Printable magnesium ion quasi-solid-state asymmetric supercapacitors for flexible solar-charging integrated units.

Wearable and portable self-powered units have stimulated considerable attention in both the scientific and technological realms. However, their innovative development is still limited by inefficient bulky connections between functional modules, incompatible energy storage systems with poor cycling stability, and real safety concerns. Herein, we demonstrate a flexible solar-charging integrated unit based on the design of printed magnesium ion aqueous asymmetric supercapacitors. This power unit exhibits excellent mechanical robustness, high photo-charging cycling stability (98.7% capacitance retention after 100 cycles), excellent overall energy conversion and storage efficiency (ηoverall = 17.57%), and outstanding input current tolerance. In addition, the Mg ion quasi-solid-state asymmetric supercapacitors show high energy density up to 13.1 mWh cm-3 via pseudocapacitive ion storage as investigated by an operando X-ray diffraction technique. The findings pave a practical route toward the design of future self-powered systems affording favorable safety, long life, and high energy

Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally, we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of-the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.Comment: Published as a KDD'22 full pape

Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

This paper discusses the couple-group consensus problems for a class of heterogeneous multiagent networks including the following two cases: with communication and input time delays, respectively. Different from the related cooperative networks, two novel delayed group consensus protocols are designed based on the competitive relationship between the agents. Furthermore, we absolutely relax the in-degree balance and other restrictive preconditions which existed in the relevant works. Some sufficient algebraic criteria for the achievement of couple-group consensus and the upper bound of the input time delays are technically obtained via the frequency domain method and matrix theory, respectively. The results show that the achievement of the couple-group consensus depends on the second-order agents’ in-degree and the control parameters of the systems, whereas it is independent of the communication time delays. Meanwhile, the upper bound of the input time delay is determined by the control parameters and the in-degree of the first-order agents. Finally, the validity of the proposed results is verified by several simulated examples

A Method of L1-Norm Principal Component Analysis for Functional Data

Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracted attention. Functional data principal component analysis (FPCA), as an unsupervised machine learning method, plays a vital role in the analysis of functional data. FPCA is the primary step for functional data exploration, and the reliability of FPCA plays an important role in subsequent analysis. However, classical L2-norm functional data principal component analysis (L2-norm FPCA) is sensitive to outliers. Inspired by the multivariate data L1-norm principal component analysis methods, we propose an L1-norm functional data principal component analysis method (L1-norm FPCA). Because the proposed method utilizes L1-norm, the L1-norm FPCs are less sensitive to the outliers than L2-norm FPCs which are the characteristic functions of symmetric covariance operator. A corresponding algorithm for solving the L1-norm maximized optimization model is extended to functional data based on the idea of the multivariate data L1-norm principal component analysis method. Numerical experiments show that L1-norm FPCA proposed in this paper has a better robustness than L2-norm FPCA, and the reconstruction ability of the L1-norm principal component analysis to the original uncontaminated functional data is as good as that of the L2-norm principal component analysis

Enhancing the thermal, electrical, and mechanical properties of silicone rubber by addition of graphene nanoplatelets

Graphene nanoplatelets (GNPs)/silicone rubber composites were prepared with the assistance of the Flacktek SpeedMixer. A scanning electron microscope (SEM), transmission electron microscopy (TEM), Raman spectra, Fourier-transform infrared spectra (FTIR), and X-ray photoelectron spectra (XPS) were carried out to characterize the structure of graphene nanoplatelets. An electronic universal testing machine, laser thermal conductivity analysis (LFA), thermogravimetric analysis (TGA), and a scanning electron microscope (SEM) reveal the effects of GNP loading content on the thermal conductivity, electrical, and mechanical properties of the composites. The results show that the GNPs present a homogeneous dispersion in silicone rubber and the thermal conductivity of composites exhibits improving from 0.16 to 0.26W / (m center dot K) (an increase of 53.1%) and the tensile strength varies from 0.240 to 0.608 MPa (an increase of 153%) with the addition of a low content (0-8%) of GNPs. In addition, the thermal stability of silicone rubber composites is significantly enhanced. (C) 2015 Elsevier Ltd. All rights reserved

Does isolated greater trochanter implication affect hip abducent strength and functions in intertrochanteric fracture?

Abstract Background A fracture in the isolated greater trochanter is an infrequent type of femoral intertrochanteric fracture. The gluteus medius and gluteus minimus are abducent muscle groups with attachments located on the greater trochanter. Thus, a fracture of the greater trochanter could cause avulsion injury of these attachment points and eventually affect the abducent function of the hip joint and cause chronic pain. Despite these prospects, the impact of a greater trochanter fracture on abducent strength and hip joint function have yet to be investigated. Methods Patients who were diagnosed with an isolated greater trochanter fracture (via computed tomography scan and X-ray) and underwent conservative treatment from June 2013 to October 2016 were included in the present study. Magnetic resonance imaging (MRI) was used to verify the morbidity of recessive fractures. Patients’ Harris Hip Scores were determined at 3 months, 6 months, and 12 months and the abducent strength and range of motion of the hip joint on the injured side were analyzed and compared to those on the healthy side. Result Among 32 patients, there were 7 individuals diagnosed with isolated greater trochanter fractures by MRI, and 25 individuals whose fractures were found to have extended into the intertrochanteric region, wherein the recessive intertrochanteric region fractures had no relationship with patients’ age, gender, or weight. After 12 months of conservative treatment, 7 patients still complained of pain in the hip joint. The average Harris Hip Score was 87.84 ± 4.83, and the abducent range of the hip joint on the injured side (42.02 ± 13.93°) was not significantly different from that of the healthy side (46.24 ± 7.93°). The abducent strength of the hip joint of the injured side was 121.32 ± 41.06 N which was significantly lower than that of healthy side (137.44 ± 42.21 N). Conclusion Results from this investigation suggest that an isolated greater trochanter fracture attenuates the abducent strength of the hip joint, which may be related to injuries of the ligaments and muscles around the greater trochanter. The surgical skills and methods of addressing isolated greater trochanter fractures merit further investigation

The Application of Cellulose Nanofibrils in Energy Systems

Nanocellulose has emerged as a highly promising and sustainable nanomaterial due to its unique structures, exceptional properties, and abundance in nature. In this comprehensive review, we delve into current research activities focused on harnessing the potential of nanocellulose for advanced electrochemical energy storage applications. We commence with a brief introduction to the structural features of cellulose nanofibers found within the cellulose resources’ cell walls. Subsequently, we explore various processes that have been investigated for utilizing cellulose in the realm of energy storage. In contrast to traditional binders, we place significant emphasis on the utilization of solid electrolytes and 3D printing techniques. Additionally, we examine different application areas, including supercapacitors, lithium-ion batteries, and Zn-ion batteries. Within this section, our primary focus lies in integrating nanocellulose with other active materials to develop flexible substrates such as films and aerogels. Lastly, we present our perspectives on several key areas that require further exploration in this dynamic research field in the future

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment. While previous work has made significant efforts to learn traffic temporal dynamics and spatial dependencies, two key limitations exist in current models. First, only the neighboring spatial correlations among adjacent regions are considered in most existing methods, and the global interregion dependency is ignored. Additionally, these methods fail to encode the complex traffic transition regularities exhibited with time-dependent and multi-resolution in nature. To tackle these challenges, we develop a new traffic prediction framework–Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular, ST-GDN is a hierarchically structured graph neural architecture which learns not only the local region-wise geographical dependencies, but also the spatial semantics from a global perspective. Furthermore, a multi-scale attention network is developed to empower ST-GDN with the capability of capturing multi-level temporal dynamics. Experiments on four real-life traffic datasets demonstrate that ST-GDN outperforms different types of state-of-the-art baselines

Nitrogen-Doped Porous MXene (Ti3C2) for Flexible Supercapacitors with Enhanced Storage Performance

Flexible supercapacitors (FSCs) are limited in flexible electronics applications due to their low energy density. Therefore, developing electrode materials with high energy density, high electrochemical activity, and remarkable flexibility is challenging. Herein, we designed nitrogen-doped porous MXene (N-MXene), using melamine-formaldehyde (MF) microspheres as a template and nitrogen source. We combined it with an electrospinning process to produce a highly flexible nitrogen-doped porous MXene nanofiber (N-MXene-F) as a self-supporting electrode material and assembled it into a symmetrical supercapacitor (SSC). On the one hand, the interconnected mesh structure allows the electrolyte to penetrate the porous network to fully infiltrate the material surface, shortening the ion transport channels; on the other hand, the uniform nitrogen doping enhances the pseudocapacitive performance. As a result, the as-assembled SSC exhibited excellent electrochemical performance and excellent long-term durability, achieving an energy density of 12.78 Wh kg−1 at a power density of 1080 W kg−1, with long-term cycling stability up to 5000 cycles. This work demonstrates the impact of structural design and atomic doping on the electrochemical performance of MXene and opens up an exciting possibility for the fabrication of highly FSCs

3D Printing of Porous Nitrogen-Doped Ti3C2 MXene Scaffolds for High-Performance Sodium-Ion Hybrid Capacitors

2020 American Chemical Society. 3D printing technology has stimulated a burgeoning interest to fabricate customized architectures in a facile and scalable manner targeting wide ranged energy storage applications. Nevertheless, 3D-printed hybrid capacitor devices synergizing favorable energy/power density have not yet been explored thus far. Herein, we demonstrate a 3D-printed sodium-ion hybrid capacitor (SIC) based on nitrogen-doped MXene (N-Ti3C2Tx) anode and activated carbon cathode. N-Ti3C2Tx affording a well-defined porous structure and uniform nitrogen doping can be obtained via a sacrificial template method. Thus-formulated ink can be directly printed to form electrode architecture without the request of a conventional current collector. The 3D-printed SICs, with a large areal mass loading up to 15.2 mg cm-2, can harvest an areal energy/power density of 1.18 mWh cm-2/40.15 mW cm-2, outperforming the state-of-the-art 3D-printed energy storage devices. Furthermore, our SIC also achieves a gravimetric energy/power density of 101.6 Wh kg-1/3269 W kg-1. This work demonstrates that the 3D printing technology is versatile enough to construct emerging energy storage systems reconciling high energy and power density