Computational Network Research Articles

Networked corporate advocacy in a polarized public arena: analyzing discourse networks of U.S. Fortune 500 companies on controversial issues

Abstract This study investigates how U.S. Fortune 500 corporate social advocacy (CSA) discourse on multiple contentious issues is associated with public attention on Twitter/X. Our theoretical framework elucidates three discourse types and conceptualizes the diversity, structure, and stability of discourse coalition networks. Utilizing computational methods and dynamic network analysis of 43,791 corporate tweets over two years, findings reveal a prevalent use of aspirational communication in addressing climate change and healthcare disparities. Functional discourse is predominantly employed in tweets on racial equality and COVID-19 vaccination, while localization discourse is concentrated in tweets about gender/sexuality. Network analysis shows that discourse coalitions exist across industries, with a convergence in discourse types and low temporal stability in network compositions. Most discourse types and features of discourse coalition networks are positively associated with public attention to CSA tweets, though these associations vary across different issues.

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains.

Trends, prospects, challenges, and security in the healthcare internet of things

The Healthcare Internet of Things (H-IoT) is a rapidly developing problem solving model with significant potential to improve patient care and healthcare outcomes. This study focuses on integrating cryptographic platforms into H-IoT systems to enable secure data access. We present insights on how to manage challenges such as cyber risks, resource constraints, latency, and energy consumption by exploring cryptographic approaches in diverse H-IoT applications. We explore important topics including big data management, blockchain, machine learning, edge computing, and software-defined networks. Additionally, we examine real-time operational challenges and emerging trends, such as remote patient monitoring and predictive analytics. Our research underscores the need for strong encryption mechanisms, access controls, device authentication, and proactive threat detection to improve the safety and efficiency of healthcare services. By combining cryptographic approaches with the architecture of the H-IoT system, this work provides the foundation for resilient healthcare infrastructures. Addressing current challenges and anticipating future opportunities contribute to strategic healthcare planning that prioritizes patient privacy and data security while anticipating future strategic healthcare planning opportunities. This article aims to provide valuable information to researchers by covering energy-efficient and resource-optimized healthcare system approaches that are integrated into the development of H-IoT systems.

HybridGNN: A Self-Supervised Graph Neural Network for Efficient Maximum Matching in Bipartite Graphs

Solving maximum matching problems in bipartite graphs is critical in fields such as computational biology and social network analysis. This study introduces HybridGNN, a novel Graph Neural Network model designed to efficiently address complex matching problems at scale. HybridGNN leverages a combination of Graph Attention Networks (GATv2), Graph SAGE (SAGEConv), and Graph Isomorphism Networks (GIN) layers to enhance computational efficiency and model performance. Through extensive ablation experiments, we identify that while the SAGEConv layer demonstrates suboptimal performance in terms of accuracy and F1-score, configurations incorporating GATv2 and GIN layers show significant improvements. Specifically, in six-layer GNN architectures, the combinations of GATv2 and GIN layers with ratios of 4:2 and 5:1 yield superior accuracy and F1-score. Therefore, we name these GNN configurations HybridGNN1 and HybridGNN2. Additionally, techniques such as mixed precision training, gradient accumulation, and Jumping Knowledge networks are integrated to further optimize performance. Evaluations on an email communication dataset reveal that HybridGNNs outperform traditional algorithms such as the Hopcroft–Karp algorithm, the Hungarian algorithm, and the Blossom/Edmonds’ algorithm, particularly for large and complex graphs. These findings highlight HybridGNN’s robust capability to solve maximum matching problems in bipartite graphs, making it a powerful tool for analyzing large-scale and intricate graph data. Furthermore, our study aligns with the goals of the Symmetry and Asymmetry Study in Graph Theory special issue by exploring the role of symmetry in bipartite graph structures. By leveraging GNNs, we address the challenges related to symmetry and asymmetry in graph properties, thereby improving the reliability and fault tolerance of complex networks.

Intelligent Logistics Warehousing Strategy Based on 5G Network and Cloud Computing in the Era of Big Data

ABSTRACTBased on the resource base of “cloud computing,” a super big data storage center was built to exchange data between companies, units, departments and businesses, management, customers and personnel, so as to ensure the timely and accurate reception of all aspects of information. Using radio frequency identification technology, barcode technology, GPS/GIS (Global Positioning System/Geographic Information System) technology, mobile communication technology, and 5G (5th Generation Mobile Communication Technology) network technology, a new business model was realized. At the same time, SOA (Service Oriented Architecture) structure was adopted to combine decentralized business and SOA architecture in the form of loose coupling, and dynamic binding was adopted to realize dynamic access of cloud logistics warehousing platform. Massive data were collected, analyzed, and preprocessed through big data technology and logistics facilities. Real‐time data transmission was realized through 5G network. At the same time, scene acquisition, modeling, analysis, control, feedback, and other technologies were applied to the logistics system to realize the intellectualization of logistics warehousing services. The receiving and sending business process of B2C (Business‐to‐Consumer) e‐commerce enterprises based on “cloud logistics warehousing” is to take the terminal shipper as the seller and the terminal consignee as the buyer. By integrating the information of all links in the logistics process into the “cloud” platform, various stakeholders can conduct information, capital, and other transactions on this platform, thus ensuring the quality of customer logistics services. The advantage of recognized logistics cloud warehouse is that its application value accounts for 43.1%. The scheme proposed in this paper can shorten the storage time of the warehouse, thus speeding up the operation of the warehouse; it can ensure the quality of each link, thus reducing the operating costs of the warehouse; it can increase the use space of the warehouse and save the manpower of the warehouse; it can improve the operation efficiency of the warehouse, thus improving the economic benefits.

Role of Computer-Aided Design and Development in Modern Pharmaceuticals

The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.

AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

AI technologies are drastically changing the backbone of telecommunications networks and bringing unheard-of security, network management, and optimization capabilities. From dynamic resource allocation to predictive maintenance and intelligent traffic management, this article shows how artificial intelligence and machine learning technologies are being used to solve important problems in modern telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that technologies are offering real-time network optimization, automatic threat identification, and improved service dependability while drastically lowering operating costs. While addressing important technical issues and operational considerations, the conversation covers pragmatic solutions across several network domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining real-world case studies and developing trends helps this article show how artificial intelligence is not only transforming present network operations but also opening the path for future telecommunications innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to changing technological demand and user expectations.

Multi-Domain Resource Management for Space–Air–Ground Integrated Sensing, Communication, and Computation Networks

Multi-Domain Resource Management for Space–Air–Ground Integrated Sensing, Communication, and Computation Networks

Tensor quantum programming

Abstract Running quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications appears daunting. In this article, we propose a novel approach called Tensor Quantum Programming, which leverages tensor networks (TNs) for hybrid quantum computing. Our key insight is that the primary challenge of algorithms based on TNs lies in their high ranks (bond dimensions). Quantum computing offers a potential solution to this challenge, as an ideal quantum computer can represent tensors with arbitrarily high ranks in contrast to classical counterparts, which indicates the way towards quantum advantage. While tensor-based vector-encoding and state-readout are known procedures, the matrix-encoding required for performing matrix-vector multiplications directly on quantum devices is much less studied. We introduce an algorithm that encodes matrix product operators into quantum circuits with a depth that scales linearly with the number of qubits. We demonstrate the algorithm’s performance for matrices commonly encountered in differential equations, optimization problems, and quantum chemistry, for systems involving up to 50 qubits.

LDM-Enhanced Computation and Communication Codesign in Unmanned Aerial Vehicle Networks

LDM-Enhanced Computation and Communication Codesign in Unmanned Aerial Vehicle Networks

Resource Management for Integrated Communications, Computing, and Sensing (ICCS) Networks

Resource Management for Integrated Communications, Computing, and Sensing (ICCS) Networks

Minimization of Task Completion Time in Wireless Powered Mobile Edge–Cloud Computing Networks

Minimization of Task Completion Time in Wireless Powered Mobile Edge–Cloud Computing Networks

Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning

Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning

Training Installation and Configuration Network Computer Simple for Students of Cipta Insani Vocational School Independent

Training installation and configuration network computer simple for students of Cipta Insani Vocational School Independent aiming For increase understanding and skills student in build as well as manage network computer . The method used in devotion This is approach practical through session theory and practice directly , where students given material about the basics network , device hard used , as well as steps installation and configuration . The results of training This show improvement knowledge student about network computer , which is measured through pre-test and post-test, as well bait come back positive from participant about involvement and implementation material . With training this , it is expected student can implement knowledge gained​ in projects at school and in the world of work in the future . The success of this program shown with activity all participants and enthusiasts participant do discussion and results questionnaire satisfaction show by 90.50% of participants satisfied with existence training This Because can increase understanding as well as ability practical student about Network Computer

Technical Guidance For Implementation Cloud Computing Technology For Support Learning Network Computers At Cipta Insani Vocational School Independent

The purpose of community service is give guidance technical about implementation cloud computing technology in support learning network computers at Cipta Insan Vocational School Independent . Activities This covering socialization , training practical , and study designed case​ For increase teacher and student understanding about cloud computing. The results of activity This show improvement significant in understanding participant about cloud computing concepts and applications , as well as integration a number of module learning with technology In addition , students​ show improvement skills practical in using cloud platforms to project network computer . Feed come back Participants also reflected mark positive from activity this , which is expected can contribute to the improvement quality education in the digital age. The success of this program shown with activity all participants and enthusiasts participant do discussion and results questionnaire satisfaction show by 90.50% of participants satisfied with existence training This Because can increase understanding as well as ability practical student about Cloud Computing.

Joint edge caching and computation offloading for heterogeneous tasks in MEC-enabled vehicular networks

Edge caching is an effective paradigm that can significantly reduce the computation task offloading latency for mobile edge computing (MEC) in vehicular networks, while also alleviating the backhaul transmission pressure for retrieving content data from the cloud server. However, most existing works fail to address how to handle heterogeneous tasks generated by vehicle terminals (VTs), especially in complex scenarios where both computation and content tasks are generated simultaneously. In this paper, we consider a mobility-aware vehicular network model where VTs simultaneously generate heterogeneous task requests, i.e., a computation task and a content task, and investigate joint optimization of caching for heterogeneous tasks data, computation offloading, and computing resource allocation. In order to optimize the latency for processing the heterogeneous tasks, an average execution latency minimization problem with sojourn time and caching capacity constraints is formulated. We decompose this problem into two tractable subproblems, i.e., caching optimization subproblem, and computation offloading and resource allocation optimization subproblem. We first develop a dynamic programming (DP) algorithm to obtain the optimal caching strategies for heterogeneous tasks data. We compare the obtained content retrieval latency with the local computing latency, and derive the optimal computation offloading and edge computing resource allocation solutions. On this basis, we propose a joint computation offloading and resource allocation (JCORA) algorithm to determine the computing resources allocated to each VT and corresponding computation offloading strategy. Numerical results indicate that the proposed algorithm, which integrates DP algorithm and JCORA algorithm, can achieve lower execution latency for heterogeneous tasks compared to the benchmark schemes. Additionally, for task loss scenarios where the sojourn time constraint cannot be met, the impact of VT mobility on the task loss probability is also revealed.

Quantum social network analysis: Methodology, implementation, challenges, and future directions

Quantum social network analysis (QSNA) is a recent advancement in the interdisciplinary field of quantum computing and social network analysis. This manuscript comprehensively reviews QSNA, emphasizing its methodologies, implementation strategies, challenges, and potential applications. It explores the conceptual foundation of key social network analysis research problems, including link prediction, influence maximization, and community detection. The research examines how quantum algorithms can revolutionize such social network tasks by leveraging principles from quantum mechanics and information theory and highlights the advantages of quantum algorithms in handling complex social network structures. The implementation section delves into the practical aspects of QSNA, such as frameworks, experimental setups, and evaluation methods. We assess the capabilities of existing quantum programming language tools and platforms. Various case studies illustrate the potential of quantum computing to enhance the performance of social network analysis. Additionally, we identify several crucial challenges and future research directions for QSNA, including the complexity of developing quantum algorithms, the need for interdisciplinary knowledge, and the challenges of integrating quantum and classical computing resources. This paper aims to serve as a foundational resource for researchers and practitioners, providing insights into the transformative potential of quantum computing in advancing the analysis of social networks and outlining future research directions in this emerging field.

Artificial neural network model for convectively heated Casson fluid with the appliance of solar energy

This study is to illustrate the thermometric exchange of Casson fluid’s double diffusive nonlinear radiative heat flux over vertical plate associated with convective boundary conditions, incorporating artificial intelligence (AI)-based neural network computation technique. The AI analysis provides enhanced and optimized results with predictive modeling. The analysis utilizes a dataset generated through Mathematica environment and then embedded the filtered matrix dataset in MATLAB employs AI analysis using the Neural Auto Regressive Exogenous (NARX) method for optimized solutions. Levenberg–Marquard algorithm is used to train neural network. The model’s importance and applications span nuclear reactors, aerodynamics, hydrodynamics, transportation and radiating processes of energy transfer. The physical problem is governed by PDEs that is transformed into a set of ODEs by using similarity transformation. The flow is analyzed against the variation in significant influencing parameter like Prandtl number (Pr), velocity ratio ([Formula: see text]), convective coefficient ([Formula: see text]), Rayleigh number (Ra), buoyancy ratio parameter ([Formula: see text]), radiation parameter ([Formula: see text]) and Casson fluid parameter ([Formula: see text]). Findings of this paper are significant for various industrial, engineering and research-based activities.

A Survey of Artificial Neural Network Computing Systems

A Survey of Artificial Neural Network Computing Systems

Output feedback control of LTI systems using quantized periodic event-triggering and 1-bit data transmission

This paper presents a novel framework for periodic event-triggered control (PETC) coupled with dynamic quantization for linear systems. Unlike traditional time-driven control methods, our approach leverages event-based mechanisms to judiciously update control actions, thus minimizing computational load and network traffic. We introduce a two-level dynamic quantizer for encoding feedback information with a single bit, thereby enhancing resource efficiency. The proposed PETC mechanism decides the transmission instants based on the quantized output samples. The resulting system is modeled as a hybrid dynamical system to capture both continuous and discrete dynamics. Sufficient conditions for ensuring the stability of the closed-loop system are presented in the form of a linear matrix inequality. Through numerical simulations, we demonstrate that our approach captures the initial output within a finite time and significantly reduces data transmissions compared to traditional methods. This paper makes key contributions in the integration of dynamic quantization with PETC, leading to resource-efficient and stable control systems.