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To mitigate the environmental hazards aroused by fossil-based lubricants, the development of eco-friendly internal lubricants is imperative. Siloxane-based internal lubricants, widely applied as plasticizers in polymeric compounds, are a promising option. However, their impacts on the tribological properties of polymeric tribocomponents are still unclarified. Therefore, in the current study, a siloxane-based internal lubricant with the product name ‘EverGlide MB 1550 (EG)’ was dispersed into a polybutylene terephthalate (PBT)-based tribological composite to investigate whether the tribological properties of the composite can be optimized. A block-on-ring (BOR) test configuration was used for this purpose. It was found that the addition of EG to the composite significantly improved the tribological behavior; the improvement was particularly significant under lower load conditions (pv-product ≤ 2 MPa∙m/s). Compared to the reference PBT composite, the addition of EG reduced the friction coefficient (COF) by about 30% and the specific wear rate by about 14%. An accompanying surface analytical investigation using photoelectron spectroscopy to elucidate the effective mechanisms at the molecular level showed the availability of tribologically effective and free EG after its addition to the composite in the relevant tribocontact.

Recent years witness the success of pre-trained models to alleviate the data sparsity problem in recommender systems. However, existing pre-trained models for recommendation mainly focus on leveraging universal sequence patterns from user behavior sequences and item information, whereas ignore heterogeneous user information to capture personalized interests, which has been shown to contribute to the personalized recommendation. In this paper, we propose a simple yet effective model, called User-aware Pre-training for Recommendation (UPRec), which could flexibly encode heterogeneous user information into the sequential modeling of user behaviors. Specifically, UPRec first encodes the sequential behavior to generate user embeddings, and then jointly optimizes the model with the sequential objective and user-aware objective constructed from the user attributes and structured social graphs. Comprehensive experimental results on two real-world large-scale recommendation datasets demonstrate that UPRec can effectively enrich the user representations with user attributes and social relations and thus provide more appropriate recommendations for users.

The rise of artificial intelligence applications has led to a surge in Internet of Things (IoT) research. Biometric recognition methods are extensively used in IoT access control due to their convenience. To address the limitations of unimodal biometric recognition systems, we propose an attention-based multimodal biometric recognition (AMBR) network that incorporates attention mechanisms to extract biometric features and fuse the modalities effectively. Additionally, to overcome issues of data privacy and regulation associated with collecting training data in IoT systems, we utilize Federated Learning (FL) to train our model This collaborative machine-learning approach enables data parties to train models while preserving data privacy. Our proposed approach achieves 0.68%, 0.47%, and 0.80% Equal Error Rate (EER) on the three VoxCeleb1 official trial lists, performs favorably against the current methods, and the experimental results in FL settings illustrate the potential of AMBR with an FL approach in the multimodal biometric recognition scenario.

Generally, the filament layer adhesion in articles 3D printed by Fused Deposition Modeling (FDM, one of 3D printing method) is completely provided by the diffusion and entanglement of molecular chains between adjacent layers. However, the poor layer adhesion and strong anisotropy in FDM due to the difficult movement of polymer chains and the complex thermal history during 3D printing have turned to be the main factors restricting the development of FDM. In this paper, poly (lactic acid)/poly (butylene adipate terephthalate)/poly (lactic acid) grafted glycidyl methacrylate biodegradable blend (PLA/PBAT/PLA-g-GMA, PLA-g-GMA was a self-made compatibilizer) was used as the matrix. A low molecular weight three-armed PLA (noted as U-PLA) was synthesized by ring opening polymerization of lactide which was initiated by trimethylolpropane (TMP) and end capping with double bonds. Then, U-PLA and phenylbis (2,4,6-trimethylbenzoyl) phosphine oxide (XBPO) was added in the matrix. The rheological results found that U-PLA acted as a low molecular weight plasticizer to promote the fluidity of the matrix during 3D printing process. On the other hand, compared with polymer chains in matrix, U-PLA was more prone to cross interlayer motion, resulting in stronger physical entanglements. With UV irradiation, XBPO was triggered to release free radicals and initiated crosslinking at terminal double bonds of U-PLA which rich concentrated between adjacent layers. Molecular dynamics and experimental results showed that with increase of U-PLA, the interlayer bonding strength of FDM specimens increased significantly. The maximum tensile strength of 3D printing specimens increased by 82.5% (printing direction at 90°). This work clearly showed that with UV irradiation covalent bonds were successfully architected between filament layers of FDM specimens.

The user equipment and directory server stay in the state of asymmetric information in anonymous networks, so that it is hard to coordinate information leakage prevention and information sharing when we explore the routing addressing technology. The severe security risk faced by existing anonymous networks, such as Tor and I2P, is the leakage of privacy information of routing nodes. This paper tries to resolve this problem and proposes a novel secure querying scheme of the routing table (SQRT) based on oblivious transfer, which can effectively ensure that both user equipment and directory server faithfully follow the routing querying protocol and protect the privacy information of both parties to the greatest extent. The SQRT scheme can realize that the directory server cannot only send the required routing nodes to the user equipment, but can also ensure that the directory server cannot know the exact routing nodes received by the user equipment and protect the information of other routing nodes in the directory server from disclosure. Security analysis shows that in the scenario where the directory server and user equipment are semi-honest, respectively, the SQRT scheme can ensure the privacy of both directory server and user equipment. The experimental simulation results show that compared with existing schemes, the SQRT scheme has obvious performance advantages in terms of the degree of anonymity, protocol running time, and communication traffic.

Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.

BACKGROUND:The CHADS2/CHA2DS2-VASc scores are used to predict thrombo-embolic/stroke in patients with nonvalvular atrial fibrillation (AF). Nevertheless, limited data are available regarding the association between these risk stratification for stroke and left atrial (LA) remodeling status of AF patients. The purpose of this study was to explore the association between these scores and LA remodeling status assessed quantificationally by echocardiography in AF patients. METHODS:One hundred AF patients were divided into 3 groups based on the CHA2DS2-VASc/CHADS2 score: the score of 0 (low stroke risk), the score of 1 (moderate stroke risk) and the score of ≥2 (high stroke risk). All patients were performed through conventional and velocity vector imaging echocardiography. Echocardiographic parameters: maximum LA volume index (LAVImax), LA total emptying fraction (LAEFt) and LA mean strain were obtained to assess quantificationally LA remodeling status. RESULTS:On categorizing with CHA2DS2-VASc, the score of 1 group showed augment in LAVImax and attenuation in LA mean strain derived from VVI, compared with the score of 0 group (LAVImax: 40.27±21.91 vs. 26.79±7.87, p=0.002; LA mean strain: 15.18±6.36 vs. 22±8.54, p=0.001). On categorizing with the CHADS2 score, similar trends were seen between the score of ≥2 and 1 groups (LAVImax: 43.72±13.77 vs. 31.41±9.50, p