Context Of Networks Research Articles

Product development the IKEA way – The role of target costing as a framing device to configure and combine resources in networks

Relying on two embedded case studies of product development within IKEA's industrial network, this paper examines the role of Target Costing as an accounting tool that has the capacity to frame development efforts into either exploitative or explorative projects. Such a framing affects, in turn, the configuration of the relevant network of resources and mediates the behavior of the actors involved via specific types of controls. We contribute to the IMP-inspired literature on innovation and product development as we add nuances to how the concepts of exploitative and explorative innovation paths play out on a network level, especially in relation to the resource dimension. In addition, the notion of the mediating role of accounting adds to how we can understand control and its consequences in an interorganizational network context. Our data consists of several sources, about 70 interviews with individuals working at IKEA and its partners. We also had access to internal company material such as costing calculations. Our study has practical implications as it can help managers identify which types of control to use and how these can be matched with different innovations strategies on a network level.

Looking for resource density in the platform supply network

In a platform supply network, a platform owner uses a digital platform to link other firms such as suppliers, customers, logistics firms and other service firms for resource combination to manage supply chains in a comprehensive manner. Resources from different providers are combined on the platform to create value for suppliers. The literature on the Service-Dominant Logic (SDL) has implied that resource density (defined as the optimal combination of resources) is key to value co-creation. Resource density is enhanced when the number and variety of resources and/or their combinability at the opportune time and place are boosted. Yet the process by which the platform owner, complementors (independent companies providing complementary services) and suppliers interact to make this happen is still unclear. This research aims to explore how these three kinds of actors interact to build a resource density situation that guarantees satisfactory value creation in a platform supply network. Based on a qualitative study of a Business-to-Business (B2B) e-commerce digital platform, we identify three mechanisms through which these companies achieve resource density. The findings demonstrate how the mechanisms for achieving resource density manifest in the platform supply network context. We reveal that resource density can be achieved at three different levels, and that artificial intelligence and digital platforms play key roles in optimizing resource density.

Representation learning of in-degree-based digraph with rich information

Network representation learning aims to map the relationship between network nodes and context nodes to a low-dimensional representation vector space. Directed network representation learning considers mapping directional of node vector. Currently, only sporadic work on direct network representation has been reported. In this work, we propose a novel algorithm that takes into account the direction of the edge with text’s attribute of the node in directed network representation learning. We then define the matrix based on in-degree of Laplacian and signless Laplacian for digraph, and it utilizes web page datasets from universities in the USA to evaluate the performance of vertex classification. We compare our algorithm with other directed representation learning algorithms. The experimental results show that our algorithm outperforms the baseline by over 20% when the training ratio ranges from 10 to 90%. We apply the in-degree-Laplacian and In-degree-signless-Laplacian to directed representation learning, which is one of the main contributions of this algorithm. Additionally, we incorporate text information through matrix completion in directed network representation learning and the experimental results show an increase in performance of up to 20% compared to the baseline, especially when the training ratio is 10%.

Network Centrality and Road Freight Efficiency: A Spatial Econometric Analysis of South Korea’s Transport Regions

Purpose - The purpose of this research is to investigate the road freight efficiency in 15 regions of South Korea using a super-efficiency SBM model and to understand the role of network centrality in influencing this efficiency. It aims to analyze how in-degree and out-degree centrality within the road freight network impacts the efficiency of road freight and how these effects manifest spatially across different regions. Design/Methodology – The study applied a super-efficiency Slacks-Based Measure (SBM) model to evaluate the efficiency of road freight across various regions. It incorporated two indices, indegree and out-degree centrality, to measure the network centrality of regions within the road freight network. Furthermore, it utilized the Spatial Durbin Model (SDM) to decompose and analyze the direct and indirect effects of network centrality on road freight efficiency, considering different spatial weight matrix. Findings – The study found that road freight efficiency in a region positively correlates with its centrality within the road transport network, indicating regions that are central to the network tend to utilize their inputs more effectively and improve transport efficiency. It also discovered positive spillover effects of network centrality, suggesting that a region’s central role in the network benefits not only itself but also adjacent regions. Originality/value - The research is valuable for its application of the super-efficiency SBM and SDM models to the context of road transport networks in South Korea, a relatively underexplored area in transportation studies. It contributes original insights into the spatial dynamics of road freight efficiency, emphasizing the role of network centrality and its variable effects across different regions.

Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding

Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not yield a consistent improvement for all instances. This is because some hidden neurons are redundant, and the noise mixed in input neurons tends to distract the model. Previous work mainly focuses on extrinsically reducing low-utility neurons by additional post- or pre-processing, such as network pruning and context selection, to avoid this problem. Beyond that, can we make the model reduce redundant parameters and suppress input noise by intrinsically enhancing the utility of each neuron? If a model can efficiently utilize neurons, no matter which neurons are ablated (disabled), the ablated submodel should perform no better than the original full model. Based on such a comparison principle between models, we propose a cross-model comparative loss for a broad range of tasks. Comparative loss is essentially a ranking loss on top of the task-specific losses of the full and ablated models, with the expectation that the task-specific loss of the full model is minimal. We demonstrate the universal effectiveness of comparative loss through extensive experiments on 14 datasets from three distinct NLU tasks based on five widely used pre-trained language models and find it particularly superior for models with few parameters or long input.

Research and Practice of Online Postgraduate Quality Management Systems in Local Institutions with the Context of Digital Network

In the digital era, there is an urgent need to reconstruct the talent cultivation system with the support of computer technology, and it is crucial to create and build a quality control system for postgraduate education that meets the development of the times. The system is usually operated online using digital network technology. Most of the postgraduates admitted to the research group come from the same level of institutions or non-graduate training authorization units, and their scientific research ability is relatively weak, hence they need more guidance and help from the university and supervisors in the cultivation process. Relying on the university's "Intelligence +" academic characteristics, the group has explored and practiced digital transformation in terms of accurate publicity, quality improvement, and differentiation cultivation, which mainly includes the following two aspects. The first, take the online teaching management platform as the core, cooperate with the enrollment platform, the practice results platform, and the graduation work platform through the network, with the user experience of teachers and students as the test standard, mine data on the habits of teachers and students, realize the orderly operation of the data-driven postgraduate education process. The second is to improve the teaching management ability and innovation consciousness of teachers under the intelligence education conditions through data platform and online or underline staged training. At the same time, the Group will continue to pay attention to data security and privacy protection issues in the postgraduate QMS, and deeply explore the integration and application of big data technology and education management, so as to improve the management level of postgraduate education and adapt to the new situation and requirements of postgraduate education.

Quantifying intragroup and intergroup connections in non-disjoint groups in social networks: A comprehensive analysis incorporating edges' and nodes' weights

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems.

Comparative Evaluation of VXLAN with Traditional Overlay Network Protocols

This article examines various network virtualization technologies, including Virtual Extensible LAN (VXLAN), as well as overlay network protocols such as VXLAN-EVPN (Ethernet VPN) and VXLAN-LISP (Locator/Identifier Separation Protocol). These protocols play a crucial role in improving the scalability and flexibility of big cloud computing infrastructures. While each of these technologies can be employed to expand a Layer 2 connection across an already established network, they possess unique qualities and applications. The objective is to offer a comprehensive comprehension of these technologies and their suitability in diverse network contexts.VXLAN-EVPN has higher performance in terms of encapsulation speed and reduced packet overhead, rendering it highly suitable for high-speed and large-scale deployments. Conversely, VXLAN-LISP demonstrates superior network latency and interoperability, offering benefits in multi-tenant and geographically distributed networks. VXLAN can be combined with other widely used overlay network protocols, including Generic Network Virtualization Encapsulation (GENEVE), Stateless Transport Tunneling (STT), and Network Virtualization using Generic Routing Encapsulation (NVGRE). The objective is to offer a comprehensive comprehension of these technologies and their suitability in diverse network contexts.

Optimizing Performance Random Forest Algorithm Using Correlation-Based Feature Selection (CFS) Method to Improve Distributed Denial of Service (DDoS) Attack Detection Accuracy

In the ever-evolving digital era, Distributed Denial of Service (DDoS) attacks have become a major threat to the security of networks and online services, making it important to develop effective strategies to detect and overcome such attacks.This research aims to improve the performance of Random Forest algorithm in dealing with DDoS attacks by using Correlation-Based Feature Selection method. This method can identify and select the most relevant features from the dataset used, in this case the CIC-DDoS2019 dataset, with respect to accuracy, precision, recall, and F1-score as evaluation metrics, so that this research achieves the best results in effectively detecting and preventing DDoS attacks, making an important contribution in strengthening the security of networks and online services.The results show that the application of the Correlation-Based Feature Selection method is able to improve DDoS attack detection in a complex network context using the Random Forest algorithm, increasing the detection accuracy rate to 99.89%. These findings highlight the potential of using the Random Forest algorithm with the CFS method in improving DDoS attack detection in complex network environments.This study recorded a significant improvement compared to the previous study, which only achieved an accuracy rate of 99.7% using the feature importance method.

Dual-promotion: Bragging better by promoting peers.

To create favorable impressions and receive credit, individuals need to share information about their past accomplishments. Broadcasting one's past accomplishments or claiming credit to demonstrate competence, however, can harm perceptions of warmth and likability. In fact, prior work has conceptualized self-promotion as a hydraulic challenge: tactics that boost perceptions along one dimension (e.g., competence) harm perceptions along other dimensions (e.g., warmth). In this work, we identify a novel approach to self-promotion: We show that by combining self-promotion with other-promotion (complimenting or giving credit to others), which we term "dual-promotion," individuals can project both warmth and competence to make better impressions on observers than they do by only self-promoting. In seven preregistered studies, including analyses of annual reports from members of Congress and experiments using social network, workplace, and political contexts (total N = 1,448), we show that individuals who engage in dual-promotion create more favorable impressions of warmth and competence than those who only engage in self-promotion. The beneficial effects of dual-promotion are robust to both competitive and noncompetitive contexts and extend to behavioral intentions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Influence of Self-Efficacy of College Students in Privacy Protection and Behavior in Social Networking Context

In the era of mobile social media, users who show themselves on social platforms will face the risk of privacy breaches. Frequent data leaks and increasingly complex social media privacy protection terms intensify users’ privacy concerns, along with mandatory privacy exposure, contributing to privacy fatigue among users. This study examines the changes in privacy protection behavior among college students under the influences of privacy concerns and privacy fatigue from the perspective of self-efficacy in privacy protection. Data is collected through a questionnaire survey, and a structural equation model is established to provide insights into protecting user privacy. The results of this research indicate that college students’ internal self-efficacy in privacy protection has a negative impact on privacy fatigue. College students’ privacy concerns are significantly negatively correlated with their willingness to disclose information, while privacy fatigue is significantly positively correlated with their willingness to disclose information. Privacy fatigue among college students has a significant positive influence on both their disclosure intention and the behavior of disengaging from privacy protection.

Exploring the imprint of the institutional context on the urban network in China: Comparative analyses between corporate‐based networks with different ownership structures

AbstractWhile the link between the urban network and institutional context has been well established, there has been less work on the effect of shifting political economy on the mechanism of its intercity flows. In this paper, the investment dataset of Chinese publicly listed firms is used to construct corporate‐based urban networks with different ownership structures. Two straightforward but effective comparative analyses are conducted. The first distinguishes between networks formed by state‐controlled and nonstate‐controlled firms, and the second distinguishes between subnetworks formed by central‐ and local‐state power. By surveying the distinctions from the perspectives of state–market relations and central‐local government relations, we argue that the urban network in China is shaped by the varied forms in which the central government, local governments, and the private sector interact. To gain a more proper knowledge of the geographical patterns, this research recommends further field research into enterprise‐based linkages of multiple ownership.

A conceptual review of the higher education system based on open innovation (OI) perspectives

The open innovation paradigm has evolved from its incipient form, namely Schumpeter’s new production function from 1912, to the current context of a global network of societies, including virtual communities that produce innovative ideas and industry applications through knowledge transfer. The role of industry-university cooperation and networks is widely recognized within the current academic approach. However, the context of higher education, its characteristics, and business application are not thoroughly explained from the Open Innovation (OI) perspective. The debate and further understanding of OI, industry-university cooperation, and the accurate application of academic knowledge to the business world and society are academics’ main concerns nowadays, our research being a beginning step. The research objective is to present the evolution of the innovation concept from the higher education perspective through a qualitative review of existing studies. The contribution of this paper is linked to the proposed higher education service life cycle framework for academics based on the current needs for innovation, accessibility, and global integration.

An Analysis of the Pragmatic Characteristics of "Jiong" in the Internet Era

With the Internet and technological developments, innovative communication methods and emerging new media have spawned rich cultural symbols in various forms. At the same time, they have created conditions for the rebirth and return of some long-dormant ancient Chinese characters, and "Jiong ()" is one of them. Previous researchers have made some progress in the evolution of the semantic meaning of "Jiong" from ancient times to the Internet era and have obtained a relatively clear timeline. However, there is still a certain research gap in the academic circle on the specific pragmatic use of "Jiong" in the Internet era and how its pragmatic use is affected by a variety of factors in the Internet era. Therefore, from the perspective of pragmatics, this study first analyzes the usages of "Jiong" in different contexts with specific examples and then uses data statistics and theoretical analysis to finally find that the pragmatics of "Jiong" are influenced by the network context, the homophone "embarrassment ()", the Chinese pinyin of which is also "Jiong", and the mass media.

Improved beamforming by accounting for multiple domain shifts and sources of degradation

A number of mechanisms are known to degrade ultrasound images. To address these issues, many beamforming strategies have been proposed including a slew of recent approaches centered around deep learning techniques. Most deep learning methods target a single type of image degradation. Here, we will simultaneously address multiple sources of image degradation within the same beamformer. In particular, we will address phase aberration, reverberation and off-axis scatting within a single network. Within this context of training networks to address multiple sources of image degradation, we will highlight the problem of domain shift in ultrasound beamforming deep networks. Domain shift is the mismatch in the statistics of the training data and the data encountered in the intended use case that reduces the performance of the trained deep network. We will show that ultrasound beamformers struggle with domain shift on both the input side and the output side of the networks, and we will present strategies for addressing both domain shifts including the shift on the output side where there is no ground truth data to learn from.

Meningeal lymphatic vessel crosstalk with central nervous system immune cells in aging and neurodegenerative diseases.

Meningeal lymphatic vessels form a relationship between the nervous system and periphery, which is relevant in both health and disease. Meningeal lymphatic vessels not only play a key role in the drainage of brain metabolites but also contribute to antigen delivery and immune cell activation. The advent of novel genomic technologies has enabled rapid progress in the characterization of myeloid and lymphoid cells and their interactions with meningeal lymphatic vessels within the central nervous system. In this review, we provide an overview of the multifaceted roles of meningeal lymphatic vessels within the context of the central nervous system immune network, highlighting recent discoveries on the immunological niche provided by meningeal lymphatic vessels. Furthermore, we delve into the mechanisms of crosstalk between meningeal lymphatic vessels and immune cells in the central nervous system under both homeostatic conditions and neurodegenerative diseases, discussing how these interactions shape the pathological outcomes. Regulation of meningeal lymphatic vessel function and structure can influence lymphatic drainage, cerebrospinal fluid-borne immune modulators, and immune cell populations in aging and neurodegenerative disorders, thereby playing a key role in shaping meningeal and brain parenchyma immunity.

Quantifying Morphostructural Evolution of the Eastern Algerian Saharan Atlas (NE, Algeria) Using GIS-based Surface Envelope Technique, Drainage Network Analysis, and Geological Features

This research work focuses on the morphostructural evolution of the eastern end of the Algerian Saharan atlas. The study employs a comprehensive methodology involving the utilization of the surface envelope technique, combined with an analysis of the hydrographic network and geological context. These techniques are integrated within a GIS framework to decipher the current relief characteristics. To interpret the current relief features, two ASTGTM v2 tiles were processed to extract morphometric data. The reconstruction of the original relief requires the generation of the summit level surface, the base level surface and the relief amount maps for 3 different grid sizes (4000m, 1000m and 250m). The respective different grid size maps has allowed determining the initial surface of the reliefs; first before any major vertical erosion, then after a phase of dismantling and finally the recent and sub current morphological aspect. The extraction of the hydrographic network and the elimination of lower than order 5 streams revealed the harmony of the latter with a raised shaped form (mega-horst) which represents the main morphostructural unit. This morphostructure is particularly characterized by outcrops from the Lower Cretaceous outcrops. The ramification of the hydrographic network along the main tectonic accidents has allowed the digging of wide valleys, often described as graben.

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

We develop a single two-layered model framework that captures and replicates both the statistical properties of the network as well as those of the intrinsic quantities of the agents. Our model framework consists of two distinct yet connected elements that were previously only studied in isolation, namely methods related to temporal network structures and those associated with money transport flows. Within this context, the network structure emerges from the first layer and its topological structure is transferred to the second layer associated with the money transactions. In this manner, we can explain how the micro-level dynamics of the agents within the network lead to the exogenous manifestation of the aggregated system statistical data en-wrapping the very same agents within the system. This is done by capturing the essential dynamics of collective motion in complex networks that enable the simultaneous emergence of tent-shaped distributions in growth rates within the agents, together with the emergence of scaling properties within the network in the study. We can validate the model framework and dynamics by applying these to the context of the real-world inter-firm trading network of firms in Japan and comparing the results of the statistical distributions at both network and agent levels in a temporal manner. In particular, we compare our results to the fundamental quantities supporting the seven empirical laws observed in data: the degree distribution, the mean degree growth rate over time, the age distribution of the firms, the preferential attachment, the sales distribution in steady states, their growth rates, their scaling relations generated by the model. We find these results to be nearly identical to the real-world data. The framework has the potential to be transformed into a forecasting tool to support decision-makers on financial and prudential policies.

Security and Privacy of 6G Wireless Communication using Fog Computing and Multi-Access Edge Computing

The challenges surrounding the confidentiality of data transmission in the context of the upcoming sixth-generation (6G) wireless networks are proposed in this research. The study explores the potential role of blockchain systems in enhancing data security. It examines the integration of machine learning (ML) techniques to address the growing complexities of handling massive data volumes within the 6G environment. This research involves a comprehensive survey of existing strategies for maintaining data confidentiality in automotive communication systems. It further investigates an analysis of confidentiality approaches inspired by the 6G network architecture. The study examines the potential security implications of the Internet of Everything (IoE). It evaluates current research issues related to safeguarding data confidentiality within the framework of 6G communication among vehicles. The exploration involves reviewing ML techniques and their applicability in resolving the data processing challenges inherent in the 6G wireless network environment. The proposed work reveals the increasing complexity and variability of the 6G wireless network environment, leading to potential challenges in protecting private and confidential data during communication. It highlights the promising role of blockchain systems in addressing data security concerns within the 6G network context. Additionally, the study underscores the transformative potential of integrating ML techniques to handle the massive data volumes generated within the 6G ecosystem. The research highlights the importance of these technologies in mitigating data security risks and ensuring the confidentiality of information exchanged within the 6G communication framework.

The effect of incentive policies on the diffusion of construction and demolition waste recycling: A government perspective.

Construction and demolition (C&D) waste recycling plays a significant role in waste reduction and carbon reduction, which is critical for sustainable development. However, due to various limitations such as financial problems, C&D waste recycling industry is not well developed in developing countries. To address this problem, this study combines complex network theory and evolutionary game theory to analyse the diffusion of C&D waste recycling behaviour among enterprises under governmental incentive policies within a complex network context. The results demonstrate that the size of the network has limited effects on behaviour diffusion in Watts-Strogatz small-world network. Additionally, the study highlights the clear impact of governmental incentive probability, initial rate and connection degree on the diffusion path. By quantitatively investigating the effects of incentive tools, this study contributes to the knowledge of C&D waste management and provides valuable implications for stakeholders seeking to promote the diffusion of C&D waste recycling.