Transfer Network Research Articles

Nano-magnetite enhances dissimilated iron reduction to vivianite from sewage by structuring an enormous and compact electron transfer network

Acting as both terminal and conductor of extracellular electron transfer (EET), little studies were focused on how nano-magnetite participated in the dissimilated iron reduction (DIR), especially the synthesis of vivianite, which was the typical DIR products from sewage. In this study, nano-magnetite was confirmed to enhance DIR of ferrihydrite and akaganeite for vivianite recovery from sewage. Nano-magnetite incorporation enriched Comamonas and Geobacter in sewage, and microbial protein content was increased by 123 % and 57 % in ferrihydrite and akaganeite batches, respectively. In Geobacter sulfurreducens PCA pure culture, vivianite yield was promoted by 21 % and 37 % in ferrihydrite and akaganeite batches in the presence of nano-magnetite, respectively. Due to its nanoscale size and superior electrical conductivity, nano-magnetite embedded in the gaps formed by the microorganisms and electron acceptor, and architected coherent conductive pathways to promote EET. Simultaneously, the addition of nano-magnetite stimulated the secretion of proteins, polysaccharides, and humic acids in the extracellular polymeric substances. Nano-magnetite addition structured an enormous and compact electron transfer network, thus enhanced DIR and vivianite formation. Our study proposed a new strategy to promote iron-reduction-coupled phosphorus recovery with natural DIR products, and provided theoretical support for clarifying the interaction between minerals and microorganisms.

Flow style-aware network for arbitrary style transfer

Researchers have recently proposed arbitrary style transfer methods based on various model frameworks. Although all of them have achieved good results, they still face the problems of insufficient stylization, artifacts and inadequate retention of content structure. In order to solve these problems, we propose a flow style-aware network (FSANet) for arbitrary style transfer, which combines a VGG network and a flow network. FSANet consists of a flow style transfer module (FSTM), a dynamic regulation attention module (DRAM), and a style feature interaction module (SFIM). The flow style transfer module uses the reversible residue block features of the flow network to create a sample feature containing the target content and style. To adapt the FSTM to VGG networks, we design the dynamic regulation attention module and exploit the sample features both at the channel and pixel levels. The style feature interaction module computes a style tensor that optimizes the fused features. Extensive qualitative and quantitative experiments demonstrate that our proposed FSANet can effectively avoid artifacts and enhance the preservation of content details while migrating style features.

Ultra-Low Alginate-Based Multifunctional Composite Binders for Enhanced Mechanical, Electrochemical, and Thermal Performance of Li-S Batteries.

The practical application of Li-S batteries, which hold great potential as energy storage devices, is impeded by various challenges, such as capacity degradation caused volume change, polysulfide shuttling, poor electrode kinetics, and safety concerns. Binder plays a crucial role in suppressing volume change of cathode side, thereby enhancing the electrochemical performance of Li-S batteries. In this research, a novel network binder (SA-Co-PEDOT) composed of sodium alginate is presented, Co2+ ions as cross-linking agent and PEDOT as an electronic conductor. The theoretical analysis and experimental testing confirm that the SA-Co-PEDOT binder with synergistic combination of catalytic center and electron transfer network effectively mitigates large volumetric changes during cycling while simultaneously enhancing electrode kinetics through controlling the deposition morphology of sulfur end product and its nucleation and dissolution. As a result, it achieves a capacity of 844 mAh g-1 after 150 cycles at 0.2 C. Moreover, the electrode with SA-Co-PEDOT binder subjected a bending test maintains a capacity of 395 mAh g-1 after 500 cycles at 0.5 C, exhibiting an impressively low decay rate of only 0.11%. Even with an ultra-low content of 2 wt.% SA-Co-PEDOT binder, the electrode still maintains a capacity of 999.7 mAh g-1 after 100 cycles at 0.5 C.

DA-GAN: Dual-attention generative adversarial networks for real-world exquisite makeup transfer

Existing makeup transfer models do not in general perform satisfactorily when applied to real-world unseen face images. Additionally, most existing models primarily focus on color distribution and struggle to transfer accurate details, particularly for elaborate artistic makeup styles. Recognizing these limitations, we propose the dual-attention generative adversarial networks, which is referred to as DA-GAN. Our work addresses the challenge of wide-ranging and exquisite makeup transfer in real-world settings. DA-GAN mainly incorporates a global attention module, a local attention module, and a makeup rendering module. The global attention module builds a coarse mapping from the reference makeup image to the source image. Based on the coarse mapping, the local attention module is designed to build a more precise mapping and preserve makeup details. The local attention module divides the entire face into a set of local patches. Based on these patches, the makeup rendering module applies an exquisite makeup style to the source image which benefits from a set of local discriminators. Extensive experiments demonstrate that DA-GAN achieves state-of-the-art results in transferring exquisite details for both ordinary and artistic makeup styles in real-world scenarios.

Research on Machining Quality Prediction Method Based on Machining Error Transfer Network and Grey Neural Network

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data.

Research on provincial implied carbon emissions in China under the shared responsibility driven by new quality productivity: A new approach

The allocation of responsibility for carbon emissions among involved parties has garnered significant attention. This study proposes a comprehensive sharing coefficient methodology known as CVAT (direct carbon emissions, value added, and technology level) to enhance the accuracy and equity of responsibility sharing driven by the new productivity standard. This study utilizes the environmental expansion multi-region input-output (EEMRIO) model to assess China's implicit provincial carbon emissions over the previous two decades. The study investigates the characteristics of the implied carbon transfer network using social network analysis (SNA) techniques. Key findings include: (1) The CVAT method's determination of responsibility sharing coefficients aligns with fairness and efficiency principles, resulting in a more equitable distribution of implied carbon emission responsibilities conducive to achieving fair carbon emission reduction. (2) Over the research period, national implied carbon emissions exhibited an upward trajectory, increasing from 3.305 billion tons in 2002 to 9.726 billion tons in 2017, with Shandong, Hebei, and Jiangsu provinces ranking among the top emitters in China. (3) Eastern coastal and western regions function as net carbon transfer out regions, while the central, northeast, and southwest regions act as net carbon transfer in regions. (4) The implied carbon transfer network demonstrated relative stability throughout the study period, with Shandong, Hebei, Jiangsu, and Inner Mongolia emerging as major carbon transfer provinces. The study's outcomes hold significant implications for the impartial and accurate assessment of carbon emission responsibilities and the advancement of global carbon emission reduction efforts.

Neural network approach to investigate heat transfer in SWCNTs nanofluid within trapezoidal cavity with varied corrugated rod amplitudes

This article presents Artificial Neural Networks (ANN) for convective heat transfer in a trapezoidal cavity subjected to corrugated heated rod inside it. The LevenbergMarquardt algorithm is utilized to optimize the Neural Networks. The trapezoidal cavity has low-temperature inclined walls and adiabatic upper and lower walls compared to the corrugated heated rod. Single-wall carbon (SWCNTs) nanomaterials are submerged in the base liquid water. The flow of SWCNTs-water is generated due to the temperature gradient in the cavity. The system of dimensional partial differential equations has been formulated for the physical setup under investigation. The dimensional system has been converted into a non-dimensional form using dimensionless variables. Finite element is used for the solutions. The dimensionless functions velocity, temperature, and heat transfer rates are studied against the Rayleigh number (Ra). The outcomes are presented in the form of isotherms, contours, tabular values, and graphs. The data for Artificial Neural Networks (ANN) has been generated by FEM against the Nusselt number. The ANN has been trained for a specific amplitude of the rod and predicted heat transfer against a larger amplitude. The results show good agreement for both training and testing data. The outcomes of analysis reveals that convection caused by temperature gradient is dominant for higher values of the Rayleigh number (Ra). Local Nusselt number has been discussed against different amplitudes, and predicted enhancement for the larger amplitude of the rod.

Do Technology Transfer Networks Impact Urban Innovation Capacity? Evidence From Chinese Cities

Do Technology Transfer Networks Impact Urban Innovation Capacity? Evidence From Chinese Cities

Cross-scale characteristics and emission reduction pathways of trade-embodied carbon flows in the global supply Chains

In the context of the 1.5 °C warming target, there is a growing awareness of the increasingly frequent carbon flows between subregions. However, understanding the characteristics and pathways of cross-scale carbon flows between subregions and regions remains elusive and impedes achieving emission reduction targets. This study employs a nested multi-regional input–output model and the structural path analysis method, establishing the characteristics of the carbon emission transfer network within global supply chains and discerning the crucial structural pathways and nodes for carbon emissions. The study found that (1) in 36 major countries, the embodied carbon flows are predominantly influenced by the developed countries around China in Asia and the Pacific. (2) In 30 subregions, coastal subregions with large-scale foreign trade and energy-intensive subregions in China's central and western parts contribute significantly to the larger embodied carbon outflow due to exports. (3) “Manufacture of non-metallic mineral products (S09) → construction (S19)” and “manufacture and processing of metals (S10) → construction (S19)” are the two key pathways of paramount significance of embodied carbon emissions. (4) “R25 South Korea” has the highest number of key pathways and the greatest embodied carbon emissions from critical paths, followed by “R27 India” and “R22 United States.” These findings offer valuable insights for the development of regional emission reduction policies.

Performance analysis of shared relay CR-NOMA network based on SWIPT

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

Workshop "Promoting the Digital Transformation in Higher Education through the Building of a Cohesive Network for Collaboration and Transfer of Knowledge and Pedagogical Innovations"

The seminar " Promoting the Digital Transformation in Higher Education through the Building of a Cohesive Network for Collaboration and Transfer of Knowledge and Pedagogical Innovations " presents the approach and concept developed by seven organisations from Spain, Bulgaria, France, Italy and Portugal in the framework of international project European Network in D-Flexible Teaching (ENID-Teach), funded by the European Commission through the Erasmus + programme, KA220-HED - Cooperation Partnerships in Higher Education, aiming to foster the intra- and inter-university cooperation in providing a coherent and stable response to the urgent need of measures and adjustments related to the digital shift and the provision of quality digital university education informed by modern technology and innovative pedagogy with greater commitment to social inclusion and equality.

A study on the spatial correlation network structure and its influencing factors of coupling coordination between FDI flow network and carbon transfer network in the belt and road initiative countries

Clarifying the spatial correlation characteristics and influencing factors of coupling coordination between Foreign Direct Investment (FDI) flow networks and carbon transfer networks in countries along the Belt and Road Initiative is of utmost importance for the formulation of regional carbon governance strategies and the establishment of a high-quality Green Silk Road. This study used a comprehensive approach combining social network analysis and coupling coordination model to measure the coupling coordination degree of FDI flow networks and carbon transfer networks of 67 Belt and Road countries from 2010 to 2016. In addition, a modified gravity model is used to characterize the spatial correlation network structure of coupling coordination between the two networks, and the QAP regression analysis method is applied to investigate the factors influencing the spatial association network. The results are as follows: 1) The spatial correlation network of coupling coordination between the two networks has good accessibility and relatively high overall network stability. 2) Countries such as Qatar and the United Arab Emirates occupy central positions in the network, while Bahrain and Jordan are positioned on the periphery of the network. 3) The spatial correlation network can be divided into three sectors: net outflow, net inflow, and bidirectional overflow sectors. 4) Spatial adjacency, bilateral investment treaties, economic development, and institutional quality have significant positive effects on the spatial association network, while the industrial structure and the level of infrastructure development have a significant negative impact. This study proposes an indicator system for the coupling coordination between FDI flow networks and carbon transfer networks. The aim is to investigate the coupling coordination relationship between FDI flow networks and carbon emission transfer networks in countries along the Belt and Road Initiative, providing important guidance for the formulation of regional cooperative carbon emission reduction strategies in other regions.

Patent output and patent transfer network in China universities: Moderating roles of absorptive capacity and desorptive capacity

Although absorptive capacity and desorptive capacity has been widely present in university-industry relationships, there has been no research on their potential impact on the relationship between university patent output and university patent transfer network. We constructed datasets of patent applications and patent transfers to firms from 383 universities between 2002 and 2021 in China, and empirically demonstrated the effect of the patent output on university patent transfer network using negative binomial regression models. We also examined the moderating effects of absorptive capacity and desorptive capacity. The results reveal that university patent output positively affects the size and the connection strength of the university patent transfer network. Technology transfer offices and satellite institutions, for desorptive capacity in universities, negatively moderate the relationship between the university patent output and the university patent transfer network, respectively. For absorptive capacity in firms, research and development intensity of new products and the level of research and development personnel input, respectively, have a positively moderate effect on the relationship between the university patent output and the university patent transfer network. Our research provides insights into the dynamics of the university patent transfer network; especially against the backdrop of a sharp increase in university patent output and insufficient development of the university patent transfer network, this study provides evidence to the substitution effect of desorptive capacity in universities and the bidirectional effect of absorptive capacity in firms.

3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows.

A temporal information transfer network approach considering federal funds rate for an interpretable asset fluctuation prediction framework

This study explores the complex interdependencies among five major financial assets—S&P 500, Bitcoin, Crude Oil, Gold, and USD/EUR—from April 2015 to September 2022, emphasizing the importance of understanding global financial dynamics for robust financial management. We quantitatively analyze daily causal relationships using conditional transfer entropy, a method that surpasses traditional correlation analyses. By incorporating the effective federal funds rate into our models, we enhance predictive accuracy and account for monetary policy impacts, ensuring our findings are relevant to current economic conditions. Our results reveal significant causal networks, providing key insights into asset interdependencies that support advanced hedging strategies and effective diversification. This research improves prediction models through the innovative use of network-based features and offers practical strategies for managing multinational financial assets, with relevance across various economic scenarios.

Cvstgan: A Controllable Generative Adversarial Network for Video Style Transfer of Chinese Painting

Cvstgan: A Controllable Generative Adversarial Network for Video Style Transfer of Chinese Painting

Novel Insights on Extracellular Electron Transfer Networks in the Desulfovibrionaceae Family: Unveiling the Potential Significance of Horizontal Gene Transfer.

Some sulfate-reducing bacteria (SRB), mainly belonging to the Desulfovibrionaceae family, have evolved the capability to conserve energy through microbial extracellular electron transfer (EET), suggesting that this process may be more widespread than previously believed. While previous evidence has shown that mobile genetic elements drive the plasticity and evolution of SRB and iron-reducing bacteria (FeRB), few have investigated the shared molecular mechanisms related to EET. To address this, we analyzed the prevalence and abundance of EET elements and how they contributed to their differentiation among 42 members of the Desulfovibrionaceae family and 23 and 59 members of Geobacteraceae and Shewanellaceae, respectively. Proteins involved in EET, such as the cytochromes PpcA and CymA, the outer membrane protein OmpJ, and the iron-sulfur cluster-binding CbcT, exhibited widespread distribution within Desulfovibrionaceae. Some of these showed modular diversification. Additional evidence revealed that horizontal gene transfer was involved in the acquiring and losing of critical genes, increasing the diversification and plasticity between the three families. The results suggest that specific EET genes were widely disseminated through horizontal transfer, where some changes reflected environmental adaptations. These findings enhance our comprehension of the evolution and distribution of proteins involved in EET processes, shedding light on their role in iron and sulfur biogeochemical cycling.

Imidazole and triazine framed porous aromatic framework with rich proton transport sites for high performance high-temperature proton exchange membranes

Phosphoric acid-doped polybenzimidazole (PA-PBI) membranes for high-temperature proton exchange membranes (HT-PEMs) exhibit low proton conductivity under low phosphoric acid loading and poor mechanical properties under high phosphoric acid loading. In order to overcome these disadvantages, a novel kind of porous aromatic framework framed with imidazole and triazine groups (PAF-227) was designed and synthesized, phosphoric acid (PA) was incorporated into PAF-227 by utilizing vacuum-assisted infusion to yield the product PAF-227-PA. Subsequently, this dopant was combined with polybenzimidazole (OPBI) to create the PAF-227-PA/OPBI composite high-temperature proton exchange membranes (HT-PEMs). The PAF-227-PA/OPBI exhibited high proton conductivity (0.24 S cm−1) at 200 °C, as well as a favorable mechanical property (6.73 MPa) and a high PA absorption rate (250.2 %), and the peak power density of the fabricated fuel cells was significantly increased to 678.21 mW cm−2 at 200 °C with the Pt loading of 0.3 mg cm−2. The basic sites of PAF-227 framed with rigid imidazole and triazine groups can anchoring the PA to achieve a high PA retention rate through the acid-basic interaction with PA, and provide sufficient proton conduction sites by forming hydrogen bonds with PA, and synergized with OPBI membranes to form a multiple hydrogen bond and proton transfer network for enhanced mechanical properties and dimensional thermal stability. Thus, this work provided a novel approach to the preparation of HT-PEMs, which exhibited excellent overall performance for HT-PEMs fuel cells.

Application of flow and heat transfer network analysis method to the core of a prismatic gas-cooled micro reactor

Application of flow and heat transfer network analysis method to the core of a prismatic gas-cooled micro reactor

Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network.

Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.