Integrated Network Structure Research Articles

Ammonia-assisted Ni particle preferential deposition in Ni-Fe pyrophosphates on iron foam to improve the catalytic performance for overall water splitting

Designing efficient and cost-effective electrocatalysts for overall water splitting remains a major challenge in hydrogen production. Herein, ammonia was introduced to pyrophosphate chelating solution assisted Ni particles preferential plating on porous Fe substrate to form coral-like Ni/NiFe-Pyro electrode. The pyrophosphate with multiple complex sites can couple with nickel and iron ions to form an integrated network structure, which also consists of metallic nickel due to the introduction of ammonia. The large network structure in Ni/NiFe-Pyro significantly enhances the synergistic effect between nickel and iron and then improves the electrocatalytic performance. As a result, the coral-like Ni/NiFe-Pyro@IF exhibits good electrocatalytic activity and stability for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). The electrolyzer assembled with Ni/NiFe-Pyro@IF as cathode and anode just needs a low water-splitting voltage of 1.54 V to obtain the current density of 10 mA cm−2. Meanwhile, the stability test of Ni/NiFe-Pyro@IF is performed at the current densities ranging from 10 to 400 mA cm−2 for 50 h without any significant decay, indicating robust catalytic stability for overall water splitting. This strategy for synthesizing metal/metal pyrophosphate composites may provide a new avenue for future studies of efficient bifunctional electrocatalysts.

6G Mobile Networks: Key Technologies, Directions, and Advances

The exponential growth of the fifth-generation (5G) network gives businesses and universities a chance to turn their attention to the next-generation network. It is widely acknowledged that many IoT devices require more than 5G to send various types of data in real-time. In addition to 5G, several research centres are currently concentrating on 6G, which is expected to produce networks with great quality of service (QoS) and energy efficiency. Future application requirements will necessitate a significant upgrade in mobile network architecture. 6G technologies offer larger networks with lower latency and faster data transmission than 5G networks. This review presents a comprehensive overview of 6G networks which includes the novel architectural changes within 6G networks, recent research insights from diverse institutions, applications within the realm of 6G networks, and the key features associated with them. We also explored various technologies of 6G networks encompassing terahertz, visible light connectivity, blockchain, and symbiotic broadcasting, all of which contribute to the establishment of robust and socially integrated network structures. In this survey, we have focused on 6G network slices and discussed a detailed exploration of security and privacy concerns regarding the potential 6G technologies at the levels of physical infrastructure, connecting protocols, and service provisions, alongside an evaluation of current security strategies.

Machine-Learning-Based UAV-Assisted Agricultural Information Security Architecture and Intrusion Detection

In recent years, Unmanned Aerial Vehicle (UAV) remote sensing has developed rapidly in the field of farmland information monitoring. Real-time and accurate access to farmland information and crop growth dynamics is a prerequisite for the implementation of precision agriculture. Machine Learning identifies existing knowledge to acquire new knowledge, promotes the development of Artificial Intelligence, and brings a large number of data training sets for Machine Learning. The present work aims to ensure the safe operation of agricultural information systems and guarantee the data security of intelligent agriculture. The Machine Learning method explores the wireless network deployment of the UAV system. The geographical location deployment of agricultural information security can effectively carry out rapid security detection of agricultural information security. Firstly, the UAV-assisted information acquisition system was studied. Besides, a Double Deep Q-network (DDQN) algorithm for location deployment based on Geography Position Information (GPI) was proposed to quickly optimize the deployment location of UAVs. GPI can avoid the complicated calculation process of channel state information. The DDQN algorithm was introduced to obtain the functional relationship between GPI and the optimal UAV deployment position, forming a new GPI-Learning strategy. In addition, the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are integrated as the CNN-LSTM algorithm to build the intrusion detection system for agricultural IoT for agricultural. In the integrated network structure of the system, LSTM is responsible for data transmission, and CNN is capable of network model building. Combined with the influence of various parameters on the performance of the UAV deployment location algorithm, the simulation experiment set the population size as 36, the discovery probability as 0.25, the step scaling factor as 0.8, and the Levy flight index as 1.25. The network throughput performance of the GPI-Learning algorithm combined with Cuckoo Search was better than other algorithms under different numbers of UAVs. On the KDD-CUP99 data set, the accuracy and detection rate of the agricultural IoT intrusion detection system based on the CNN+LSTM algorithm reached 93.5% and 94.4%, respectively. In general, the agricultural IoT intrusion detection system reported here has crucial practical reference value for the safe operation of agricultural information systems.

Ameliorating structural and electrochemical properties of traditional poly-dioxolane electrolytes via integrated design of ultra-stable network for solid-state batteries

Poly(1,3-dioxolane) (PDOL)-based solid electrolytes hold great potential for solid-state lithium metal batteries (SLMBs) due to their high ionic conductivity, good lithium metal compatibility, and facile synthesis through in-situ polymerization. However, traditional PDOL electrolyte suffers from inferior structural stability and low Li-ion transference number (tLi+), which has impeded PDOL from authentic commercialization. Here we design and attain an ultrathin crosslinked polymer electrolyte (viz. PTADOL) to significantly upgrade the functional properties of PDOL. The in-situ formed PTADOL has rational O-Li+ coordination for fast Li+ transport, which enhances both ionic conductivity and tLi+. The unique integrated network structure stabilizes the electrode/electrolyte interface, and achieves additional favorable features, including improved oxidative stability, thermal stability, and flame retardancy. Based on the ultra-stable PTADOL polymer electrolyte, the high-voltage LiNi0.8Mn0.1Co0.1O2||Li solid batteries exhibit excellent operation stability with suppressed polymer degradation. This work provides not only a practical approach to the design of highly stable solid polymer electrolytes for SLMBs, but also the deep understanding of enhancement mechanism.

Hierarchical Porous and Three-Dimensional MXene/SiO2 Hybrid Aerogel through a Sol-Gel Approach for Lithium–Sulfur Batteries

A unique porous material, namely, MXene/SiO2 hybrid aerogel, with a high surface area, was prepared via sol-gel and freeze-drying methods. The hierarchical porous hybrid aerogel possesses a three-dimensional integrated network structure of SiO2 cross-link with two-dimensional MXene; it is used not only as a scaffold to prepare sulfur-based cathode material, but also as an efficient functional separator to block the polysulfides shuttle. MXene/SiO2 hybrid aerogel as sulfur carrier exhibits good electrochemical performance, such as high discharge capacities (1007 mAh g–1 at 0.1 C) and stable cycling performance (823 mA h g–1 over 200 cycles at 0.5 C). Furthermore, the battery assembled with hybrid aerogel-modified separator remains at 623 mA h g–1 over 200 cycles at 0.5 C based on the conductive porous framework and abundant functional groups in hybrid aerogel. This work might provide further impetus to explore other applications of MXene-based composite aerogel.

Global Network Analysis of Alzheimer's Disease with Minimum Spanning Trees.

A minimum spanning tree (MST) is a unique efficient network comprising the necessary connections needed to connect all regions in a network while retaining the lowest possible cost of connection weight. This study aimed to utilize functional near-infrared spectroscopy (fNIRS) to analyze brain activity in different regions and then construct MST-based regions to characterize the brain topologies of participants with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC). A 46 channel fNIRS setup was used on all participants, with correlation being calculated for each channel pair. An MST was constructed from the resulting correlation matrix, from which graph theory measures were calculated. The average number of connections within a lobe in the left versus right hemisphere was calculated to identify which lobes displayed and abnormal amount of connectivity. Compared to those in the MCI group, the AD group showed a less integrated network structure, with a higher characteristic path length, but lower leaf fraction, maximum degree, and degree divergence. The AD group also showed a higher number of connections in the frontal lobe within the left hemisphere and a lower number between hemispheric frontal lobes as compared to MCI. These results indicate a deviation in network structure and connectivity within patient groups that is consistent with the theory of dysconnectivity for AD. Additionally, the AD group showed strong correlations between the Hamilton depression rating scale and different graph metrics, suggesting a link between network organization and the recurrence of depression in AD.

Reduction, reorganization and stasis in the evolution of turtle shell elements

Abstract Novel phenotypic configurations can profoundly alter the evolutionary trajectories of species. Although innovation can precede lengthy periods of evolutionary stasis, the potential for species to diversify further can be realized via modular changes across distinct levels of hierarchical organization. To test this expectation, we undertook anatomical network analyses to model the organization and composition of the turtle’s shell. Our results suggest that stem turtles featured the greatest diversity in the number of skeletal (bones) and epidermal (scutes) shell elements. The shell subsequently underwent numerical simplification. Thus, the sum of potential connections (links) in shell networks has diminished in modern turtles. Some network system descriptors of complexity, integration and modularity covaried with the number of network components (nodes), which has remained evolutionarily stable since the Jurassic. We also demonstrated that shell reorganization might be feasible within modular subdivisions, particularly in modern turtles with simplified and less integrated network structures. We discuss how these findings align with previous studies on numerical simplification with enhanced skeletal specialization in the tetrapod skull. Altogether, our analyses expose the evolvability of the turtle’s shell and bolster the foundation for further macroevolutionary comparisons of ancient and modern species.

Synthesis of poly(2-(methacryloyloxy) ethyl ferrocene carboxylate-co-glycidyl methacrylic acid)s and their anti-migration and burning rate catalytic properties

Ferrocene (Fc)-based polymers have promising application in burning rate catalysts (BRCs) for solid composite propellants, and some of them have already been practically used. However, they have some drawbacks such as high migration during a long-time storage. Copolymers of poly(2-(methacryloyloxy) ethyl ferrocene carboxylate-co-glycidyl methacrylic acid)s (P(FHEMA-co-GMA)s) were synthesized to overcome the migration problem of Fc-based BRCs as well as to boost the burning rate (BR) of ammonium perchlorate (AP)-based propellants. The structures of the obtained P(FHEMA-co-GMA)s were confirmed by proton nuclear magnetic resonance (1H NMR) and Fourier transform infrared (FT-IR) spectroscopy. Cyclic voltammetry (CV) method was used to study their electrochemical behavior, and the P(FHEMA-co-GMA)s showed redox behavior for the existence of the Fc units. Because of the formation of integrated network structures, the P(FHEMA-co-GMA)s have good anti-migration performances. The BR catalytic property of P(FHEMA-co-GMA)s were investigated by thermogravimetry (TG) and differential thermogravimetry (DTG). They showed competent catalytic effects on the thermal decomposition of AP.

Research on data mining algorithm of logistics time series based on intelligent integrated network structure

With the continuous development of information, big data analysis has become important and dependent technical means-increasingly in various fields. By data mining through time series, the development regular of the object could be grasped, so we could predict its future development trend. Based on the intelligent integration architecture, a new algorithm of bi-weighted support vector machines (SVM) based on category weighting, and feature weighting was proposed to solve the problem of unbalanced samples in time series. In the non-balanced sample set classification, the recognition ability of the traditional classification method was low; the supported vector machine as classifier was taken in the new algorithm based on cost-sensitive learning, and different weighting coefficients to less and more samples were given, and Gauss kernel function with the weight coefficients of different features was reconstructed, thus the recognition ability of less samples was improved. In the experiment, classification accuracy, g-mean, f-measure, TP, and FP were selected as evaluation indexes, indicating that the two-weighted SVM algorithm is effective in the classification of non-balanced sample sets.

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

Content retrieval is becoming an emerging application on an integrated network structure referred to as Software Defined Network and Vehicular Ad hoc Network (SDVN). The SDVN is expected to flexibly provide efficient packet routing without deploying road side units. In such the infrastructure-less environment, however, it is challenging to retrieve contents from dynamic networks clustered with vehicles on roadways. As a solution to the problem, we propose a new network design by conjoining Named Data Network (NDN) with SDVN (NDN-SDVN). The new NDN-SDVN is designed as to improve stableness of the cluster formation and hence enables to efficiently perform the content retrieval over Vehicle-to-Vehicle (V2V) communications. Note that one of the most important problems in the V2V is how to select a cluster head. The proposed NDN-SDN adaptively determines the cluster head by using the SDN controller under the maximum Quality of Service (QoS) criterion, while reserving an assistance head based on a Moth Flame Optimization (MFO) algorithm to immediately recompose a new cluster with the back-off time. Moreover, the content retrieval in the NDN-SDVN can be performed over both intra- and inter-cluster communications. To this end, a Kalman filter with Neural Network (KF-NN) is applied for selecting a certain number of preferable vehicles in 1-hop in order to mitigate the broadcast storm problem that often occurs in an NDN. Content retrieval is initiated by sending interest packets including two additional fields of the timestamp and the target MAC address. The head vehicle of the SDN controller maintains ontology regarding the content information cashed by other vehicles, which significantly improves the packet delivery ratio. This NDN-SDVN design is developed on the OMNeT++ simulator and their advantageous results are evaluated in terms of the head lifetime, the cluster lifetime, the satisfactory rate, the latency and the packet delivery ratio.

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

The self-encoder is a typical unsupervised deep learning algorithm. In the field of unsupervised learning, it is very popular with researchers. Therefore, in view of the shortage of labeled training samples, the convolution kernel of a typical convolutional neural network is set by experience, and the network structure is fixed and it is difficult to re-learn later. This paper combines the convolutional neural network and the automatic encoder, and proposes a multi-based the method of integrated network structure to extract the features of the image for recognition. First, the SAE pre-trained CNN model convolution kernel is used to pre-train based on the classic CNN structure. Secondly, input and process image data of different scales to extract image space and spectral features respectively. Then, construct multiple channels, and use different scale filters and sampling intervals for different channels. Finally, after one layer of down sampling, the feature maps obtained from multiple channels are input into the fully connected layer, and after a hidden layer, the features finally used for classification are obtained. Experimental results show that the proposed method uses sparse automatic coding for pre-training time efficiency increased by 50%, and can further improve the recognition accuracy, the highest recognition rate reached 0.985.

The emergent integrated network structure of scientific research.

Scientific research is often thought of as being conducted by individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex and integrated system of people, papers, and ideas. Studies of co-authorship and citation networks have revealed important structural properties of researchers and articles, but currently the structure of scientific ideas themselves is not well understood. In this study, we posit that topic networks may be a useful framework for revealing the nature of conceptual relationships. Using this framework, we map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000, constructing networks in which nodes represent topics of study and edges give the extent to which topics occur in the same papers. The network displays small-world architecture, characterized by regions of dense local connectivity with sparse connectivity between them. In this network, dense local connectivity additionally gives rise to distinct clusters of related topics. Yet notably, these clusters tend not to align with assigned article classifications, and instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define two measures of interdisciplinarity, one of which is found to be positively associated with PNAS’s impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight.

An energy internet-based distribution system morphology

The future power distribution system will play an important role in the future energy system - energy Internet. With an eye to the diversification of energy and power fusion to the distribution system form, the prospect of the future integrated power distribution system for energy Internet is proposed, from the three aspects of structure, equipment form and industrial form: a multi-level AC-DC ring network as a framework, including the P2P interconnection and DC information satisfied power grid integrated network structure, is given; the concept of energy exchanger, the concept of energy server is put forward, and on this basis, the future distribution system of the equipment is prospected; the future distribution of the three types of industrial forms is analysed.

Integrated structure investigation in complex networks by label propagation

The investigation of network structure has important significance to understand the functions of various complex networks. The communities with hierarchical and overlapping structures and the special nodes like hubs and outliers are all common structure features to the networks. Network structure investigation has attracted considerable research effort recently. However, existing studies have only partially explored the structure features. In this paper, a label propagation based integrated network structure investigation algorithm (LINSIA) is proposed. The main novelty here is that LINSIA can uncover hierarchical and overlapping communities, as well as hubs and outliers. Moreover, LINSIA can provide insight into the label propagation mechanism and propose a parameter-free solution that requires no prior knowledge. In addition, LINSIA can give out a soft-partitioning result and depict the degree of overlapping nodes belonging to each relevant community. The proposed algorithm is validated on various synthetic and real-world networks. Experimental results demonstrate that the algorithm outperforms several state-of-the-art methods.

English

Aunifiedfiber-to-the-homes (FTTHs) and wireless sensor network (WSN) provides a profitable solution to build up an immaculate ubiquitous-City (U-city). The key objects of actual convergence of FTTH and WSN are less computational intricacy for data packet processing, little installation cost, and good quality of services. In this paper, we familiarise an integrated network structure of multi-optical line terminal (multi-OLT) passive optical network (PON) which can lodge multiple service workers in a single PON. A modified version of interleaved polling algorithm is proposed for scheduling of control messages from multiple OLTs in a single network. We also provide thorough mathematical analysis of cycle time variation, succeeding grant scheduling time, and average packet interruption for both uniform and non-uniform traffic loads generated by each ONU, using fixed service bandwidth provision scheme and limited service bandwidth sharing scheme. We also compare the throughput of the anticipated scheme with existing single-OLT PON for non-uniform traffic load using limited provision bandwidth allocation scheme. The replication results show that the proposed multi-OLT PON system can ropes prevailing bandwidth allocation schemes with better routine than the single-OLT PON in terms of average packet delay, bandwidth consumption, and throughput.

Influence of network structure on the viscoelastic properties of dynamically vulcanized rubber/plastic blends: An alternative approach to understand the microstructure evolution

The formation of finely dispersed micron-sized cross-linked rubber agglomerates in the thermoplastic matrix during dynamic vulcanization is now a well accepted theory to explicate the final properties of thermoplastic vulcanizates (TPVs). Based on our previous results, we have investigated further in the present work on the most influential and essential parameters which controls the ultimate properties of the TPVs. Three TPVs based on poly[styrene-b-(ethylene-co-butylene)-b-styrene] triblock copolymer (S-EB-S) and solution polymerized styrene butadiene rubber (S-SBR) have been prepared containing different proportions of rubber fraction. A semi-efficient (SEV) sulphur based curing system has been adopted to cross-link the rubber phase and advanced microscopic techniques viz. transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) have been used for the microstructure analysis. Thereafter, dynamic experiments have been performed to correlate the morphological observations with viscoelastic properties. The experimental results and the morphological images confirm that the network structure formation during dynamic vulcanization and its integrity is the most influential parameter to cause the utmost properties of the TPVs. The finely dispersed cross-linked rubber particles obtained during dynamic vulcanization are actually the disintegrated and agglomerated rubber nano-particles having average particle size between 80 and 85nm. It has also been confirmed that the integrated rubber network structure has an inverse relationship with the proportion of rubber fraction present in the TPVs. Mechanical properties, melt rheology and dynamic viscoelastic measurements also support the network structure disruption and disintegration observed in the morphological images and thus, nullifies the supremacy of dispersed phase morphology theory behind the superior properties obtained from the TPVs. This work elucidates the necessity and importance of integrated network structure formation over the morphology evolution during dynamic vulcanization and leads to a new avenue to understand morphology–mechanical–rheological–viscoelastic property correlation in TPVs.

A New Scheme for Network Selection in Heterogeneous Wireless Network using Fuzzy Logic

ABSTRACT Ensuring seamless and reliable switching across different constituent technologies is the basic requirement of heterogeneous wireless networks. IEEE 802.21 standard is recommended for 4G networks for coordinating among different mobile network for better services. Still, there exists a scope of further improving its performance by introducing the fuzzy logic concepts in it. The present work aims in this direction where a fuzzy interface system is designed using four different parameters viz. speed of mobile terminal, available bandwidth, received signal strength and number of users. The simulation result show that the number of vertical handoffs in the proposed technique reduced by 27% as compared to the existing fuzzy approach and by 42% as compared to the classical approach. Keywords Heterogeneous networks, seamless mobility, fuzzy logic, handoff. 1. INTRODUCTION The heterogeneous wireless network is a collection of different wireless technologies like cellular networks, wireless metropolitan area network (WiMax) and wireless local area network (WiFi). Besides traditional handoff (between cells of same technology), a vertical handoff (between cells of different technology) is the primary requirement for the proper functioning of heterogeneous networks. The most desiring features are to maintain best quality of service (QOS) by connecting to most fascinating network among the available networks. Figure 1 shows the integrated network structure of cellular networks, Wi-Fi and WiMax. In WiFi or WiMax the point of attachment through which a user gets connected is called an access point and in cellular network is termed as base station. The three basic steps in the vertical handover process are network discovery, handover decision and handover execution process [1]. In the traditional cellular system also known as GSM, the threshold comparison of different specific parameter metrics is used to make handover decisions [2]. The inter-system handovers are not only supposed to maintain ongoing connections but also to provide users with better services and to meet their individual requirements. In contrast to horizontal handover, vertical handover considers not only the received signal strength (RSS) but also the service class Mapping between handoff-in and handoff-out networks [3].The IEEE 802.21 standard defines the basic requirement for heterogeneous mobility. Quality of service is measured with the performance of transmission of data, network technologies and service accuracy [4]. The idea is to provide good connectivity with different network, reduction in call dropping and good quality of service. A cell phone call may be interrupted or drop after an unnecessary handover or incorrect handover. An unnecessary handover decision can degrade the quality of service and can also interrupt the ongoing communication. So it is essential that the handover process execute seamlessly and accurately to improve the quality of service. The multimedia applications like video streaming, VOIP, web browsing requires good quality of service. Therefore it is mandatory to indicate the quality of a vertical handover network selection and the network performance. In the vertical handover quality can be define as reduction in ping-pong effect, reduction in interruption in the ongoing call during handover and enhancing the throughput of the system [5].The scheme presented in this work shows better performance in this regard.

Determination of temperate bird–flower interactions as entangled mutualistic and antagonistic sub‐networks: characterization at the network and species levels

Most network studies on biological interactions consider only a single interaction type. However, individual species are simultaneously positioned in various types of interactions. The ways in which different network types are merged and entangled, and the variations in network structures between different sympatric networks, require full elucidation. Incorporating interaction types and disentangling complex networks is crucial, because the integration of various network architectures has the potential to alter the stability and co-evolutionary dynamics of the whole network. To reveal how different types of interaction networks are entangled, we focused on the interaction between birds and flowers of temperate plants in Japan, where flower-feeding birds are mainly generalist passerines, acting as pollinators and predators of flowers. Using long-term monitoring data, we investigated the flower-feeding episodes of birds. We constructed the whole network (WN) between birds and plants, separating the network into mutualistic and antagonistic sub-networks (MS and AS, respectively). We investigated structural properties of the three quantified networks and species-level characteristics of the main bird species. For bird species, we evaluated dietary similarity, dietary specialization and shifts of feeding behaviour relative to plant traits. Our results indicate that WN comprises entangled MS and AS, sharing considerable proportions of bird and plant assemblages. We observed distinctive differences in the network structural properties between the two sub-networks. In comparison with AS, MS had lower numbers of bird and plant species, showed lower specialization and modularity and exhibited higher nestedness. At the species level, the Japanese white-eye acted as pollinator, while the brown-eared bulbul acted as both pollinator and predator for large numbers of flowers, based on its behavioural plasticity. Overall, the pattern of avian feeding behaviour was influenced by flower size and plant origin. Birds showed nectarivory for plants with medium-sized flowers and exotic origins. Our results highlight the complex patterns of interactions between birds and the flowers of plants in temperate regions. They also indicate that understanding the interaction type for each species pair and consideration of the behavioural plasticity of animal species are important for elucidating integrated network structures.

Academic Collaborative Centre Limburg: A platform for knowledge transfer and exchange in public health policy, research and practice?

Research findings often fail to find their way into policy and practice, which is assumed to limit the effectiveness of public health policies and programmes. We evaluated to what extent the Academic Collaborative Centre Limburg (ACCL), a Dutch boundary organization linking policy, research and practice, has improved knowledge transfer and exchange between the three domains.We used a mixed-methods approach. First, stakeholders jointly defined the ACCL's programme theory, showing how the ACCL was supposed to achieve its intended effects. Second, we assessed the achievements of the ACCL in terms of knowledge transfer and exchange on the basis of the programme theory.The ACCL was found to provide a platform for interaction between actors from the policy, research and practice domains, facilitated by integrated network structures. The number of collaborative projects and actors involved in the ACCL increased, but actual cross-domain interaction patterns did not really change. Cross-domain knowledge transfer and exchange still require major boundary-spanning efforts by the ACCL programme leader.Boundary organizations do not automatically produce cross-domain interactions. In addition to infrastructural arrangements, cross-domain knowledge transfer and exchange could benefit from additional cultural changes, like adopting a deliberative approach to policy making and applying constructivist research designs.

Accounting Firms: Global Spread with Limited Transnationalization

As financial markets have increasingly globalised, the regulatory framework is still nationally fragmented. One core regulatory element is the provision of assurance services by accounting firms. The Big 4 – the leading international providers of audit services – are not as international as are accounting standards or their clients. The paper traces three facets of the transnationalization of accounting firms: explanations for internationalization rooted in the theory of the firm, their modes of internal organization and limits to further transnationalization. Accounting firms are professional service firms with low fixed and high intangible assets. This limits the theories that can explain the internationalization of firms. We examine which theories are the most promising to explain the internationalization of the Big 4. Regarding their internal structures, our empirical analysis suggests that Big 4 accounting firms aspire to develop an integrated network structure which is known from the transnational corporation of the Bartlett and Ghoshal typology. This becomes particularly manifest in international staff exchange. Accounting firms are also deeply embedded in the regulatory framework. Its national fragmentation sets limits to further globalisation of assurance services.