Additive manufacturing (AM) technology is extensively utilized in aerospace and industrial manufacturing. However, parts built using AM are susceptible to spheroidization, porosity, cracks, and poor surface quality, making it difficult to establish an actionable product quality degree. Hence, developing a reasonable method to equate product quality with a new degree and further analyzing the product quality based on these standards has proven effective for enhancing part quality in AM. To achieve this goal, this paper proposes a surface roughness classification method that utilizes surface roughness analysis and sample enhancement. This method leverages edge cloud cooperation to efficiently analyze and integrate data from different sensors, enabling real-time monitoring and adjustment of the manufacturing process. Subsequently, the quality degree analysis system was developed utilizing matter-element extension cloud model. Furthermore, a bidirectional-gated recurrent unit (Bi-GRU) based model for quality classification and recognition has been established, with Wasserstein generative adversarial network (WGAN) employed for sample enhancement to address the issue of imbalanced column classification and to enhance the accuracy of both classification and recognition. Finally, the results obtained from this case study demonstrate through comparative experiments that the proposed method for classifying surface roughness can accurately identify 98% of prepared samples.

As significant sources of energy consumption and carbon emissions, data centers have become a focal point for improving energy efficiency worldwide. To address the challenges of high computational resource demands and limited adaptability of traditional prediction models to complex conditions, this paper proposes an artificial intelligence-enabled predictive energy saving planning based on the Transformer-GRU model for predicting coolant temperature in the liquid cooling system of data centers. By integrating the self-attention mechanism of the Transformer and the time-series prediction strengths of GRU, the model performs correlation analysis and feature extraction of key parameters to achieve high-precision predictions of coolant return temperature. Experimental results demonstrate the model’s superior accuracy compared to traditional prediction models, achieving an MSE of 1.349, RMSE of 1.157, MAPE of 0.0244, and R 2 of 81.07 %, significantly outperforming baseline models such as Transformer-LSTM (MSE = 1.355), Informer (MSE = 1.356), Reformer (MSE = 1.353), DeepAR (MSE = 1.385), LSTM (MSE = 1.351), GRU (MSE = 1.366), and CNN-GRU (MSE = 1.363). The model maintains high predictive accuracy under fluctuating environments and complex cooling conditions, effectively reducing the operational energy consumption of the liquid cooling system. This advancement not only enhances cooling efficiency but also drives data centers toward greater intelligence and sustainability. By leveraging real-time monitoring data and predictive control, the model dynamically optimizes cooling strategies, reducing coolant and energy usage while promoting sustainable resource utilization. Additionally, this study offers implementation insights for high-performance computing environments, laying the groundwork for future research on extending model capabilities and integrating multimodal data.

Electricity is fundamental to national economic and social development, and its stable supply depends on accurate power load forecasting. Thus, developing precise forecasting models is essential for efficient power system operation. However, increasing global energy demand exacerbates the volatility, randomness, and intermittency of power loads, compromising forecasting accuracy. To address complex dynamic data characteristics, this study proposes a hybrid forecasting method integrating parallel decomposition and deep learning. The method first decomposes the original data into multiple modal components and stable feature items, iteratively generating optimal sub-feature sets. Subsequently, an optimization framework is constructed based on the Sparrow Search Algorithm (SSA). This framework integrates binary feature selection with hyperparameter tuning. The tuned hyperparameters belong to an advanced neural network combining a Bidirectional Temporal Convolutional Network (BiTCN) and a Long Short-Term Memory (LSTM) network. This achieves joint feature and parameter optimization. Compared with traditional methods, this method fully exploits the temporal and structural characteristics of the data by integrating feature selection and hyperparameter optimization. For 6-step forecasting on dataset1, the method achieves a mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) are 2.30 %, 845.8 and 606.2, respectively.

The emergence of Industry 4.0 and growing interest in human resources (HR) analytics has prompted investments in tech-enabled HR management. Drawing on the UTAUT theory, this study aims to explore the strategic positioning and future technological avenues of smart technology-driven HRM by identifying articles using the PRISMA approach and applied bibliometric analysis to extract themes. The analysis reveals three clusters of HR technology-based research themes and six current research themes guiding future research directions. The current study also categorizes five significant gaps in extant research. In addition, the study maps current research themes to the United Nations' sustainable development goal (SDG) initiatives to enable researchers to understand the sustainability focus on technology-based HRM research. The present study argues for the role of technology-led HR research themes in becoming a part of sustainable business models, stakeholders, and systems thinking.