Additive Manufacturing (AM) technology has the potential to significantly improve supply chain dynamics. The purpose of this paper is to investigate the impact of AM on spare parts supply chain. Three supply chain scenarios are investigated in this paper, namely conventional supply chain, centralised AM-based supply chain and distributed AM-based supply chain. Based on system dynamics simulations, this paper specifically compares three supply chain scenarios, in terms of total variable cost and carbon emission. The results show the spare part supply chain utilising AM is indeed superior to the traditional one in sustainable performance. It is also expected that AM can facilitate the spare parts supply chain to achieve more economic benefits along with its development. To our knowledge, this paper is one of the early studies that explores the impact of AM on supply chain performance and quantitatively examines the superiority of utilising AM in spare parts supply chain. Some suggestions are also provided to help managers adopting AM in their spare parts supply chains.

Humans possess an extraordinary ability to create and utilize tools. With the advent of foundation models, artificial intelligence systems have the potential to be equally adept in tool use as humans. This paradigm, which is dubbed as tool learning with foundation models , combines the strengths of tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. This article presents a systematic investigation and comprehensive review of tool learning. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research and formulate a general framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate generalization in tool learning. Finally, we discuss several open problems that require further investigation, such as ensuring trustworthy tool use, enabling tool creation with foundation models, and addressing personalization challenges. Overall, we hope this article could inspire future research in integrating tools with foundation models.

Rumors spread dramatically fast through online social media services, and people are exploring methods to detect rumors automatically. Existing methods typically learn semantic representations of all reposts to a rumor candidate for prediction. However, it is crucial to efficiently detect rumors as early as possible before they cause severe social disruption, which has not been well addressed by previous works. In this paper, we present a novel early rumor detection model, Credible Early Detection (CED). By regarding all reposts to a rumor candidate as a sequence, the proposed model will seek an early point-in-time for making a credible prediction. We conduct experiments on three real-world datasets, and the results demonstrate that our proposed model can remarkably reduce the time span for prediction by more than 85%, with better accuracy performance than all state-of-the-art baselines.

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.

Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.