Network System Research Articles

Carbon capture and co-pollutants in a networked power system

Abstract We evaluate how the availability of Carbon Capture (CC) in a networked electricity system affects the emissions of both carbon and of co-pollutants, under a range of plausible technical, economic, and policy scenarios about CC technology, the pace of renewable deployment, the structure of the power grid, and climate policy. We employ a Power Flow model of a three-node, mixed-source network in which fossil fuel power plants may invest in CC via retrofit. Our stylized model retains some of the complexities of a real power system while allowing for a detailed analysis of the impact of power plant operations and transmission constraints. We find that, in a networked system, the availability of CC may lead some generation to move from Natural Gas to Coal, thus leading to a significant increase in co-pollutants. This is of particular concern during the mid-transition, a period when both carbon and non-carbon electrical generation is active. The introduction of CC can lead to an increase in co-pollution even as the energy system transitions toward renewable energy and, surprisingly, co-pollution outcomes can be worse under a stronger decarbonization policy. This insight is important and timely in light of recent rules incentivizing the use of CC. Systems in the early stages of the energy transition may experience an increase in co-pollution if the co-pollutant dynamics are not considered in the first steps of CC policy design.

A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks

Recently, Sahoo et al. introduced a three-factor authentication scheme for Wireless Sensor Networks (WSNs) based on an elliptic curve cryptosystem. Nonetheless, upon closer examination, we have identified critical vulnerabilities in their scheme, including susceptibility to user impersonation, gateway impersonation, sensor node impersonation attacks, and a breach in the three-factor security aspect. Further, the scheme fails to withstand offline sensor node identity guessing attacks, man-in-the-middle attacks, and known session-specific temporary information attacks. Intending to elevate both security and efficiency, we propose a novel three-factor authentication scheme that capitalizes on the strengths of a fuzzy extractor and a cryptographic one-way hash function. The proposed scheme’s security has been rigorously assessed using the SCYTHER tool, confirming its validity under the real-or-random (ROR) model. Moreover, a heuristic analysis exemplifies that the scheme effectively withstands various known cryptographic attacks. Consequently, the performance comparisons establish the superiority of our scheme over related approaches in terms of security and efficiency. Additionally, its suitability for WSNs is evident due to the minimal overhead on the sensor nodes, making it a highly promising solution for real-world implementation.

Dynamic sum-based event-triggered H∞ filtering for networked T-S fuzzy wind turbine systems with deception attacks

This article is concerned with the dynamic sum-based event-triggered H∞ filtering issue for networked wind turbine systems subject to deception attacks and communication delays. By considering the time-varying wind power case rather than the existing maximum power case, a more general T-S fuzzy system with two premise variables is modeled for nonlinear wind turbine system. In order to save the communication cost, a novel dynamic sum-based event-triggered scheme is proposed, which has the following three merits. First, some past sampled measurements are utilized to reduce redundant transmissions. Second, an auxiliary dynamic variable based on the past sampled measurements is introduced in the triggering condition to further enlarge the triggering intervals. Third, a dynamic triggering threshold is designed, which can be regulated adaptively along with the system evolution. With the help of Lyapunov method and linear matrix inequality technique, some sufficient co-design conditions of H∞ filter and triggering matrices are derived for the T-S fuzzy filtering error system with deception attacks and communication delays. Lastly, some simulations are carried out to illustrate the advantages of the proposed strategy.

Study the effect of different concentrations of polydopamine as a secure and bioactive crosslinker on dual crosslinking of oxidized alginate and gelatin wound dressings

Alginate hydrogels are commonly used in wound care due to their ability to maintain a moist environment, absorb fluids, and aid wound healing. However, their stability and mechanical properties can sometimes limit their effectiveness. This study explores a new approach by creating a dual network system of oxidized alginate and gelatin hydrogel crosslinked with polydopamine in a single step, with the goal of improving the mechanical properties of these hydrogels. The unique aspect of this research is the comprehensive examination of different polydopamine concentrations in dual crosslinking systems. First, alginate was modified with sodium periodate to create additional active groups on its backbone, and various polydopamine concentrations were then tested to assess their impact on the dual crosslinking network and hydrogel properties. The study involved a range of tests, including FTIR, H-NMR, SEM, gelation time, rheology, adhesion, antioxidant activity, swelling ratio, weight loss, drug release, and cell viability. The addition of polydopamine was found to enhance the crosslinking density (0.859 × 109 mol.cm−3). Additionally, the results indicated improvements in properties such as reduced weight loss, enhanced antioxidant and adhesive qualities, and better mechanical properties (2240 kPa). However, the optimal concentration of polydopamine must be determined to achieve the best properties for a wound dressing. Excessive polydopamine can increase the space between polymer chains, leading to a reduction in crosslinking density and storage modulus. Nevertheless, it can also increase the swelling ratio, degradation rate, pore size, porosity, antioxidant activity, and dopamine release. Therefore, identifying the optimal concentration for a functional hydrogel is crucial. Notably, the hydrogel containing 0.5 mg.mL−1 polydopamine exhibited outstanding cell viability (108 % on the third day), swelling capacity (480 %), storage modulus (2240 kPa), gelation time (3 min), antioxidant activity (42.27 %), and skin adherence (11 kPa), making it an optimal choice for advanced wound management. According to the findings, it is emphasized that the application of this particular hydrogel expedites wound healing, as indicated by wound closure and histological studies. AbbreviationsUnlabelled TableOALoxidized alginateGELgelatinPDApolydopamineEDCsodium meta periodate 1-ethyl-3-(3-dimethyl aminopropyl) carbodiimide hydrochlorideNHSN-hydroxysuccinimide

Digital Twin and federated learning enabled cyberthreat detection system for IoT networks

The widespread deployment of Internet of Things (IoT) devices across various smart city applications presents significant security challenges, increased by the rapidly evolving landscape of cyber threats. Traditional security solutions, including those using Federated Learning with federated averaging, suffer from inefficiencies due to random node selection and partial data sampling, which can hinder the detection of comprehensive network-wide attacks. This paper introduces a novel Cyberthreat Detection System for IoT networks that leverages Digital Twin technology and an optimized Federated Learning approach. Our hypothesis implies integrating Digital Twin models within an IoT security framework to improve real-time cyberthreat detection capabilities. We implement a 'Adaptive Thresholding with Early Stopping method' based methodology in Federated Learning to systematically train and aggregate local models based on predefined training rounds, thereby ensuring that all local models contribute to the global model until a target accuracy is achieved. This method significantly improves the detection of zero-day attacks by reducing dependency on random selections and partial data. The system architecture features Digital Twins of IoT medical infrastructure components—such as radiology, intensive care, and outpatient care—positioned at the network edge to minimize latency and bandwidth usage. Comparative evaluations of our model against traditional federated averaging methods demonstrate superior performance, with enhancements in model aggregation efficiency evidenced by higher F1 scores and reduced CPU usage. Specifically, our distributed digital twin environment at the edge layer shows 14% and 33% latency reductions compared to fog and cloud-based implementations, respectively. This study highlights the potential of Digital Twin and advanced Federated Learning methodologies to secure IoT networks against evolving and growing cyber threats.

Atmospheric density estimation in very low Earth orbit based on nanosatellite measurement data using machine learning

The atmospheric density in the very low Earth orbit (VLEO), the lower region of the thermosphere is a critical factor for satellite missions on prediction of orbit and lifetime. However, accurately determining atmospheric density remains challenging owing to difficulties in acquiring data within the VLEO. The re-Entry satellite with Gossamer aeroshell and GPS/Iridium (EGG) nanosatellite mission, conducted in 2017, obtained sparse coordinate data through the iridium network and Global Navigation Satellite System (GNSS). Using these data, we developed a methodology based on machine learning and special perturbations to estimate atmospheric density. The originally obtained atmospheric density predicted by NRLMSISE-00 was corrected during trajectory reproduction from GNSS data by special perturbation. The prediction performance of the method was validated using the generated trajectory data. Subsequently, we estimated the density at altitudes of approximately 200 km, particularly in the lower region of the VLEO. Density values that accurately reproduced trajectories of the EGG were 0.5 to 0.64 times greater than that predicted by the NRLMSISE-00 model. This methodology is effective for measuring atmospheric density in the lower VLEO region using GNSS data from low-cost and high-frequency nanosatellite missions. By increasing the availability of accurate atmospheric density data, this methodology enhances our understanding of the atmosphere in the VLEO.

Design and application of fuzzy neural network systems optimized with hybrid algorithms

Design and application of fuzzy neural network systems optimized with hybrid algorithms

Enhanced Energy Transfer Efficiency for IoT-Enabled Cyber-Physical Systems in 6G Edge Networks with WPT-MIMO-NOMA

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

Dynamic RIS partitioning in NOMA systems using deep reinforcement learning

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

Abstract September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

A covert attack detection strategy combining physical dynamics and effective features-based stacked transformer for the networked robot systems

A covert attack detection strategy combining physical dynamics and effective features-based stacked transformer for the networked robot systems

A Preliminary Study on Factors That Drive Patient Variability in Human Subcutaneous Adipose Tissues.

Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients, which included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25), and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loadings in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue, with lipid deposition being favored extracellularly, an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, which is highly correlated with leptin signaling, was correlated with metabolic inflammation. Furthermore, this study draws attention to what we define as "extracellular lipid droplets", which were consistently found in collagen-rich regions of the obese adipose tissues evaluated here. Reduced levels of TGIF1 were correlated with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Finally, this study indicated that M1 and M2 macrophage markers were correlated with each other and leptin in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients.

Exploration of a brain-inspired photon reservoir computing network based on quantum-dot spin-VCSELs.

Based on small-world network theory, we have developed a brain-inspired photonic reservoir computing (RC) network system utilizing quantum dot spin-vertical-cavity surface-emitting lasers (QD spin-VCSELs) and formulated a comprehensive theoretical model for it. This innovative network system comprises input layers, a reservoir network layer, and output layers. The reservoir network layer features four distinct reservoir modules that are asymmetrically coupled. Each module is represented by a QD spin-VCSEL, characterized by optical feedback and optical injection. Within these modules, four chaotic polarization components, emitted from both the ground and excited states of the QD Spin-VCSEL, form four distinct reservoirs through a process of asymmetric coupling. Moreover, these components, when emitted by the ground and excited states of a driving QD spin-VCSEL within a specific parameter space, act as targets for prediction. Delving further, we investigated the correlation between various system parameters, such as the sampling period, the interval between virtual nodes, the strengths of optical injection and feedback, frequency detuning, and the predictive accuracy of each module's four photonic RCs concerning the four designated predictive targets. We also examined how these parameters influence the memory storage capabilities of the four photonics RCs within each module. Our findings indicate that when a module receives coupling injections from more than two other modules, and an RC within this module is also subject to coupling injections from over two other RCs, the system displays reduced predictive errors and enhanced memory storage capacities when the system parameters are fixed. Namely, the superior performance of the reservoir module in predictive accuracy and memory capacities follows from its complex interaction with multiple light injections and coupling injections, with its three various PCs benefiting from three, two, and one coupling injections respectively. Conversely, variations in optical injection and feedback strength, as well as frequency detuning, introduce only marginal fluctuations in the predictive errors across the four photonics RCs within each module and exert minimal impact on the memory storage capacity of individual photonics RCs within the modules. Our investigated results contribute to the development of photonic reservoir computing towards fast response biological neural networks.

Robust power converters for renewable generation systems in future networks with short circuit ratio wide variations

The European Directive (EU) 2018/2001 requests the connection of a new renewable inverter-based resources (RIR) but ensuring that the connection point is strong enough to minimize and avoid possible interaction phenomena. Historically, the indicator used to determine the strength of a network has been the Short Circuit Ratio (SCR). Until now, the tendency had been to ensure that the network remained strong enough with High SCR, but another possibility is to ensure that RIR can operate stably and reliably at different SCR values, both high and low. The inverters currently deployed in European power systems use grid following inverter technology (GFI). The main objective of the present work is the redesign of the traditional current regulators of JEMA Energy PV family inverters, based on grid following technology, to guarantee the stability of electrical systems with a variable short-circuit ratio in the range [1÷20]. To achieve this, the current loops of the inverter are redesigned, introducing an additional first-order controller that improves the dynamics of the system and allows stable and efficient behaviour. For validation purposes, the system is analysed under adverse fault scenarios. The results obtained in all cases are satisfactory, but the stability with PLL (phase lock loop) slower than those originally used, is better.

MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task

Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task based on a new generative perspective. To address the challenges of ineffective numerical embedding, flexible table formats, and dense medical terminology and numerical entities in an end-to-end manner, we present the innovative medical text-to-table model called MedT2T. This model, built on the BART backbone, operates in an end-to-end manner and comprises three essential modules: Encoder, Decoder, and Adapter. The Encoder utilizes an innovative adaptive medical numerical constraint to facilitate precise embedding and generation of medical numerical data. The generated output of the Decoder adheres to relational constraints and table formats, ensuring the desired structure and organization. Additionally, the Adapter incorporates an adaptive pointer generation mechanism, allowing for dynamic referencing of medical terminology and numerical information either from the source text or generated through the vocabulary distribution of the Decoder. Our method outperforms existing baselines in terms of exact match, character level match, and BERTScore. We also proved that MedT2T can serve as an essential table extraction tool to bring informative gains for medical downstream classifiers and predictors. This study not only achieved accurate entity generation for tables from lengthy medical texts to improve physician efficiency in accessing critical information for decision-making, but also provided large-scale structured training table data for downstream tasks such as AI-driven smart healthcare.

H∞ stabilization of singular networked cascade control systems subject to periodic DoS attack

In this paper, a [Formula: see text] controller with an event-triggered mechanism is designed for a singular networked cascade control system (SNCCS) under periodic denial-of-service (DoS) attacks. The SNCCS is treated as a system of differential-algebraic equations (DAEs) with an index of two and is modeled as a switched system. The paper first establishes sufficient stability conditions, which are formulated as linear matrix inequalities based on multiple Lyapunov functions. These conditions ensure that the system remains regular, free from impulse modes, and stable. Next, state feedback controllers are designed for both the inner loop and the outer loop. However, the outer loop is subjected to periodic DoS cyber-attacks. Finally, the proposed method’s effectiveness is demonstrated through simulation results on a practical thermal cascade control system.

Synergy of Target-Induced Magnetic Network and Single-Drop Chromogenic System for Ultrasensitive "All-in-Tube" Detection of miRNA in Whole Blood.

The development of liquid biopsy methods for the accurate and reliable detection of miRNAs in whole blood is critical for the early diagnosis and monitoring of diseases. However, accurate quantification of miRNA expression levels remains challenging due to the complex matrix and low abundance of miRNAs in blood samples. Herein, we report a contactless signal output strategy with low background interference that ensures "zero-contact" between the reaction system and the colorimetry system. The designed target-induced magnetic ZnS/ZIF-90/ZnS network can serve as a unique signal amplifier and transducer. It releases hydrogen sulfide (H2S) gas in an acidic solution which can be concentrated in a droplet of only a few microliters in volume, etching the silver layer of Au@Ag nanostars (NSTs) in the droplet. This will lead to changes in the localized surface plasmon resonance signals of the NSTs. Finally, quantitative detection of let-7a is realized by measuring the offset value of the UV-vis absorption peak. Therefore, by virtue of the synergistic action of quadruple signal amplification methods, including catalytic hairpin assembly, ZnS/ZIF-90/ZnS, magnetic separation, and microextraction, the "All-in-Tube" ultrasensitive detection of low-abundance let-7a in whole blood is achieved with a detection limit as low as the aM level. In addition, the "zero-contact" signal output mode effectively solves the problem of complex matrix interference, demonstrating the great potential of this method for miRNA quantification in complex samples, such as whole blood.

Relational hyperevent models for the coevolution of coauthoring and citation networks

Abstract The development of appropriate statistical models has lagged behind the ambitions of empirical studies analysing large scientific networks—systems of publications connected by citations and authorship. Extant research typically focuses on either paper citation networks or author collaboration networks. However, these networks involve both direct relationships, as well as broader dependencies between references linked by multiple citation paths. In this work, we extend recently developed relational hyperevent models to analyse networks characterized by complex dependencies across multiple network modes. We introduce new covariates to represent theoretically relevant and empirically plausible mixed-mode network configurations. This model specification allows testing hypotheses that recognize the polyadic nature of publication data, while accounting for multiple dependencies linking authors and references of current and prior papers. We implement the model using open-source software to analyse publicly available data on a large scientific network. Our findings reveal a tendency for subsets of papers to be cocited, indicating that the impact of these papers may be partly due to endogenous network processes. More broadly, the analysis shows that models accounting for both the hyperedge structure of publication events and the interconnections between authors and references significantly enhance our understanding of the mechanisms driving scientific production and impact.