Concept Of Network Research Articles

Network analysis: An overview for mental health research.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

SD-Meta: The Software-Defined Network of Human-Centric Metaverse for Multi-Lead or Multi-Media Data in Spread Spectrum Communications

In this paper, the novel adjacent two-end link or network multi-end link concept of software-defined network is presented to support the data transmission of electroencephalogram, audio and video streaming in spread spectrum communications. Instead of solving the problem of software-defined prioritization scheme in networking calculation and communication, the southbound-northbound structure of existing neural or information network is found in control and perceptual interactions. The proposed framework of multi-lead and multi-media structures allow the human-centric Metaverse to execute different operations for local and global planning schemes with the high-speed lead or media data transmission of spread spectrum streaming, depending on the kind of brain-computer interaction, and similar to the system of multi-modal perception in the routing or switching. Firstly, this research designed the filed programmable gate array for conventional routing nodes in the computation of different neural networks. Secondly, this study demonstrates its enhanced applicability with software core for multiple routers in the computing power network of the Internet of brains in different brain-computer smart terminals. Experiment results also show the benefits of proposed model and structure, and exposing the different running indicators in the simulation.

Review of Physical Layer Security in Integrated Satellite–Terrestrial Networks

With the success and commercialization of 5G, 3GPP has started working toward the sixth generation of communication systems. While 5G explored the concept of non-terrestrial networks like satellites and unmanned aerial vehicles working alongside terrestrial networks, 6G is expected to take this integration a step further, aiming to achieve a more coherent network where satellites and terrestrial infrastructure work together seamlessly. However, the complexity and uniqueness of such networks create numerous attack surfaces that make them vulnerable to cyberattacks. The solution to such cyberattacks can be addressed by encryption and other upper-layer authentication methods. However, with the move to higher-frequency bands, such encryption techniques are difficult to scale for low-latency networks. In addition, the recent progress in quantum computing will make networks more vulnerable. To address such challenges, physical layer security (PLS) is proposed as a secure and quantum-resistant way to implement security by taking advantage of the physics of the channel and transceiver. This article reviews the latest trends and progress in PLS in integrated satellite–terrestrial networks (ISTNs) from a signal processing perspective. This work provides a comprehensive survey of the state-of-the-art research conducted, challenges, and future directions in the PLS of ISTNs.

DC-SoC: Optimizing a Blockchain Data Dissemination Model Based on Density Clustering and Social Mechanisms

Due to its partially decentralized and highly scalable features, the consortium blockchain has currently overtaken other blockchain technologies as the one most frequently used and studied across various industries. However, performance issues such as low transaction efficiency and redundant communication processes continue to hinder the development of consortium blockchains. In the Hyperledger Fabric consortium blockchain system, transaction efficiency is largely influenced by the consensus protocol and broadcast protocol. This paper introduces a novel consortium blockchain network model, DC-SoC, focused on optimizing broadcast protocols. By incorporating the concept of density clustering, a stable propagation structure is established for the blockchain network, thus optimizing data dissemination in the Gossip protocol. Additionally, the concept of social networks is integrated, using trustworthiness scores and economic incentives to evaluate node security. The experimental results demonstrate that when DC-SoC is applied in a large-scale consortium blockchain environment, it significantly improves communication performance between nodes and ensures transmission reliability.

Latest advancements and prospects in the next-generation of Internet of Things technologies

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.

Comparison Between Gans and Diffusion Models in the Generation of Synthetic Images for Enhancing Tree Species Recognition

Objective: This study investigates the application of Generative Adversarial Networks (GANs) and Diffusion Models in data augmentation to improve the classification of tree species images, which is essential for sustainable forest management. Theoretical Framework: Fundamental concepts of machine learning, generative and classifier networks, as well as data augmentation techniques through synthetic image generation, are presented, establishing a solid foundation for the research. Method: The research utilized 2,178 images of cross-sections of wood from 18 species, applying Deep Convolutional GAN (DCGAN) and U-Net with diffusion on a rare species, evaluated using FID and IS metrics. The generated images were used to train and validate classification models, assessed by F1-score. Results and Discussion: The results revealed that Diffusion Models generated more realistic images and performed better in the classification of the rare species. The discussion contextualizes these results in light of the theoretical framework, exploring their implications for environmental management. Limitations, such as the impact of indiscriminate addition of synthetic data, are also addressed. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental management. These implications may cover areas such as sustainable innovation and biodiversity conservation. Originality/Value: This study contributes to the literature by demonstrating the potential of Diffusion Models to outperform GANs in generating synthetic images for sustainable forest management purposes. The relevance and value of this research are evidenced by its practical application in promoting innovative and sustainable practices in environmental management.

Person-centered and qualitative approaches to network analysis in physics education research

Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of whole-network cases, such as the structure of networks in single classrooms. Egocentric or personal network approaches are largely unexplored, and qualitative methods are underdeveloped. In this paper, we outline theoretical and practical differences between two major network paradigms—whole-network and egocentric—and introduce theoretical frameworks and methodological considerations for egocentric studies. We also describe qualitative and mixed-methods approaches that are currently missing from the PER literature. We identify areas where these additional network methods may be of particular interest to physics education researchers and end by discussing example cases and implications for new PER studies. Published by the American Physical Society 2024

Scene Classification of Remote Sensing Image Based on Multi-Path Reconfigurable Neural Network

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of researchers’ time, and it is often impossible to find the right model quickly. To address the issue of existing models being too large for easy selection, this paper proposes a multi-path reconfigurable network structure and takes the multi-path reconfigurable residual network (MR-ResNet) model as an example. The reconfigurable neural network model allows researchers to selectively choose the required modules and reassemble them to generate customized models by splitting the trained models and connecting them through modules with different properties. At the same time, by introducing the concept of a multi-path input network, the optimal path is selected by inputting different modules, which shortens the training time of the model and allows researchers to easily find the network model suitable for the current application scenario. A lot of training data, computational resources, and model parameter experience are saved. Three public datasets, NWPU-RESISC45, RSSCN7, and SIRI-WHU datasets, were used for the experiments. The experimental results demonstrate that the proposed model surpasses the classic residual network (ResNet) in terms of both parameters and performance.

Physics Guided Neural Networks with Knowledge Graph

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning

A heterogeneous information network, which is composed of various types of nodes and edges, has a complex structure and rich information content, and is widely used in social networks, academic networks, e-commerce, and other fields. Link prediction, as a key task to reveal the unobserved relationships in the network, is of great significance in heterogeneous information networks. This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks. This paper introduces the basic concepts of heterogeneous information networks, and the theoretical basis of representation learning, and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail. The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Enhancing Text-To-Image Translation with Stack-Generative Adversarial Networks

Text-to-image translation is the process of generating visually coherent images from textual descriptions. Traditional GANs suffer from inadequacy in capturing intricate details and therefore pose an obstacle in achieving this task. Hence, in this paper, we propose an improved approach based on the concept of Stack Generative Adversarial Networks, in which the generative process is applied in two stages. Stage I produces a 64x64 low-resolution image that offers solid shapes and colors. Then, in Stage II, this is further intensified into a resolution of 256x256 such that it closely resembles the text input. The architecture of StackGAN surpasses the earlier variant GANs and other derivatives for it produces high-resolution images with semantic correctness closely following the description in the given text. The staged nature of this approach improves image quality while ensuring that a stronger relationship lies between the produced image and the input text. Future works will attempt to enhance the model's capabilities while applying it in other areas of synthesis in images.

A Rose by Any Other Name: Mapping Taxonomic and Thematic Naming Errors Poststroke.

Understanding the neurobiology of semantic knowledge is a major goal of cognitive neuroscience. Taxonomic and thematic semantic knowledge are represented differently within the brain's conceptual networks, but the specific neural mechanisms remain unclear. Some neurobiological models propose that the anterior temporal lobe is an important hub for taxonomic knowledge, whereas the TPJ is especially involved in the representation of thematic knowledge. However, recent studies have provided divergent evidence. In this context, we investigated the neural correlates of taxonomic and thematic confrontation naming errors in 79 people with aphasia. We used three complementary lesion-symptom mapping (LSM) methods to investigate how structure and function in both spared and impaired brain regions relate to taxonomic and thematic naming errors. Voxel-based LSM mapped brain damage, activation-based LSM mapped BOLD signal in surviving tissue, and network-based LSM mapped white matter subnetwork integrity to error type. Voxel- and network-based lesion symptom mapping provided converging evidence that damage/disruption of the left mid-to-anterior temporal lobe was associated with a greater proportion of thematic naming errors. Activation-based lesion symptom mapping revealed that higher BOLD signal in the left anterior temporal lobe during an in-house naming task was associated with a greater proportion of taxonomic errors on the Philadelphia Naming Test administered outside of the scanner. A lower BOLD signal in the bilateral angular gyrus, precuneus, and right inferior frontal cortex was associated with a greater proportion of taxonomic errors. These findings provide novel evidence that damage to the anterior temporal lobe is especially related to thematic naming errors.

TFDNet: A triple focus diffusion network for object detection in urban congestion with accurate multi-scale feature fusion and real-time capability

Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To tackle these challenges, this paper introduces a novel object detection method, dubbed the triple focus diffusion network (TFDNet). Firstly, the gradient convolution is introduced to construct the C2f-EIRM module, replacing the original C2f module, thereby enhancing the network’s capacity to extract edge information. Secondly, by leveraging the concept of the Asymptotic Feature Pyramid Network on the foundation of the Path Aggregation Network, the triple focus diffusion module structure is proposed to improve the network’s ability to fuse multi-scale features. Finally, the SPPF-ELA module employs an Efficient Local Attention mechanism to integrate multi-scale information, thereby significantly reducing the impact of background noise on detection accuracy. Experiments on the VisDrone 2021 dataset reveal that the average detection accuracy of the TFDNet algorithm reached 38.4%, which represents a 6.5% improvement over the original algorithm; similarly, its mAP50:90 performance has increased by 3.7%. Furthermore, on the UAVDT dataset, the TFDNet achieved a 3.3% enhancement in performance compared to the original algorithm. TFDNet, with a processing speed of 55.4 FPS, satisfies the real-time requirements for vehicle detection.

Multi-Objective Optimization based decision-making process using Dombi Logarithmic aggregation operator in CNN environment and its application to optimally select suitable greenhouse site for tomato crops

In this research article, we have defined Dombi Logarithmic aggregation operational laws in the framework of Cylindrical Neutrosophic Numbers (CNN) and utilized these laws to establish a new aggregation operator namely Cylindrical Neutrosophic Dombi Weighted Logarithmic Aggregation Operator(C N DW L A ). The said aggregation operational laws & aggregation operator have been used to present a new and novel decision making process where Full Consistency Method (FUCOM) and Multi-Objective Optimization(MOO) have been inte- grated and embedded fruitfully. Here the objective functions were formulated using the concept of a single-layer neural network and the FUCOM method to assess criterion weights. We have resolved the most favorable Pareto optimal solution derived from Multi-Objective Optimization by employing simulation and the Technique for Or- der of Preference by Similarity to Ideal Solution (TOPSIS) approach. Finally, we have applied our proposed decision making method along with MARCOS and MOORA techniques to determine the best greenhouse site for cultivating tomato crops. An exhaustive sensitivity and comparative analysis has been conducted to assess the robustness and stability of our Multi Criteria Group Decision Making (MCGDM) techniques. It is observed that our proposed method has some advantages in cylindrical neutrosophic environment and it produces stable robust results with the variation of underlying model parameters.

On the Prototypical Core of Ancient Greek grástis, Latin grāmen and Old English græs

Abstract The present work explores the relevance of prototype semantics in determining genetic relations in Indo-European linguistics. It argues that schematisation as a constitutive process of category formation can be used as a criterion of economy and analytical coherence, when it comes to corroborating assumed phonological correspondences. Thus, it takes the terms grástis, grāmen and græs in Ancient Greek, Latin and Old English, respectively, all roughly meaning “fodder, grass”, and examines the possibility of all three stemming from a common non-propositional prototypical core. The analysis proceeds in the following way: it first tries to reconstruct a schematic prototype for the series of all meanings related genetically to each of the terms, whereas in turn it compares this prototype for each series with that of the rest. It concludes that the image schema of vigorous internal growth is the prototypical core from which all senses emanate in a conceptual network with radial structure.

End-to-End Resource Allocation Management Model in Next-Generation Network: Survey

Network communication has grown rapidly with massive demands of services. Moreover, resource allocation in networking is a fundamental and crucial issue that cannot ensure the network's stability and efficiency with the myriad requirements of different services. Various vertical businesses may seek varied network services, particularly in the Fifth-Generation networks and Beyond (5G+). The pros of Fifth-Generation communication networks are to outperform 4G in performance by having higher bandwidth, minimum latency, more capacity, and QoS (Quality of Service). Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two technologies that are combined in the next generation cellular network to provide improved network management. The primary idea behind resource allocation (RA) in the next generation network is the concept of network slicing where the network resources are virtually partitioning into many separate networks. Each separated network must satisfy the unique needs of the required service to achieve the required QoS. In this survey, we focus on resource management issues related to network slicing and tackling the biggest obstacles in this field while offering a thorough and up-to-date overview of this field. Thus, thorough analysis of the allocation of resources on the access side and core side of the network communication was sought. Also, demonstrates how revolutionary techniques that are used to support the management of sliced networks which are based on Machine Learning (ML) and Artificial Intelligence (AI). Importantly, use appropriate ML techniques such as deep learning for predicting the network condition and Reinforcement learning to learn optimal allocation policy without depending on prior knowledge and other techniques such as classification and clustering to aggregate the similar needs of users into separate slices. This could help to enhance resource utilization by allocating a sufficient amount of resource as needed based on ML algorithms and optimal utilization of resources and reducing operational costs by real-time adjustment of it based on user demands and network conditions

David Ferrier’s “complex whole”: Early traces of a “brain network” concept

ABSTRACT Currently, the idea that the brain is a complex network of interacting brain regions is hardly controversial. The rapid development of this field is often attributed to the emergence of powerful brain-imaging techniques and, around the millennium, the merging of the neuroscience of brain networks with modern mathematical graph theory. However, little is known about the historical roots of this concept. It is interesting to know when the first traces of a concept of brain networks can be found in the work of early neuroscientists, how this concept evolved over time, and what factors may have influenced this evolution. This study aims to set a first step in addressing these questions by a detailed analysis of David Ferrier’s classic study, The Functions of the Brain. From this analysis it will become clear that, in addition to a clear notion of localized functions in the brain, Ferrier speculated in several places about the need for several of these brain regions to communicate and interact in order to bring about higher brain functions. He referred to this perspective on the brain as a “complex whole,” which could be interpreted as an early precursor of the modern concept of brain networks.

ОПТИМІЗАЦІЯ ІННОВАЦІЙНИХ БІЗНЕС-ПРОЦЕСІВ В СИСТЕМІ УПРАВЛІННЯ ЛАНЦЮГАМИ ВАРТОСТІ

The article examines applied aspects of implementing the strategy of clustering the national economy based on the optimization of integrated value chains in the context of globalization challenges. The peculiarities of organizational-procedural and communicative-network support of business processes under the conditions of clustering of socio-economic systems are determined. The components of managing the processes of administration and implementation of the innovative potential of integrated production chains in the context of digital development and the format of optimization parameters were formed: intellectual capital; entrepreneurial potential; dominant technologies (level and possibilities of technology development in this area); industry localization (industry environment, market segments); information innovation infrastructure (environment of targeted integrated interaction). The evolution of the concept of integrated value chains in the context of their application in partnership activities of enterprises was considered, their mutual influence and combination, features and advantages were studied. The expediency of using the theory of digital business process management systems in business network structures and value chains is substantiated. The prospects for the use of integrated solutions are outlined, which allow creating a significant synergy of innovative network concepts, ensuring the overcoming of information asymmetry, and moving to an emergent level of business process management. Thus, the integrated value chain gradually expands from the production area to system processes in marketing, scientific and technical activities, financial support, sales process and service activities. A positive characteristic of this is the acquisition of invaluable experience in the systematic management of business processes and the implementation of innovative solutions to optimize the processes of increasing the value of enterprises in the strategic sector of the economy. Therefore, taking into account the current trends in the manifestation of innovative business processes in the digital administration system, one should possess the tools of the analog and digital management model when determining the profile of the business processes of filling the integrated value chain.

Investigating the Mediation of Powerful Entrepreneurial Networking in the Marketing Performance of Halal Smes in Indonesia Towards Achieving Sustainable Development Goals

Objective: This study introduces and analyzes a framework for Powerful Entrepreneurial Networking (PEN) to connect Entrepreneurial Orientation (EO) and Marketing Performance (MP) of halal Small and Medium Enterprises (SMEs) in Indonesia. It explores the direct impact of EO and Resource Availability (RA) on marketing performance and examines the mediating role of PEN in these relationships. The study aligns these factors with the Sustainable Development Goals (SDGs), aiming to enhance the economic and social performance of halal SMEs. Theoretical Framework: The research integrates concepts of entrepreneurial orientation, resource availability, and business networks, positing that PEN acts as a mediator, enhancing the influence of EO and RA on marketing performance. This holistic approach underscores the role of both internal capabilities and external networks in achieving business success, particularly within the halal sector. Method: A quantitative research approach was used, applying Structural Equation Modeling with Partial Least Squares (SEM-PLS). Data were collected from 354 halal-certified SMEs in North Sumatra, Indonesia, through structured questionnaires, and SEM-PLS was used to validate the research model. Results and Discussion: The findings reveal that entrepreneurial orientation and resource availability significantly improve the marketing performance of halal SMEs. Furthermore, PEN mediates these effects, enabling SMEs to leverage their entrepreneurial orientation and resources effectively, which is crucial for achieving superior marketing outcomes and contributing to the SDGs. Research Implications: The study offers practical implications for halal SMEs, suggesting the need to focus on entrepreneurial orientation, resource management, and networking to drive sustainable growth and align with SDGs. Originality/Value: This research contributes to the understanding of how PEN mediates the relationship between EO and marketing performance, providing insights into enhancing the capabilities of halal SMEs in line with SDGs.

A method for teaching Arabic to non-native speakers using the social networking site Telegram as a model

The title of this research is the method of teaching the Arabic language to non-native speakers using the social networking network Telegram as a model. It aims to identify the concept of social networking, as well as the role of social networking in teaching the Arabic language to non-native speakers. It also aims to know the goals of teaching the Arabic language to non-native speakers, and aims to know the concept of teaching methods, types of teaching methods, method of teaching the language. Arabic for non-native speakers using Telegram. To achieve these goals, we try to find answers to the following questions: What is the concept of social networking? What is the role of social networking sites Telegram in teaching the Arabic language to non-native speakers? What are the goals of teaching Arabic to non-native speakers? What is the concept of teaching methods and what are its types? What is the method of teaching Arabic to non-native speakers using Telegram, and what are the obstacles to using Telegram to teach Arabic to non-native speakers? The research adopted the descriptive approach. The research is expected to conclude that social media networks have a great role in teaching the Arabic language to non-native speakers. It is a new source in the field of education and learning, the possibility of teaching the Arabic language to non-native speakers easily, overcoming temporal and spatial boundaries, and also helping to diversify and innovate educational activities. There is There are multiple ways to teach Arabic to non-native speakers using Telegram, including: setting goals, creating a page or group, adding members and sending the join link, implementing the lesson after setting goals and choosing the content, using a recording of the teacher’s voice for a specific lesson or sending the file, books, or pictures. The educational video can be sent and students can carry it. The teacher can evaluate his lesson by asking questions orally or in writing, and he can also use poll. A call to reconsider educational systems to benefit from social media networks in teaching the Arabic language to non-native speakers; To face various challenges in the future. Holding seminars and conferences to highlight the role of using social media networks in general and Telegram in particular and its strategies in teaching the Arabic language to speakers of other languages.