Abstract ：

High energy consumption represents one of the hindrances in enabling large scale application of membrane distillation. In this work, experimental and simulation studies of a double-effect direct-contact membrane distillation integrated with a single-stage vapor-compression heat pump (DE-DCMD-HP) were carried out for concentrating tap water with the aim to evaluate the energy saving of the integrated system. It was found during our experiments that an auxiliary cooler must be added to remove the excess heat from HP to enable the integrated system to reach the steady-state condition. The temperature-heat flux plots were first utilized to analyze how HP and DE-DCMD affected each other and reveal their coupling mechanism. The simulation results showed that the increase in the first-effect feed inlet temperature, Tfi,1 and the flow rate, V caused the thermodynamic cycle line of refrigerant shift to higher temperature, which led to the increase of the input power of the compressor and the auxiliary cooler, ultimately affecting the water production mass rate, md, the coefficient of performance, COP, the gain output ratio, GOR, and the specific energy consumption, SEC. The experimental and simulation results revealed that increasing Tfi,1 and V increased the permeate flux Nh and GOR and reduced the SEC. The performances for three different DCMD configurations were furthermore evaluated via experiments at constant feed temperature whereby the SEC decreased from 2168 kWh·t−1 for single-effect DCMD to 1085 kWh·t−1 for DE-DCMD to 257 kWh·t−1 for DE-DCMD-HP. © 2024 The Authors

Keyword ：

Thermal cycling Coefficient of performance Vapor compression refrigeration Heat pump systems Energy utilization

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Abstract ：

The rapid advancement of spectrally selective materials has enabled the development of smart windows that can regulate different bands of solar radiation. However, existing methods for calculating spectral properties vary, leading to inconsistent results when evaluating their light-electricity-heat-color performance. Therefore, this paper presents five methods for calculating window spectral properties, intending to compare and identify the most effective approach for assessing building thermal performance, energy efficiency, and daylighting. The methods focus primarily on the solar spectrum input and the glazing spectrum response. The solar spectrum inputs include the Beijing local solar spectrum, ASTM G173-03, and ISO 9845 (methods S1 to S3), and the glazing spectrum response methods differ based on whether they account for spectral selectivity and the choice of spectral weighting functions (methods S3 to S5). The findings reveal that variations in solar spectrum input have a minimal effect on window performance, while differences in glazing spectral response calculations significantly impact results. It is particularly evident in thermochromic windows, where discrepancies in energy efficiency, specifically in lighting load, can reach up to 67.21 %, and variations in dynamic daylighting performance, specifically in Useful Daylight Illuminance (UDI) below 500 lx, can be as high as 28.27 %. To provide more accurate assessment results of glazing windows, this study recommends using the spectral calculation method S1, which incorporates the local solar spectrum and applies the standard luminous efficiency function to the glazing spectrum response. This paper highlights that further refinement of spectral calculation methods will enhance their utility for performance evaluation and guide the reverse design of innovative window features.

Keyword ：

Glazing window Performance evaluation Glazing spectrum response Optical property Solar spectrum

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For software evolution, user feedback has become a meaningful way to improve applications. Recent studies show a significant increase in analyzing end-user feedback from various social media platforms for software evolution. However, less attention has been given to the end-user feedback for low-rating software applications. Also, such approaches are developed mainly on the understanding of human annotators who might have subconsciously tried for a second guess, questioning the validity of the methods. For this purpose, we proposed an approach that analyzes end-user feedback for low-rating applications to identify the end-user opinion types associated with negative reviews (an issue or bug). Also, we utilized Generative Artificial Intelligence (AI), i.e., ChatGPT, as an annotator and negotiator when preparing a truth set for the deep learning (DL) classifiers to identify end-user emotion. For the proposed approach, we first used the ChatGPT Application Programming Interface (API) to identify negative end-user feedback by processing 71853 reviews collected from 45 apps in the Amazon store. Next, a novel grounded theory is developed by manually processing end-user negative feedback to identify frequently associated emotion types, including anger, confusion, disgust, distrust, disappointment, fear, frustration, and sadness. Next, two datasets were developed, one with human annotators using a content analysis approach and the other using ChatGPT API with the identified emotion types. Next, another round is conducted with ChatGPT to negotiate over the conflicts with the human-annotated dataset, resulting in a conflict-free emotion detection dataset. Finally, various DL classifiers, including LSTM, BILSTM, CNN, RNN, GRU, BiGRU and BiRNN, are employed to identify their efficacy in detecting end-users emotions by preprocessing the input data, applying feature engineering, balancing the data set, and then training and testing them using a cross-validation approach. We obtained an average accuracy of 94%, 94%, 93%, 92%, 91%, 91%, and 85%, with LSTM, BILSTM, RNN, CNN, GRU, BiGRU and BiRNN, respectively, showing improved results with the truth set curated with human and ChatGPT. Using ChatGPT as an annotator and negotiator can help automate and validate the annotation process, resulting in better DL performances. © 2024 Elsevier Ltd

Keyword ：

Input output programs Application programs Emotion Recognition Software testing Requirements engineering Deep learning

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Abstract ：

Deep Multi-view Graph Clustering (DMGC) aims to partition instances into different groups using the graph information extracted from multi-view data. The mainstream framework of DMGC methods applies graph neural networks to embed structure information into the view-specific representations and fuse them for the consensus representation. However, on one hand, we find that the graph learned in advance is not ideal for clustering as it is constructed by original multi-view data and localized connecting. On the other hand, most existing methods learn the consensus representation in a late fusion manner, which fails to propagate the structure relations across multiple views. Inspired by the observations, we propose a Structure-adaptive Unified gRaph nEural network for multi-view clusteRing (SURER), which can jointly learn a heterogeneous multi-view unified graph and robust graph neural networks for multi-view clustering. Specifically, we first design a graph structure learning module to refine the original view-specific attribute graphs, which removes false edges and discovers the potential connection. According to the view-specific refined attribute graphs, we integrate them into a unified heterogeneous graph by linking the representations of the same sample from different views. Furthermore, we use the unified heterogeneous graph as the input of the graph neural network to learn the consensus representation for each instance, effectively integrating complementary information from various views. Extensive experiments on diverse datasets demonstrate the superior effectiveness of our method compared to other state-of-the-art approaches. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Keyword ：

Graph neural networks

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We examine the effect of corporate innovation input and output on analysts' estimates of the cost of equity based on data from public listed Chinese companies between 2007 and 2017. Analysts' estimates of the cost of equity significantly increase with corporate innovation input, whereas their estimates decrease with corporate innovation output, as indicated by this research. The results of additional analysis suggest that the role of corporate innovation input in increasing the estimates of the cost of equity is more significant with an increase in environmental uncertainty and negatively mitigated by strong survival ability. However, the role of corporate innovation output in decreasing analysts' estimates of the cost of equity is not significantly moderated by environmental uncertainty or corporate survival ability. Furthermore, we find that analysts consider financial constraints and information asymmetry when estimating the cost of equity. On this basis, the present study confirms that investors are significantly responsive to analysts' estimates of the cost of equity during investment decision making.

Keyword ：

Analysts' estimates of the cost of equity Innovation output Innovation input

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Abstract ：

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

Keyword ：

monthly streamflow prediction data-driven models Yellow River LSTM lagged time analysis

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Municipal solid waste incineration (MSWI) processes emit the greenhouse gas carbon dioxide (CO2), contributing to global atmospheric warming. In order to achieve the dual carbon goal and protect the ecological environment, it is imperative to predict CO2 emission concentrations and implement proactive control measures. Addressing these concerns, this study introduces a CO2 emission prediction model for the MSWI process based on the LSTM-compensated ARIMA model. Initially, the ARIMA model serves as the primary predictor for CO2 emissions and calculates its prediction residuals. Subsequently, the LSTM model functions as a compensatory model, utilizing the predicted residuals as input truth values for constructing predictions. Finally, the predicted values from the primary and compensatory models are weighted and combined to yield the ultimate result. Experimental results, conducted using data from an MSWI plant in Beijing, demonstrate the efficacy of this approach. © 2024 IEEE.

Keyword ：

Waste incineration Carbon dioxide Long short-term memory Municipal solid waste Forecasting Greenhouse gases

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Abstract ：

The model bias caused by input outliers is a dramatic obstacle to the application of models in industrial processes. To cope with this problem, this article proposes a robust modeling method based on frequency reconstructed fuzzy neural network (FRFNN) for industrial process. The robust modeling consists of two parts: One is feature extraction, where a Fourier-based filter is developed with input data denoising. It enables the model to suppress high-frequency input noises and burst outliers. The other one is feature representation that is realized with a FRFNN. The soft margins of membership functions of FRFNN are designed with Fourier estimation of outliers, which have the capability of outlier-tolerant for filtered-residual outliers. Moreover, an adaptive gradient descent algorithm is introduced to update the model parameters. Based on the adaptive learning rate decaying with outliers, this algorithm is insensitive to the bias effect of outliers and also maintains convergence. Finally, the proposed robust modeling method is tested on two real-world industrial datasets with input outliers. The experimental results demonstrate that the proposed robust modeling method can strengthen robustness and achieve superior performance over other previous methods.

Keyword ：

Pollution measurement frequency reconstructed Robustness Feature extraction Fourier transform Standards outliers robust modeling Fuzzy neural networks fuzzy neural network (FNN) Uncertainty Noise reduction

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The plastic rotation angle or deflection is typically used as the indicator to evaluate the earthquake-induced damage and classify the seismic performance levels of reinforced concrete (RC) beams. Herein, one of the most important issues is to determine the seismic performance level limits of RC beams. Whereas, different countries provided their specific method to determine the limit values by considering the loading-carrying capacity of RC beams, which could not be used to describe the earthquake-induced seepage of structures, especially for underground structures. Therefore, in this study, the seismic performance level limits of RC beams were predicted by using the machine learning methods and considering the development of cracks. Firstly, the seismic performance level limits of RC beams were presented after discussing the methods in different codes and the development of cracks. Then an earthquake performance test database of RC beams was established after collecting 452 test results of RC beams, and Pearson correlation analysis was conducted for feature selection to determine the input mechanical parameters and dimensional parameters for machine learning. Meanwhile, the correlation between the inputs and limit values was analyzed using the mutual information method. Regression models of seven machine learning methods were then established to predict the performance level limits of RC beams, and the hyperparameters of the machine learning models were optimized with the TPE optimization algorithm and cross-validation. The generalization ability of the prediction models was evaluated and the accuracy of predicted results by different methods was analyzed. Finally, the predicted seismic performance level limits of RC beams could be used to evaluate the earthquake-induced damage of RC beams by combining them with the seismic behavior of RC beams.

Keyword ：

RC beams Characteristic points Cracks development Seismic performance Machine learning

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The three-dimensional numerical manifold method (3DNMM) method is further enriched to simulate wave propagation across homogeneous/jointed rock masses. For the purpose of minimizing negative effects from artificial boundaries, a viscous non-reflecting boundary, which can effectively absorb the energy of a wave, is firstly adopted to enrich 3DNMM. Then, to simulate the elastic recovery property of an infinite problem domain, a viscoelastic boundary, which is developed from the viscous nonreflecting boundary, is further adopted to enrich 3DNMM. Finally, to eliminate the noise caused by scattered waves, a force input method which can input the incident wave correctly is incorporated into 3DNMM. Five typical numerical tests on P/S-wave propagation across jointed/homogeneous rock masses are conducted to validate the enriched 3DNMM. Numerical results indicate that wave propagation problems within homogeneous and jointed rock masses can be correctly and reliably modeled with the enriched 3DNMM.

Keyword ：

rock masses viscous nonreflecting boundary 3D numerical manifold method wave propagation force input method

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