements, as well as the duration of each single acquisition. It was demonstrated, in fact, that an optimised experimental setup from the point of view of the duration could improve also reproducibility and reliability of the measurements. The optimised protocol was then adopted in the framework of a diagnostic campaign of the Monumento ai Caduti (War Memorial) of Lecco (IT) [ABSTRACT FROM AUTHOR]

Wind energy is one of the most widely used and fastest-growing renewable energy. Wind speed prediction is an efficient way to rationally dispatch wind power generation and ensure the stability of the power system. Typical deep learning algorithms, such as the convolution neural network (CNN), long short-term memory (LSTM), and their hybrid model (CNN-LSTM), have been extensively used for time-series prediction in various fields. The CNN-LSTM model exhibits superior forecasting performance by integrating the advantages of CNN and LSTM, but it fails to quantify the forecasting uncertainty that is critical for optimal management. This study proposes a probabilistic model to forecast day-ahead wind speed based on the CNN-bidirectional LSTM (BiLSTM) and deep ensemble strategy. Unlike purely statistical methods, the hybrid physical-statistical model that combines the numerical weather prediction (NWP) model and onsite measurements is employed to improve long-term forecasting accuracy. Specifically, the probabilistic CNN-BiLSTM model is developed by adjusting the network structure, and the uncertainty is optimized through a proper scoring rule. A combination of ensembles is then used to improve the robustness of probabilistic prediction. The spatial and temporal correlations of NWP data are both considered. The new probabilistic model is applied to forecast wind speed in measurements collected from the outdoor competition venues in the 2022 Winter Olympics. Nine probabilistic benchmark methods are used to compare the performance of the proposed deep ensemble model. The results indicate that the proposed model exhibits the highest forecasting accuracy and the best ability in uncertainty estimation. • A probabilistic CNN-BiLSTM model is developed using a deep ensemble strategy. • A multi-head attention layer is added to enhance the extracted predictors of the CNN layer. • The NWP model and onsite measurements are combined to improve long-term forecasting accuracy. • The spatial and temporal correlations of NWP data are both considered. [ABSTRACT FROM AUTHOR]

The present study aims to further demonstrate the cooling benefits of an extensive green roof (EGR) and fill the gap existing in the literature in terms of a sensitivity analysis of an EGR, especially in subtropical areas. First, onsite measurements were performed. The results indicated that the peak air temperatures in the chamber with the EGR were 4.0 °C and 1.9 °C lower, respectively, compared to those in the chamber with a bare roof on sunny and rainy days. Moreover, the EGR decreased the daily electricity consumption from air conditioning by up to 16.7% on sunny days and 6.7% on cloudy days. Second, the measured values were employed to validate the green roof module (GRM) in EnergyPlus. The results demonstrated that the GRM yielded accurate results in quantifying the cooling benefits of the EGR. Finally, we selected 16 factors of the EGR, each with four levels, to perform the sensitivity analysis. Range and variance analyses revealed that the factors that most significantly impacted the EGR performance were the R-value of roof construction, substrate (soil) thickness, the thermal conductivity of dry substrate, the leaf area index, leaf emissivity, and the solar absorptance of the substrate. These factors contributed 90.8% to the performance index.

Non-ferrous metal smelting takes up a large proportion of the anthropogenic mercury emission inventory in China. Zinc, lead and copper smelting are three leading sources. Onsite measurements of mercury emissions were conducted for six smelters. The mercury emission factors were 0.09–2.98 g Hg/t metal produced. Acid plants with the double-conversion double-absorption process had mercury removal efficiency of over 99%. In the flue gas after acid plants, 45–88% was oxidized mercury which can be easily scavenged in the flue gas scrubber. 70–97% of the mercury was removed from the flue gas to the waste water and 1–17% to the sulfuric acid product. Totally 0.3–13.5% of the mercury in the metal concentrate was emitted to the atmosphere. Therefore, acid plants in non-ferrous metal smelters have significant co-benefit on mercury removal, and the mercury emission factors from Chinese non-ferrous metal smelters were probably overestimated in previous studies. [Copyright &y& Elsevier]

Static var compensator (SVC) devices have been extensively utilized in modern substations to achieve high flexibility and wide tolerance to load variation. However, due to frequent switching of the thyristors in a static var compensator, large amounts of electromagnetic interference (EMI) are being generated, which may cause malfunction of the SVC or unaccepted response of victim devices positioned nearby. In this paper, an advanced acquisition system was established to implement onsite measurements in order to obtain the EMI data. A digital calibration technique for the antenna was applied to trace back the actual electromagnetic emissions. The radiated and conducted EMI data are comparatively analyzed to present detailed and useful information of the EMI levels. According to the configuration of an SVC and the dynamic performance of the random load, an equivalent model was established to simulate and predict the conducted EMI levels. Based on onsite measurements as well as theoretical analysis, pertinent immunity measures were put forward to reduce coupling impacts on the electronic units of the secondary side. [ABSTRACT FROM PUBLISHER]

The present study aims to further demonstrate the cooling benefits of an extensive green roof (EGR) and fill the gap existing in the literature in terms of a sensitivity analysis of an EGR, especially in subtropical areas. First, onsite measurements were performed. The results indicated that the peak air temperatures in the chamber with the EGR were 4.0 °C and 1.9 °C lower, respectively, compared to those in the chamber with a bare roof on sunny and rainy days. Moreover, the EGR decreased the daily electricity consumption from air conditioning by up to 16.7% on sunny days and 6.7% on cloudy days. Second, the measured values were employed to validate the green roof module (GRM) in EnergyPlus. The results demonstrated that the GRM yielded accurate results in quantifying the cooling benefits of the EGR. Finally, we selected 16 factors of the EGR, each with four levels, to perform the sensitivity analysis. Range and variance analyses revealed that the factors that most significantly impacted the EGR performance were the R-value of roof construction, substrate (soil) thickness, the thermal conductivity of dry substrate, the leaf area index, leaf emissivity, and the solar absorptance of the substrate. These factors contributed 90.8% to the performance index. [ABSTRACT FROM AUTHOR]