Concord University College Fujian Normal University

We present a first-principles calculation for the electronic and Li-ion diffusion properties of the LiFePO4 (010) surface modified by sulfur. The calculated formation energy indicates that the sulfur adsorption on the (010) surface of the LiFePO4 is energetically favored. Sulfur is found to form Fe-S bond with iron. A much narrower band gap (0.67 eV) of the sulfur surface-modified LiFePO4 [S-LiFePO4 (010)] is obtained, indicating the better electronic conductive properties. By the nudged elastic band method, our calculations show that the activation energy of Li ions diffusion along the one-dimensional channel on the surface can be effectively reduced by sulfur surface modification. In addition, the surface diffusion coefficient of S-LiFePO4 (010) is estimated to be about 10−11 (cm2/s) at room temperature, which implies that sulfur modification will give rise to a higher Li ion carrier mobility and enhanced electrochemical performance.

Abstract Silk nanofibers (SNFs) from abundant sources are low‐cost and environmentally friendly. Combined with other functional materials, SNFs can help create bioelectronics with excellent biocompatibility without environmental concerns. However, it is still challenging to construct an SNF‐based composite with high conductivity, flexibility, and mechanical strength for all SNF‐based electronics. Herein, this work reports the design and fabrication of Ti 3 C 2 T x ‐silver@silk nanofibers (Ti3C2Tx‐Ag@SNF) composites with multi‐dimensional heterogeneous conductive networks using combined in situ growth and vacuum filtration methods. The ultrahigh electrical conductivity of Ti 3 C 2 T x ‐Ag@SNF composites (142959 S m −1 ) provides the kirigami‐patterned soft heaters with a rapid heating rate of 87 °C s −1 . The multi‐dimensional heterogeneous network further allows the creation of electromagnetic interference shielding devices with an exceptionally high specific shielding effectiveness of 10,088 dB cm −1 . Besides working as a triboelectric layer to harvest the mechanical energy and recognize the hand gesture, the Ti 3 C 2 T x ‐Ag@SNF composites can also be combined with an ionic layer to result in a capacitive pressure sensor with a high sensitivity of 410 kPa −1 in a large range due to electronic‐double layer effect. The applications of the Ti 3 C 2 T x ‐Ag@SNF composites in recognizing human gestures and human‐machine interfaces to wirelessly control a trolley demonstrate the future development of all SNF‐based electronics.

The ubiquitous network services provided by the Beyond 5G enabled space-air-ground-sea networks (B5G-SAGS) depends on the reliability of each intelligent device within. However, the QoS of B5G-SAGS could be compromised if there exists faults on individual network. That suggests the significance of fault-diagnosis in the B5G-SAGS design. Previous works on fault diagnosis were designed without extra information to improve diagnosis accuracy. In this paper, we propose an Intelligent Drone-assisted Fault-diagnosis Algorithm (IDFA) utilizing B5G-enabled Multiple-access Edge Computing/Cloud (B5G-MEC) services to detect faulty buoys. Specifically, IFDA first employs a Cubature Kalman Filter based Radial Bias Function Neural Network (CKF-RBFNN) for each fault-diagnosis center to perform preliminary fault detection based on the data provided by both buoys and drones. The data collection path is planned utilizing the deep reinforcement learning algorithm, Deep Deterministic Policy Gradient (DDPG), on B5G-MEC servers for energy efficiency. Eventually, the collective decision made by all fault-diagnosis centers determines the faulty status of each buoy. The theoretical analysis and validation experiments show that: (i) the IDFA has a better diagnosis accuracy in both single fault detection and multi-fault classification while compared with contemporary algorithms; (ii) the IDFA obtains a high aggregation ratio and a low energy cost.

Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.

This article aims to analyze the interplay between the digital economy (DE) and the real economy (RE), examining how they impact each other in terms of empowerment and supply effects. The study object is China from 2011 to 2021. This study applies the panel vector autoregressive model (PVAR). The study’s findings underscore a delayed empowerment effect within the DE. While DE growth has the potential to substantially enhance the future overall expansion of the tangible economy, it might concurrently dampen the short-term structural balance of the latter. However, the supply effect in the RE mode exhibits a similar delay. The time-lagged factors relating to the tangible economy’s total growth and structural fine-tuning play a pivotal role in fostering the progress of DE. Self-enhancement mechanisms significantly influence the overall growth of the tangible economy. However, this mechanism does not have the same significance in regard to enhancing structural coordination. Although the tangible economy’s expansion can catalyze structural refinement, the inverse relationship—where structural enhancement profoundly fuels tangible economic growth—does not hold true to a substantial extent. By assessing the overall degree of coupling and coordination between the DE and the tangible economy, it becomes apparent that these two domains are not tightly integrated. Instead, they exist in a fundamentally coordinated state, with a year-on-year upwards trend in their alignment, albeit at a modest pace. Furthermore, this coupling coordination degree displays a progressively diminishing trend from the southeastern coastal regions to the western interior, revealing a pronounced spatial imbalance. The contribution of this paper lies in its comprehensive enhancement of the theoretical framework and empirical research in the integration of energy and digital economy, addressing sustainable development, regional economic disparities, and practical policy implications to support future strategies for blending digital advancement with renewable energy utilization.

The effective work functions for Ni/HfO2 interfaces under two strain modes (uniaxial and triaxial strains) were studied by using first-principles methods based on density functional theory. The calculated results indicate that the effective work functions are strongly affected by the type of interface and the strain states (tensile and compressive strains). For the both above strain states, the changed value of the effective work functions linearly increases with increasing strain. Moreover, it is observed that for a certain strain, the variation of the effective work function for triaxial strain state is almost larger than that for uniaxial strain state. Finally, the electrons gas model, the interface dipole, and screening role of HfO2 were used to analyze and explain the strain and interface effects in metal-oxide interfaces. The evident difference between the effective work functions of Ni-Hf and Ni-O interfaces is found to be attributed to different metallic bondings and ionic bondings via the analysis of the charge density distributions. Our work strongly suggests that controlling the strain and interface structure is a promising way for modulating the work function of Ni/HfO2 interfaces.

With the advancement of global sustainable development goals and the introduction of the ‘dual-carbon’ strategy, intelligent manufacturing (IM) has become an important pathway to promote the transformation and upgrading of enterprises. However, the ways in which IM enhances environmental, social, and corporate governance (ESG) performance, along with its potential mechanisms, remain unexplored. This study employs a two-way fixed-effects model with panel data from 4417 Chinese listed firms spanning the period 2009–2022 to examine these relationships. It is found that IM significantly improves corporate ESG performance. Robustness tests confirm the reliability of these results, and mechanism analysis highlights the mediating effects of information transparency, green technology innovation, and supply chain collaborative innovation. Furthermore, the heterogeneity analysis indicates that IM has a notably stronger effect in high-carbon-emission sectors, state-owned enterprises, and high-tech industries. This suggests that policymakers should design differentiated policies based on industry and firm characteristics to promote the adoption of IM and foster sustainable development strategies. This research contributes to expanding the theoretical understanding of how IM affects ESG while also providing empirical evidence for enterprises and governments to promote green transformation.

Using first-principles calculations within the generalized gradient approximation (GGA) +U framework, we investigate the effect of C doping on the structural and electronic properties of LiFePO4. The calculated formation energies indicate that C doped at O sites is energetically favoured, and that C dopants prefer to occupy O3 sites. The band gap of the C doped material is much narrow than that of the undoped one, indicating better electro- conductive properties. To maintain charge balance, the valence of the Fe nearest to C appears as Fe3+, and it will be helpful to the hopping of electrons.

With the economic development of the human society, growth of population, and accelerated urbanization and industrialization, increase of asymmetric carbon cycle and global climate change caused by rapid growth of energy consumption are becoming the current hot spots of all walks of life worldwide. Therefore, improving energy efficiency has become an effective way of active response to energy saving and realization of sustainable development. This paper, by analyzing the characteristics of energy efficiency, industrial structure and energy consumption structure of China and five other developed countries, establishes a VAR model and uses impulse responses function and variance decomposition methods to reveal the mechanism of energy efficiency response to industrial structure evolvement and energy consumption structure change. Research suggests that in the last 40 years, China's tertiary industry has played a greater role than its secondary industry in improving energy efficiency. Although the development of the secondary industry has a positively weak correlation with energy efficiency, according to the experience of developed countries, the inhibition of secondary industry on the energy efficiency improvement has not yet appeared in China, which indicates that the economy still needs pushing by industry with extensive and high energy consumption. Otherwise, the energy consumption relies on coal, which also has the inhibition on the energy efficiency improving. In the end, basing on such analysis, the paper discusses the response of energy efficiency to industrial restructuring and energy consumption structure change. Some conclusions can be drawn as follows: (1) Compared with developed countries, the industrial restructuring has inhibition on energy efficiency improvement in the short term until the mid-term, then the positive impact of industrial restructuring will emerge, which will improve energy efficiency. (2) Since existence of coal-based energy consumption structure for a long time, its negative effect appeared later in China than in developed countries, and the evolution of energy consumption structure in both China and developed countries has little effect on energy efficiency. (3) The influence of industrial structure change is greater than the evolution of energy consumption structure, so the impact of industrial restructuring and upgrading is the key to improve energy efficiency and carry out energy saving policies.

Abstract Most exogenous electronic skins (e‐skins) currently face challenges of complex structure and poor compatibility with the human body. Utilizing human secretions (e.g., sweat) to develop e‐skins is an effective solution strategy. Here, a new kind of “sweat‐driven” e‐skin is proposed, which realizes energy‐storage and thermal‐management multifunctions. Through the layer‐by‐layer assembly of MXene‐carbon nanotube (CNT) composite with paper, lightweight and versatile e‐skins based on supercapacitors and actuators are fabricated. Long CNTs wrap and entangle MXene nanosheets, enhancing their long‐distance conductivity. Furthermore, the CNT network overcomes the structural collapse of MXene in sweat, improving the energy‐storage performance of e‐skin. The “sweat‐driven” all‐in‐one supercapacitor with a trilayer structure is patternable, which absorbs sweat as electrolyte and harnesses the ions therein to store energy, exhibiting an areal capacitance of 282.3 mF cm −2 and a high power density (2117.8 µW cm −2 ). The “sweat‐driven” actuator with a bilayer structure can be driven by moisture (bending curvature of 0.9 cm −1 ) and sweat for personal thermal management. Therefore, the paper serves as a separator, actuating layer, patternable layer, sweat extractor, and reservoir. The “sweat‐driven” MXene‐CNT composite provides a platform for versatile e‐skins, which achieve the interaction with humans and offer insights into the development of multifunctional wearable electronics.

This study examined how visual salience affects the processing of salient information it highlights (here after called visually salient information), as well as its connection with associated content during online reading. Participants were asked to read descriptive concepts that contained a two-character key concept term with a short definition, and subsequently complete a memory test. The visual salience of the key concept terms was manipulated. The results show that visual salience shortened the reading times of key concept terms, as well as the go-past times of concept definition. In addition, improving the visual salience of the key concept terms helped subjects in the subsequent memory test to make quicker and more accurate judgments regarding incorrect concepts. These results indicate that visual salience accelerates the lexical processing of visually salient information and helps readers build faster and more elaborate connections between visually salient information and associated content in the subsequent integration.

The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques.

This study utilizes the flipped classroom approach in conjunction with Bloom's taxonomy. Through a series of literature reviews, the author evaluates the merits and downsides of the flipped classroom. This article proposes a method that combines Bloom's taxonomy of instructional goals and flipped classrooms to address instruction problems. This article starts with flipped classroom's history and application. It continues Bloom's taxonomy research and investigates the combination of the flipped classroom and Bloom's taxonomy. The findings show that the flipped classroom has several drawbacks. To begin, its inconsistency arises from the variety of course categories; Secondly, instructors cannot get immediate feedback when producing the pre-class study material. Thus, measures to these shortcomings should begin with curriculum design and effective instructor-student interaction. According to the results, educators must improve their ability to engage students and alter their design of classroom activities. Teachers must do various additional tasks, including guiding, encouraging, and diverting traditional teaching methods toward more constructive learning programs.

Aggregating Ti–Mg–Al co-dopant distribution in the surface layer can significantly inhibit surface oxygen losses, synergistically promoting the surface stability of LiCoO 2 at high voltages.

The all-solid-state batteries (ASSBs) are of particular interest because of their higher energy density and improved safety. However, the interfacial instability and resulting high interfacial resistance between the cathode and solid electrolyte (SE) have become the major challenges for the practical application of ASSBs. Herein, we report a stable LiFePO4 cathode/γ-Li3PO4 SE interface and systemically investigate the mechanism of Li-ion transport at the interface and the effects of surface nitrogen doping using first-principles calculations. It is found that delithiation at the LiFePO4/γ-Li3PO4 interface initially occurs at the topmost layer of the LiFePO4 cathode side, and hopping through the interface barrier is a rate-limiting step for Li mobility. Nitrogen doping leads to local structural distortion occurred at the interface, affecting the interfacial Li+ diffusion kinetics. Furthermore, the underlying mechanisms in which the different N doping sites alter the Li diffusion barrier are analyzed. We find that, by a rational design, N doping could significantly enhance Li+ diffusion kinetics. Further analysis of the electronic structure of the interface system reveals that the Li3PO4 electrolyte is electrochemically stable against the LiFePO4 cathode in the N-doped interface. Our findings provide a microscopic understanding of the Li+ transport at solid–solid LiFePO4/γ-Li3PO4 interface and suggest that controlling synthesis condition can be critical for enhancing Li+ transport at the N-doped LiFePO4/γ-Li3PO4 interface in an ASSB.

Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

IoT sensors have already penetrated into extremely broad fields such as industrial production, smart home, environmental protection, medical diagnosis, and bioengineering. Although efficient data fusion helps improve the quality of intelligent services provided by the Internet of things, because the perceived data carry the sensitive information of the perceived object, the data fusion process is prone to the risk of privacy leakage. To this end, in this paper, we proposed a privacy-enhanced federated learning data fusion strategy. This strategy adds Gaussian noise at different stages of federated learning to achieve privacy protection in the data fusion process. Experimental results show that this strategy provides better privacy protection while achieving high-precision IoT data fusion.

The generation, processing and distribution of multimedia data from video are increasingly toward the edge of the network with the development of mobile and industrial network. The uncertainty of user behavior and limited system resources have become a major challenge for network video services, for example, the distribution of teaching surveillance video. It is a hot spot to support network video services and content distribution with lower latency and higher bandwidth requirements by using the computing, storage, and network resources at the edge of network. In this paper, we first analyze the challenges which are faced in video distribution based on edge computing; then propose a framework for teaching surveillance video content distribution through the network, storage, and computing capabilities of edge computing; lastly provide an edge caching architecture and a cache update strategy by using a LSTM network. The experimental results demonstrate the proposed framework is more efficient than previous ones.

Silk Nanofibers In article number 2412307, Chan Zheng, Huamin Chen, Huanyu Cheng, Mingcen Weng, and co-workers present the design and fabrication of Ti3C2Tx-Ag@SNF composites with multi-dimensional heterogeneous conductive networks using combined in situ growth and vacuum filtration methods. The Ti3C2Tx-Ag@SNF composite exhibits ultrahigh electrical conductivity of 142,959 S m−1 and exceptionally high specific shielding effectiveness of 10,088 dB cm−1 for electromagnetic interference shielding, showcasing applications in human–machine interfaces with wireless control.

This study is motivated by the rising global demand for sustainable development and the increasingly important role of foreign institutional investors in shaping corporate behavior in emerging markets. It aims to investigate whether and how qualified foreign institutional investors (QFIIs) influence the Environmental, Social, Governance (ESG) performance of Chinese listed companies. Using panel data from Chinese A-share listed firms between 2009 and 2022, this study employs a two-way fixed-effects model to examine the impact of QFII shareholding on corporate ESG performance and its underlying mechanisms. The findings reveal that QFIIs significantly enhance ESG performance, primarily through promoting green technology innovation, green investment, and green expenses. Furthermore, a composite index of information transparency is developed to investigate its moderating effect, uncovering a substitution effect: QFIIs’ marginal governance impact diminishes in highly transparent firms. Notably, the mediation analysis reveals that QFIIs enhance ESG performance through multiple environmental investment pathways—green innovation, green investment, and green expenses—while the moderating effect of information transparency suggests that QFIIs exert greater influence in less transparent firms. This research advances the theoretical understanding of foreign institutional investors’ influence on sustainability in emerging markets and provides actionable insights for policymakers seeking to align foreign capital with green transition goals.