Dynamic Network Research Articles

Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning

This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions.

Colourful network: pair-bonding temporal dynamics involve sexual signals and impact reproduction

Abstract Mate choice and pair-bonding are the products of complex decisions involving repeated social interactions over time. They generally rely on multiple sexual signals and behaviours, depend on the phenotype and experiences of the chooser and are affected by environmental conditions and competition for mate access. However, studies investigating the mechanisms of mate selection often ignore the dynamic aspects of the pair formation. In the current study, we used social network analyses to (1) describe sexual interactions over time in multiple groups of captive zebra finches (Taeniopygia guttata), (2) ask how individual sexual signals and body condition relate to pair-bonding temporal dynamics and (3) investigate whether sexual networks influence assortative mating and reproductive performance. We followed sexual interactions of 8 sex-balanced groups of 8 individuals to extract social network metrics over 8 sessions of observation. We assessed individual body condition, sexual signals and couples’ pairing latency, laying date, clutch size and embryo viability. Pair-bonding dynamics were first characterized by song interactions between most individuals, then by numerous allopreening and clumping behaviours, targeting more specific partners. More colourful individuals became engaged in more sexual interactions more rapidly, and such network dynamics explained assortative mating for beak colour. Interestingly, being involved in song interactions was negatively associated with reproductive performances while the reverse was true for being involved in allopreening and clumping interactions. Our study highlights the need to study pair-bonding dynamics to better understand how variations in individual phenotype within sexual networks explain assortative mating and reproductive performance in monogamous species.

The role of electroencephalography in epilepsy research-From seizures to interictal activity and comorbidities.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and othercomorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

Crosslink strength governs yielding behavior in dynamically crosslinked hydrogels.

Yielding of dynamically crosslinked hydrogels, or the transition between a solid-like and liquid-like state, allows facile injection and utility in translational biomedical applications including delivery of therapeutic cells. Unfortunately, the time-varying nature of the transition is not well understood, nor are there design rules for understanding the effects of yielding on encapsulated cells. Here, we unveil underlying molecular mechanisms governing the yielding transition of dynamically crosslinked gels currently being researched for use in cell therapy. We demonstrate through nonlinear rheological characterization that the network dynamics of the dynamic hydrogels dictate the speed and character of their yielding transition. Rheological testing of these materials reveals unexpected elastic strain stiffening during yielding, as well as characterization of the rapidity of the yielding transition. A slower yielding speed explains enhanced protection of directly injected cells from shear forces, highlighting the importance of mechanical characterization of all phases of yield-stress biomaterials.

Covalent Dynamic DNA Networks to Translate Multiple Inputs into Programmable Outputs.

Inspired by naturally occurring protein dimerization networks, in which a set of proteins interact with each other to achieve highly complex input-output behaviors, we demonstrate here a fully synthetic DNA-based dimerization network that enables highly programmable input-output computations. Our DNA-based dimerization network consists of DNA oligonucleotide monomers modified with reactive moieties that can covalently bond with each other to form dimer outputs in an all-to-all or many-to-many fashion. By designing DNA-based input strands that can specifically sequester DNA monomers, we can control the size of the reaction network and thus fine-tune the yield of each DNA dimer output in a predictable manner. Thanks to the programmability and specificity of DNA-DNA interactions, we show that this approach can be used to control the yield of different dimer outputs using different inputs. The approach is also versatile and we demonstrate dimerization networks based on two distinct covalent reactions: thiol-disulfide and strain-promoted azide-alkyne cycloaddition (SPAAC) reactions. Finally, we show here that the DNA-based dimerization network can be used to control the yield of a functional dimer output, ultimately controlling the assembly and disassembly of DNA nanostructures. The covalent dynamic DNA networks shown here provide a way to convert multiple inputs into programmable outputs that can control a broader range of functions, including ones that mimic those of living cells.

Highly Stable Electronics Based on β-Ga2O3 for Advanced Memory Applications.

Wide-bandgap (WBG) semiconductors are at the forefront of driving innovations in electronic technology, perpetuating Moore's Law and opening up new avenues for electronic devices. Although β-Ga2O3 has attracted extensive research interest in advanced electronics, its high-temperature and high-speed volatile memory applications in harsh environment has been largely overlooked. Herein, a high-performance hexagonal boron nitride (h-BN)/β-Ga2O3 heterostructure junction field-effect transistor (HJFET) is fabricated, exhibiting an off-state current as low as ≈10 fA, a high on/off current ratio of ≈108, a low contact resistance of 5.6 Ω·mm, and an impressive field-effect electron mobility of 156 cm2 （Vs）-1. Notably, the current h-BN/β-Ga2O3 HJFET exhibits outstanding thermal reliability in the ultra-wide temperature range from 223 to 573 K, as well as long-term environmental stability in air, which confirms its inherent capability of operation in harsh environments. Moreover, the h-BN/β-Ga2O3 HJFET demonstrates successful applications for accelerator-in-memory computing fields, including dynamic random-access memory structure and neural network computations. These superior characteristics position β-Ga₂O₃-based electronics as highly promising for applications in extreme environments, with particular relevance to the automotive, aerospace, and sensor sectors.

Real-time dynamic scale-aware fusion detection network: take road damage detection as an example

Real-time dynamic scale-aware fusion detection network: take road damage detection as an example

Dual-Cure Dynamic Networks for Mechanophotopatterning of Surface Topography

Dual-Cure Dynamic Networks for Mechanophotopatterning of Surface Topography

The Huntingtin Transport Complex.

A dynamic network of scaffolding molecules, adaptor proteins, and motor proteins work together to orchestrate the movement of proteins, mRNA, and vesicular cargoes. Defects in intracellular transport can often lead to neurodegeneration. Huntingtin (HTT) is a ubiquitously expressed scaffolding protein with a multitude of cellular roles, including regulating the transport of various organelles. HTT is remarkable in its ability to regulate the transport of a wide range of cargoes, including BDNF vesicles, APP vesicles, early endosomes, autophagosomes, lysosomes, and mitochondria. This interaction network allows huntingtin to control microtubule-based transport by kinesin and dynein, as well as actin-based transport by myosin VI. By forming complexes with multiple motor adaptors, huntingtin regulates a variety of cargoes and guides cargoes through the different stages of biosynthesis, signaling, and degradation. Accordingly, pathogenic polyglutamine expansions seen in Huntington's Disease (HD) dysregulate huntingtin transport complexes, resulting in defects in transport and neurodegeneration.

Evaluating circular economy performance in the global chemical sector: a dynamic network Data Envelopment Analysis model approach

Evaluating circular economy performance in the global chemical sector: a dynamic network Data Envelopment Analysis model approach

Ubunye: An MEC Orchestration Service Based on QoE, QoS, and Service Classification Using Machine Learning

The increasing adoption of Internet of Things devices has led to a significant demand for cloud services, where latency and bandwidth play a crucial role in shaping users’ perception of network service quality. However, the use of cloud services with the desired quality is not always available to all users. Furthermore, uneven network coverage in urban and rural areas has created “digital deserts”, which are characterized by a lack of connectivity resources, complicating access to cloud services. In this scenario, edge computing emerges as a promising alternative for service provision. Edge computing leverages data processing at or near the source where it is generated rather than sending it to the cloud for processing. It can lead to several advantages, such as reduced latency and lower bandwidth usage. This paper addresses the need to ensure consistent quality of experience (QoE) and quality of service (QoS) in dynamic network environments, particularly in remote regions with limited infrastructure. We propose an orchestration service called Ubunye, which operates at the network edge and selects the most appropriate edge node to fulfill a given application request while satisfying its quality requirements. Ubunye considers factors such as latency and available bandwidth when selecting a node to execute the requested service. It implements a service classification system based on machine learning (ML) techniques. The ideal edge node is chosen through a multi-faceted evaluation, which includes current CPU load, memory availability, and other relevant parameters. Experiment results show that Ubunye effectively orchestrates resources at the network edge, enhancing QoE and QoS for services that demand low latency and high bandwidth. Additionally, it showcases the ability to classify services and allocate resources under challenging network conditions.

Securing fog computing in healthcare with a zero-trust approach and blockchain

As healthcare systems increasingly adopt fog computing to improve responsiveness and real-time data processing at the edge, significant security challenges emerge due to the decentralized architecture. The traditional perimeter-based security models are inadequate for addressing the dynamic and distributed nature of fog networks, leaving them vulnerable to unauthorized access, data tampering, and latency issues. Therefore, this paper proposes a novel security framework that integrates blockchain (BC) and software-defined network (SDN) technologies, underpinned by zero-trust (ZT) principles, to address these challenges in latency-sensitive healthcare environments. The proposed framework enhances security by combining BC’s immutable transaction logs for data integrity and traceability with SDN’s dynamic network reconfiguration for real-time access control and anomaly detection. The integration of BC and SDN supports continuous authentication and monitoring using cryptographic protocols (SHA-256A and RSA-2048) to secure data transmission. Additionally, tasks are dynamically allocated to fog nodes based on a multi-metric scheduling mechanism that considers fog node capacity, proximity, and compliance with predefined security protocols. The framework was evaluated using iFogSim, simulating a healthcare environment with 50 IoT devices, 10 fog nodes, and varying workloads (100–1000 tasks/min). The key evaluation performance metrics include intrusion detection rate (IDR), data integrity (DI), task completion rate (TCR), average task response time (ART), and average block time. The implementation results demonstrate satisfactory improvements compared to existing models: a 40% increase in IDR, a 30% enhancement in DI, a 15.29% rise in TCR, and a 39.66% reduction in ART. Moreover, the baseline IDR (85%) and DI (70%) were drawn from ZT-1, while TCR (85%) and ART (300 ms) were measured using ZT-2 as benchmarks. These findings illustrate the feasibility of integrating BC, SDN, and ZT principles to mitigate threats such as unauthorized access, data tampering, and delays in latency-sensitive tasks.

Innovative Application of 6G Network Slicing Driven by Artificial Intelligence in the Internet of Vehicles

ABSTRACTThe rapid growth of vehicle networks in the Internet of Vehicles (IoV) needs novel approaches to optimizing network resource allocation and enhancing traffic management. Sixth‐generation (6G) network slicing, when paired with artificial intelligence (AI), has enormous potential in this field. The purpose of this research is to investigate the use of AI‐driven 6G network slicing (NS) for efficient usage of resources and accurate traffic prediction in IoV systems. A unique network design is suggested, combining data‐driven approaches and dynamic network slicing. Data are acquired from vehicular sensors and traffic monitoring systems, and log transformation is used to handle exponential growth patterns like vehicle counts and congestion levels. The Fourier transform (FT) is used to extract frequency‐domain information from traffic data, which allows for the detection of periodic patterns, trends, and anomalies such as vehicle velocity and traffic density. The Dipper Throated Optimized Efficient Elman Neural Network (DTO‐EENN) is used to forecast traffic and optimize resources. This technology allows the system to predict traffic patterns and dynamically alter network slices to ensure optimal resource allocation while reducing latency. The results show that the suggested AI‐driven NS technique increases forecast accuracy and network performance while dramatically reducing congestion levels. The research indicates that AI‐driven 6G based NS offers a solid framework for optimizing IoV performance.

How does Independent Component Analysis Preprocessing Affect EEG Microstates?

Over recent years, electroencephalographic (EEG) microstates have been increasingly used to investigate, at a millisecond scale, the temporal dynamics of large-scale brain networks. By studying their topography and chronological sequence, microstates research has contributed to the understanding of the brain's functional organization at rest and its alteration in neurological or mental disorders. Artifact removal strategies, which differ from study to study, may alter microstates topographies and features, possibly reducing the generalizability and comparability of results across research groups. The aim of this work was therefore to test the reliability of the microstate extraction process and the stability of microstate features against different strategies of EEG data preprocessing with Independent Component Analysis (ICA) to remove artifacts embedded in the data. A normative resting state EEG dataset was used where subjects alternate eyes-open (EO) and eyes-closed (EC) periods. Four strategies were tested: (i) avoiding ICA preprocessing altogether, (ii) removing ocular artifacts only, (iii) removing all reliably identified physiological/non physiological artifacts, (iv) retaining only reliably identified brain ICs. Results show that skipping the removal of ocular artifacts affects the stability of microstate evaluation criteria, microstate topographies and greatly reduces the statistical power of EO/EC microstate features comparisons, however differences are not as prominent with more aggressive preprocessing. Provided a good-quality dataset is recorded, and ocular artifacts are removed, microstates topographies and features can capture brain-related physiological data and are robust to artifacts, independently of the level of preprocessing, paving the way to automatized microstate extraction pipelines.

Study on Human Factor Reliability under the Dynamic Evolution of Intelligent Lifting Construction Accidents

This research explores human factors involved in intelligent lifting construction operations risks to prevent errors and manage processes affecting risk evolution. The study employs the human factors analysis and classification system with text mining to identify risk-inducing factors from accident reports. An initial hierarchy is created from the data using the ISM technique, which is further developed into a Bayesian network diagram based on Fuzzy Dynamic Bayesian Networks. Using the approach of the fuzzy set and the developed similarity aggregation method, probabilities before or under the conditions of the network nodes are also estimated. The model’s performance is carefully verified through forward reasoning based on specific indicators, through an analysis which traces the evolution of the various paths through time, and again through sensitivity analysis. They used the FDBN-based human factor reliability risk model; the results show that the constructed risk prediction and control mechanisms help determine the significant evolutionary risk paths, optimising the management strategies for intelligent lifting construction. It presents new findings regarding outcomes of human reliability engineering applied to intelligent construction systems and presents a set of practical recommendations on safety and operations improvement.

Reaction Pathway Differentiation Enabled Fingerprinting Signal for Single Nucleotide Variant Detection.

Accurate identification of single-nucleotide variants (SNVs) is paramount for disease diagnosis. Despite the facile designof DNA hybridization probes, their limited specificity poses challenges in clinical applications. Here, a differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network is presented. DRPP leverages differences in reaction intermediate concentrations between SNV and WT groups, directing them into distinct reaction pathways. This generates a strong pulse-like signal for SNV and a weak unidirectional increase signal for wild-type (WT). Through the application of machine learning to fluorescence kinetic data analysis, the classification of SNV and WT signals is automated with an accuracy of 99.6%, significantly exceeding the 80.7% accuracy of conventional methods. Additionally, sensitivity for variant allele frequency (VAF) is enhanced down to 0.1%, representing a ten-fold improvement over conventionalapproaches. DRPP accurately identified D614G and N501Y SNVs in the S gene of SARS-CoV-2 variants in patient swab samples with accuracy over 99% (n = 82). It determined the VAF of ovarian cancer-related mutations KRAS-G12R, NRAS-G12C, and BRAF-V600E in both tissue and blood samples (n = 77), discriminating cancer patients and healthy individuals with significant difference (p <0.001). The potential integration of DRPP into clinical diagnostics, along with rapid amplification techniques, holds promise for early disease diagnostics and personalized diagnostics.

GPS TRAJECTORY CLUSTERING FOR SPATIO – TEMPORAL BEHAVIOR ANALYSIS: THE APPLICATION OF HEATMAP TECHNIQUES AND SPATIO- TEMPORAL DUAL GRAPH NEURAL NETWORK

The introduction of GPS technology has led to the creation of vast amounts of spatio-temporal data, which captures the movement patterns of different things. Efficient allocation of resources to ensure user satisfaction is a crucial factor in shaping the future of urban planning and development. It is required to comprehend the factors that can contribute to the creation of methods for studying user behaviours using a substantial number of persons within a brief timeframe. It is essential to employ appropriate clustering approaches to analyze this data in order to comprehend spatio-temporal behaviours.Heatmaps offer a graphical display of changes in density across both location and Time, making them a user-friendly tool for initial data analysis and identifying areas of high activity. The Spatio-Temporal Dynamic Graph Neural Network (ST-DGNN) utilizes graph neural networks to represent the intricate connections present in spatio-temporal data, encompassing both spatial interdependencies and temporal changes. Our methodology improves the accuracy and interpretability of trajectory clustering by integrating different methods. The suggested method has been shown to identify relevant clusters effectively and reveal noteworthy spatio-temporal characteristics through experimental analysis on real-world GPS datasets. The research utilizes a dataset comprising 182 users for analysis. Numerous measures are taken to boost the clustering accuracy of the applied techniques, including addressing missing values and outliers. Additionally, this thesis introduces a framework for time estimation based on graph-based deep learning, termed Spatio-Temporal Dual Graph Neural II Networks (STDGNN). The method entails constructing node-level and edge-level graphs that depict the adjacency connections between intersections and road segments. The results showed a number of cluster changes in each period of time dependent on move users and period; for example, the (2592) cluster of period one hour.

Rest-Activity Rhythms, Their Modulators, and Brain-Clinical Correlates in Opioid Use Disorder.

Sleep and circadian disruptions are highly prevalent in opioid use disorder (OUD) and are a barrier to successful treatment and recovery; yet few objective data are available, especially for individuals in OUD treatment with opioid agonist therapy. If disruptions remain present despite OUD treatment, this information would yield potential new targets for adjunctive therapy. To systematically investigate different aspects of rest-activity rhythms (RAR), including sleep, physical activity, circadian rhythmicity, and brain functional correlates in individuals with OUD. This cross-sectional study conducted from October 12, 2017, through January 11, 2024, recruited participants with OUD from treatment programs or the community in the District of Columbia, Maryland, and Virginia area. Participants included individuals with OUD treated with methadone or buprenorphine, individuals with OUD who remained abstinent without medications, and healthy controls (HCs). Healthy participants were recruited from advertisements. Statistical analyses were conducted between March 1 and May 31, 2024. In total, 21 RAR features were derived from 1-week actigraphy data, and principal components were used to extract independent RAR components. Modulators and brain and clinical correlates of RAR were also examined. This study included 73 participants (46 [63%] male; mean [SD] age, 43.5 [11.3] years). Among 42 patients with OUD (16 [38%] female; mean [SD] age, 42.7 [11.4] years), 33 receiving medications for opioid use disorder (MOUD) exhibited greater sleep-wake irregularity than 9 patients without MOUD (mean difference, 0.85 [95% CI, 0.00-1.69]) or 31 age- and sex-matched HCs (11 [36%] female; mean [SD] age, 44.5 [11.3] years; mean difference, 0.75 [95% CI, 0.19-1.31). Among participants receiving MOUD, greater sleep irregularity was associated with longer heroin use history (r26 = 0.45; P = .02) and lower daytime light exposure (r33 = -0.57; P < .001). Compared with HCs, participants with OUD exhibited lower fractional occupancy (percentage of occurrence) in a default mode network-dominated brain state, with individuals experiencing more pronounced sleep-wake irregularities displaying exacerbated impairments (r23 = -0.55; P = .007). Findings of this cross-sectional study showed that sleep irregularity in participants with OUD receiving opioid agonist medications correlated with years of opioid misuse and shorter daylight exposures and was associated with impaired brain state dynamics. These findings suggest that interventions increasing light exposure may improve sleep-wake irregularity and brain functional network dynamics in individuals with OUD receiving opioid agonist medications.

Link prediction in complex networks based on resource transition capacity and local paths

Link prediction aims to identify missing links within static networks or estimate the probability of emerging links in dynamic networks, representing a critical and challenging research direction in complex network. Many similarity-based methods have been established from various viewpoints, however, there are relatively few methods that take into account both the path information between node pairs and the nodes along the path. To fill this gap, we propose two novel link prediction algorithms, namely LPRA and LPH. The core idea is that the similarity between node pairs is closely related to the local paths connecting the two nodes and the resource transition capability of the nodes along those paths. The algorithms utilize the local paths with adjustable lengths between node pairs and the topological information of the nodes on the path to calculate the similarity index, which incorporate the resource transition capabilities of all the nodes on the possible paths between node pairs. We conducted multiple groups of comparative experiments on 10 real-world networks to validate the effectiveness of the proposed algorithm. Experimental results demonstrate that LPRA and LPH outperform nine classical methods and five recently popular methods. Moreover, LPRA demonstrates a significant difference level (P-value [Formula: see text]) compared to most of the methods in the ANOVA of AUC accuracy, indicating a statistically significant performance advantage of LPRA.

Annealing dynamics of regular rotor networks: Universality and its breakdown

The spin-vector Monte Carlo model is widely used as a benchmark for the classicality of quantum annealers but severely restricts the time evolution. The spin-vector Langevin (SVL) model has been proposed and tested as an alternative, closely reproducing the real-time dynamics of physical quantum annealers such as D-Wave machines in the dissipative regime. We investigate the SVL annealing dynamics of classical O(2) rotors on regular graphs, identifying universal features in the nonequilibrium dynamics when changing the range of interactions and the topology of the graph. Regular graphs with low connectance or edge density exhibit universal scaling dynamics consistent with the Kibble-Zurek mechanism, which leads to a power-law dependence of the density of defects and the residual energy as a function of the annealing time. As the interaction range is increased, the power-law scaling is suppressed, and an exponential scaling with the annealing time sets in. Our results establish a universal breakdown of the Kibble-Zurek mechanism in classical systems characterized by long-range interactions, in sharp contrast with previous findings in the quantum domain. Published by the American Physical Society 2025