Search

Networks are widely used in understanding the spread of infectious disease. Universities and colleges are characterized by frequent social gatherings and extensive interpersonal communication, making them ideal applications of complex networks. In this paper, a compound model consisting of a homogeneous (small world) network and a heterogeneous (scale free) network is established, the spreading characteristics of epidemics are analyzed by using the mean‐field (MF) and the heterogeneous mean‐field (HMF) approaches, and the effects of various factors such as the total node number (N), the randomly reconnection probability (p), the initial proportion of infected node (p0), the average degree (k), and the shared nodes (s) are simulated by numerical calculations. The simulation results are in consistent with theoretical predictions, indicating that the randomness effect decreases with increasing k and N and is small when k (k = 8) and N (N = 200) are relatively large; the randomness effect is more significant in the scale free (SF) network than in the small world (SW) network, and the disease spread faster in the SF network; shared nodes can weakly increase the disease spread, but when the shared nodes are involved in an infected and an uninfected network, it can induce the disease outbreak in the uninfected network. [ABSTRACT FROM AUTHOR]

Brain development during childhood involves significant structural, functional, and connectivity changes, reflecting the interplay between modularity, information interaction, and functional segregation. This study aims to understand the dynamic properties of brain connectivity and their impact on cognitive development, focusing on temporal co-occurrence diversity patterns. We recruited 481 children aged 6 to 12 years from the Healthy Brain Network database. Functional MRI data were used to construct dynamic functional connectivity matrices with a sliding window approach. Modular structures were identified using multilayer network community detection, and the Dagum Gini coefficient decomposition technique, which uniquely allows for multi-faceted exploration of modular temporal co-occurrence diversities, quantified these diversities. Mediation analysis assessed the impact on small-world properties. Temporal co-occurrence diversity in brain networks increased with age, especially in the default mode, frontoparietal, and salience networks. These changes were driven by disparities within and between communities. The small-world coefficient increased with age, indicating improved information processing efficiency. To validate the impact of changes in spatiotemporal interaction disparities during childhood on information transmission within brain networks, we used mediation analysis to verify its effect on alterations in small-world properties. This study highlights the critical developmental changes in brain modularity and spatiotemporal interaction patterns during childhood, emphasizing their role in cognitive maturation. These insights into neural mechanisms can inform the diagnosis and intervention of developmental disorders. • Used multilayer networks to construct dynamic functional connectivity matrices from fMRI. • Dagum Gini coefficient explored brain network disparities, showing age-related increases. • Spatiotemporal interaction disparities increased within and between communities with age. • Mediation analysis showed spatiotemporal interaction mediates age and small-world properties. [ABSTRACT FROM AUTHOR]

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

Amidst the global restructuring of the semiconductor supply chain, this paper constructs a global semiconductor trade network (2007, 2012, 2017, 2021) encompassing three segments (raw materials, equipment, and finished components), based on the CEPII database. After initially exploring trade flows among different regions, the paper conducts an in-depth analysis of the network's overall structure and the significance of its nodes. Furthermore, the evolution of the trade network's community structure is discussed and its robustness and dynamics over recent years are assessed through computer program simulation. The findings are as follows: First, semiconductor trade flows are concentrated primarily among a few regions in Asia, US, and EU. Second, the network has grown in size and exhibits significant "small-world" characteristics in all segments, deviating from the typical "sparsity" seen in large-scale networks. Third, Japan, the US, and a few European regions wield significant influence in semiconductor materials and equipment trade, while Asian economies such as Chinese mainland, Chinese Taiwan, and Korea dominate semiconductor components trade. Fourth, the raw materials trade network has diversified in recent years, while the trade networks for equipment and finished components remain in a state of continuous "polarization." Fifth, the semiconductor trade network demonstrates robustness against random attacks but collapses quickly under targeted attacks. Among the three segments, the trade network of finished components, being larger in scale, exhibits greater resilience against both random and targeted attacks. This paper not only enhances the construction of the global semiconductor trade network but also introduces a dynamic perspective, offering deeper insights into its structure and robustness. The insights gained from this analysis provide valuable guidance for policymakers and companies, especially amidst rapid technological change and geopolitical tensions. [ABSTRACT FROM AUTHOR]

This paper adopts the visibility graph (VG) methodology to analyze the dynamic behavior of West Texas Intermediate (WTI), Brent and Shanghai (SC) crude oil futures during the COVID-19 pandemic and Russia–Ukraine conflict. Utilizing daily and high-frequency data, our study reveals a clear power-law decay in VG degree distributions and highlights pronounced clustering tendencies within crude oil futures VGs. We also uncover an inverse correlation between clustering coefficients and node degrees, further identifying that all VGs adhere not only to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we observe that WTI and Brent demonstrate aligned behaviors, while the SC market, with its unique trading mechanisms, deviates. Notably, the five-minute assortativity coefficient provides deep insights into the markets reactions to these global challenges, underscoring the distinct sensitivity of each market. [ABSTRACT FROM AUTHOR]

Summary: Network communication technologies are developing rapidly in various scenarios, such as 6G, SDN/NFV, and IoT. And the demand for dynamic service function chain orchestration is increasing day by day. Due to the dynamic complexity of IoT networks, the service function chain (SFC) embedding problem in IoT scenarios is more difficult. In this paper, a reinforcement learning algorithm based on convolutional neural network is first applied to SFC embedding, combined with DQN's experience pool reply and target network mechanism. The proposed scheme is verified in three typical complex networks: Random network, BA scale‐free network, and small‐world network. The experimental data suggest that the applicability of the approach proposed in IoT scenarios and, on the whole, the proposed algorithm can achieve lower latency and faster convergence performance than the mainstream algorithms with the increase of SFC number and node number. [ABSTRACT FROM AUTHOR]

Anatomical depiction is a technique whereby the product is decomposed into components that are spatially arranged layer by layer to visually explicate its inner structure. The authors demonstrate that anatomical depiction, compared with nonanatomical depiction, enhances product valuation. This effect occurs because anatomical depiction elicits a "coming together" of the inner components in consumers' minds, thereby evoking a gestalt image of the product—a process labeled "simulated assemblage." The elicitation of simulated assemblage in turn boosts confidence in the product's performance. Two field experiments demonstrate that anatomical depiction leads to greater engagement in online settings such as peer-to-peer selling and social media advertising. Subsequently, seven laboratory and online experiments show when and how anatomical depiction elicits simulated assemblage (Studies 1a–c), test the process underlying the effect of anatomical depiction on product valuation (Studies 2a–b), and delineate two boundary conditions, showing that the positive effect of anatomical (vs. nonanatomical) depiction attenuates for consumers higher (vs. lower) in technology anxiety (Study 3) and when consumers have a hedonic (vs. utilitarian) consumption goal (Study 4). Collectively, this work provides insights to firms on how and when to use anatomical depiction to enhance consumers' confidence in and valuation of the product. [ABSTRACT FROM AUTHOR]

This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups. Relative to the Control, the SF-group exhibited lower local efficiency, while the SR-group displayed lower global and local efficiency and longer characteristic path length. Both patient groups displayed reduced centrality in certain subcortical and cortical nodes than the Control, with a more pronounced reduction in the SR-group. Additionally, the SR-group exhibited lower centrality of the right pallidum than the SF-group. Decreased subcortical-cortical connectivity was found in both patient groups than the Control, with a more extensive decrease in the SR-group. Furthermore, an edge connecting the right pallidum and left middle temporal gyrus exhibited decreased connectivity in the SR-group than in the SF-group. A weaker small-worldization network upon medication withdrawal, potentially underpinned by node decentralization and subcortical-cortical decoupling, may elevate the risk of seizure relapse., Competing Interests: Declarations. Ethical approval: This study was approved by the Ethics Committee of the West China Hospital of Sichuan University. Informed consent: Informed consent was obtained from all participants or their guardians. Conflict of interest: The authors declare no competing interests., (© 2024. Fondazione Società Italiana di Neurologia.)

Purpose: Temporal lobe epilepsy (TLE) is a brain network disorder closely associated with synaptic loss and has a genetic basis. However, the in vivo whole-brain synaptic changes at the network-level and the underlying gene expression patterns in patients with TLE remain unclear., Methods: In this study, we utilized a positron emission tomography with the synaptic vesicle glycoprotein 2 A radioligand [ 18 F]SynVesT-1 cohort and two independent transcriptome datasets to investigate the topological properties of the synaptic density similarity network (SDSN) in TLE and its correlation with significantly dysregulated risk genes., Results: We observed an overall decrease in strength, reduced clustering coefficient, and increased path length of SDSN in TLE, suggesting a loss of connectivity that is accompanied by network reorganization. These changes were predominantly distributed in the temporo-limbic circuit and fronto-parietal networks. Moreover, connectivity changes in SDSN were found to be spatially correlated with the brain-wide expression of TLE risk genes, and the transcriptional correlate of SDSN changes showed a significant relationship with gene dysregulation. In particular, we identified a total of 183 downregulated genes that were functionally enriched for synaptic transmission pathways, forming a highly connected genetic interaction network. Within this set of genes, GABAergic genes such as RBFOX1 play a central role., Discussion: Our study provides the first evidence that the spatial expression patterns of downregulated risk genes underlie in vivo synaptic density network dysfunction in TLE. These imaging-transcriptomic findings have the potential to guide the development of molecular and genetic network-based therapeutic approaches for TLE., Competing Interests: Declarations. Ethics approval and consent to participate: This retrospective study was approved by the Ethics Committee of Central South University. Written informed consent was obtained from all participants prior to enrolment. Consent for publication: Not applicable. Competing interests: Axel Rominger and Kuangyu Shi are editors of the journal European Journal of Nuclear Medicine and Molecular Imaging. The other authors declare that they have no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To apply a network medicine-based approach to analyze the phenome of the prenatal fetal MRI and biometric findings in the Chiari II malformation (CM II) to detect specific patterns and co-occurrences., Method: A single-center retrospective review of fetal MRI scans obtained in fetuses with CM II was performed. Co-occurrence analysis was utilized to generate a phenotypic comorbidity matrix and visualized by Gephi software. Traditional univariate regression and geometric thin-plate spline methodology were used to elucidate the mechanisms underlying the relationships between morphometric measurements and geometric landmarks of the spine, skull, and brain deformations., Results: The CM II phenome consists of 35 nodes interconnected by 979 edges with a density of 0.828. Key "hubs" identified within this network include spinal bony defects, reduced posterior fossa dimensions, and vermis ectopia. The brain edema phenotype appearing only in the fetal stage but disappearing after postnatal surgery, links to increased postnatal morbidity and demonstrates distinct shape patterns by geometric analysis. Traditional univariate regression reveals correlations among spinal defects, posterior fossa dimensions, and caudal extent of vermis ectopia. The degree of brain rearrangement versus spinal bony rearrangement shows a correlation (r = 0.721, p = 0.0023) by partial least-squares analysis., Conclusion: The CM II prenatal phenome is a multifaceted network centered around three key elements-spinal bony defects, small posterior fossa, and vermis ectopia-with strong interconnections. Fetal brain edema emerged as an exclusively prenatally detectable and transient phenotype of prognostic relevance., (© 2025 The Author(s). Prenatal Diagnosis published by John Wiley & Sons Ltd.)

• A range of graph theory indices were utilized to analyze the brain networks of both stroke patients and healthy subjects. • The differences in brain network connectivity across the active, motor imagery, and passive task paradigms were compared. • Both ipsilateral and contralateral motor areas were reorganized in stroke patients during the MI task. • Compared to active and passive tasks, motor imagery demonstrates a greater capacity to promote brain reorganization. • This study proposed a potential explanatory mechanism for the role of motor imagery in promoting stroke rehabilitation. Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function. [ABSTRACT FROM AUTHOR]

The U.S. swine industry is vulnerable to the rapid spread of disease due to systemic structural issues. While animal movement networks are used to identify disease spread risks and design response plans, human movement between farms were rarely accounted for. Human movements, when integrated with animal movement models, create a different, more inclusive, and accurate network structure when compared to animal movements alone. One year of propriety farm visit data was analyzed and consisted of anonymized property IDs, location, and user/truck IDs, along with visit dates, property, vehicle, and entry types from three swine management companies. A static directed network was created using the igraph package in R for all movements, with separate sub-networks for each entry type (animal, human, and subsets of vehicle types). Network statistics for each sub-network were compared. The full network included 455 properties, 11 property types, 9 vehicle types, 12 entry types, and 320001 edges (trips between properties). The longest path length was 10 in the animal movement network but decreased to 5 for the full and human movement network, while the average path length decreased from 3.2 to 2.2. Edge density increased from 0.03 to 0.09 for the human network and 0.1 for the full network. For all network properties examined, the full and human movement networks demonstrated higher connectivity than the animal network. A heavy right skew in the degree distributions indicates a 'hub' structure (scale-free-like network) and the shorter path lengths indicates a small-world network topology. The full network is very well connected, more so than expected based on animal movement alone. Hubs may indicate points of disease susceptibility and 'super-spreader' properties. The high connectivity shows that swine farm networks may be more susceptible to spread of an introduced disease than expected from previous analyses. Monitoring human, as well as animal movement, provides for a more complete and accurate understanding of swine farm biosecurity risks. [ABSTRACT FROM AUTHOR]

Complex networks, which exhibit high connectivity, self-organization, small-world properties, and heterogeneity, are susceptible to the rapid spread of local failures, often resulting in cascading effects throughout the entire system. The paper introduces a cascading failure model based on biased random walks that incorporate betweenness centrality and the power-law distribution of node degrees. This model is used to investigate cascade failures triggered by extreme fluctuations in load that follow a Poisson distribution. Furthermore, we propose a resilience-based sequential recovery strategy that accounts for varying node recovery time and resource limitations, setting an upper limit on the number of nodes that can be in recovery simultaneously. The network's robustness improves, and the variation in the power-law exponent during cascading failures and recovery decreases when the betweenness bias parameter is set to 1 instead of -1. The capacity parameter has the most significant and direct effect on improving the network's robustness. Reducing node recovery time can improve the network's initial invulnerability; however, its impact on final residual resilience remains limited. The power-law exponent of the initial network significantly affects residual resilience during the recovery process, with higher exponents leading to improved network performance. An appropriate increase in the number of nodes that can be in recovery simultaneously can enhance the overall recovery performance of the network. Extensive comparative simulations reveal substantial advantages of our proposed recovery strategy in enhancing network recovery. • We developed a cascading failure model with betweenness and power-law degree bias. • We propose a resilience-based model that synchronizes cascade failures and recovery. • Our model considers resource constraints and recovery time heterogeneity. • We introduced three optimized metrics for evaluating node importance. [ABSTRACT FROM AUTHOR]

The rapid expansion of ICT-related multinational enterprises (IMNEs) has introduced significant challenges in emissions mitigation. This paper uses a multi-regional input-output model and complex network analysis to examine the global CO 2 emissions transfer network driven by IMNEs (GCNI) from 2000 to 2019. The results reveal that between 2009 and 2019, post-financial crisis interconnectedness increased, raising network density from 0.48 to 0.58. During the period of 2000–2019, the GCNI underwent dynamic cluster adjustments, forming two distinct communities by 2019: an Asian community led by China and a cross-regional community led by the United States (US). China, Hong Kong, the US, Japan, South Korea, Germany, and Taiwan held central positions, with smaller economies acting as connectors in a core-periphery structure. These findings emphasize the need to strengthen global governance and foster cooperative emission reduction in the digital era. • We analyze the network structural evolution of global CO 2 transfer via ICT investment. • The overall network structure tends to be compact and stable after economic crisis. • The network has an obvious small-world property and hierarchical structure. • China, Hong Kong, the US, Japan, South Korea, Germany and Taiwan are key regions. • The network contains two communities led by China and the US respectively in 2019. [ABSTRACT FROM AUTHOR]

Memristor Neural Networks (MNNs) stand out for their low power consumption and accelerated matrix operations, making them a promising hardware solution for neural network implementations. The efficacy of MNNs is significantly influenced by the careful selection of memristor update thresholds and the in-situ update scheme during hardware deployment. This paper addresses these critical aspects through the introduction of a novel scheme that integrates Dynamic Threshold (DT) and Gradient Accumulation (GA) with Threshold Properties. In this paper, realistic memristor characteristics, including pulse-to-pulse (P2P) and device-to-device (D2D) behaviors, were simulated by introducing random noise to the Vteam memristor model. A dynamic threshold scheme is proposed to enhance in-situ training accuracy, leveraging the inherent characteristics of memristors. Furthermore, the accumulation of gradients during back propagation is employed to finely regulate memristor updates, contributing to an improved in-situ training accuracy. Experimental results demonstrate a significant enhancement in test accuracy using the DTGA scheme on the MNIST dataset (82.98% to 96.15%) and the Fashion-MNIST dataset (75.58% to 82.53%). Robustness analysis reveals the DTGA scheme’s ability to tolerate a random noise factor of 0.03 for the MNIST dataset and 0.02 for the Fashion-MNIST dataset, showcasing its reliability under varied conditions. Notably, in the Fashion-MNIST dataset, the DTGA scheme yields a 7% performance improvement accompanied by a corresponding 7% reduction in training time. This study affirms the efficiency and accuracy of the DTGA scheme, which proves adaptable beyond multilayer perceptron neural networks (MLP), offering a compelling solution for the hardware implementation of diverse neuromorphic systems. [ABSTRACT FROM AUTHOR]

Coastal lagoons, which cover about 13% of coastline, are among the most productive ecosystems worldwide. However, they are subject to significant stressors, both natural and anthropogenic, which can alter ecosystem services and functioning and food web structure. In the Comacchio Lagoon (Northern Italy), eutrophication, among other minor factors, transformed the ecosystem in the early 1980s. Here, we compiled available data for the lagoon into trophic networks (pre- and post-transformation), analyzed the ecosystem using local and global network analysis, and computed trophic fluxes of the two periods. For comparability, the networks of two periods (i.e., pre- and post- transformation) were aggregated into food webs with 23 nodes. We found differences in the trophic networks before and after eutrophication, resulting in some decrease in complexity, increase of flow diversity, and an overall shortening of the food chain. A crucial aspect of this change is the disappearance of submerged vegetation in the lagoon and the increased importance of cyanobacteria in the post-eutrophication period. We provide an approach to better understand ecosystem changes after severe disturbances which can be extended to biodiversity conservation and for the management of coastal resources in general. [ABSTRACT FROM AUTHOR]

Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain. Author summary: Identifying control nodes in complex networks is essential for understanding and influencing biological systems. However, existing network control methods often fall short when the number of driver nodes is small relative to the network size, limiting their practical application. To address this, we developed a novel framework combining spectral graph theory and output controllability, enabling us to reduce the system into a smaller, manageable space using network Laplacian projections. Extensive testing on synthetic and real-world networks showed that a limited number of projected components greatly improved control accuracy. We applied our method to 6,134 human brain connectomes from the UK Biobank dataset, revealing influential brain regions and mapping directed interactions across cerebral systems. Our findings offer new insights into brain organization and hemispheric lateralization, providing a robust solution for network controllability in biological systems. [ABSTRACT FROM AUTHOR]

This article discusses the dynamics of innovation in America and Europe, focusing on variables such as access to technology, education, and life expectancy. To do this, the article proposes an agent-based model called the Innovameter. The dependent variable is the Global Innovation Index. The paper focuses on data analysis through correlation analysis and multiple hierarchical regressions to determine the contribution of specific variables related to the pillars of the Global Innovation Index and indicators of the Human Development Index. After analyzing the data, an agent-based model was built to parameterize these main variables by defining two levels of abstraction: at the global level, there is the country, where birth rates, life expectancy, ICT use, and research and development are defined. At the local level, we define the individuals who have an age, years of schooling, and income. A series of experiments were conducted by selecting data from 30 countries. From the results of the experiments, a nonparametric correlation analysis was performed, and correlation indices were obtained indicating a relationship between the predicted outcomes and the outcomes in the global index. The proposed model aims to provide suggestions on how the different variables can become the norm in most of the countries studied. [ABSTRACT FROM AUTHOR]

Cognition plays a central role in the diagnosis and characterization of dementia with Lewy bodies (DLB). However, the complex associations among cognitive deficits in different domains in DLB are largely unknown. To characterize these associations, we investigated and compared the cognitive connectome of DLB patients, healthy controls (HC), and Alzheimer's disease patients (AD). We obtained data from the National Alzheimer's Coordinating Center. We built cognitive connectomes for DLB (n = 104), HC (n = 3703), and AD (n = 1985) using correlations among 24 cognitive measures mapping multiple cognitive domains. Connectomes were compared using global and nodal graph measures of centrality, integration, and segregation. For global measures, DLB showed a higher global efficiency (integration) and lower transitivity (segregation) than HC and AD. For nodal measures, DLB showed higher global efficiency in most measures, higher participation (centrality) in free-recall memory, processing speed/attention, and executive measures, and lower local efficiency (segregation) than HC. Compared with AD, DLB showed lower nodal strength and local efficiency, especially in memory consolidation. The cognitive connectome of DLB shows a loss of segregation, leading to a loss of cognitive specialization. This study provides the data to advance the understanding of cognitive impairment and clinical phenotype in DLB, with implications for differential diagnosis. [ABSTRACT FROM AUTHOR]

Opportunistic behaviour has become a research hotspot in hydraulic infrastructure project management owing to its serious damage to the cooperation efficiency of all participants in a project. The trust networks formed by each participant can restrain opportunistic behaviour, but due to the dynamic evolution of the networks, its research should adopt a dynamic paradigm. The structure and evolution of trust networks can be simulated using computer simulations and modelling. This study explores the influence mechanism of the structural characteristics of trust networks on the diffusion of opportunistic behaviour. The results show that trust network density has a positive effect on inhibiting the implementation tendency of opportunistic behaviour. However, the centralisation of trust networks has a negative correlation with the degree of inhibition of opportunistic behaviour. [ABSTRACT FROM AUTHOR]

Following recent advances in post-thrombectomy stroke care, the role of neuroinflammation and neuroprotective strategies in mitigating secondary injury has gained prominence. Yet, while neuroprotection and anti-inflammatory agents have re-emerged in clinical trials, their success has been limited. The neuroinflammatory response in cerebral ischemia is robust and multifactorial, complicating therapeutic approaches targeting single pathways. In this study, we aimed to characterize early inflammatory gene dysregulation following ischemic stroke using translational in-silico and in-vivo approaches. Using an in vivo ischemic stroke model, transcriptomic analysis revealed significant dysregulation of inflammatory genes. Graph theory analysis then showed a rich-club organization among stroke-related genes, with highly connected core nodes. The expression levels of the core genes identified within this network significantly explained radiological outcomes, including T2-signal hyperintensity (R2 = 0.57, P < 0.001), mean diffusivity (R2 = 0.52, P < 0.001), and mean kurtosis (R2 = 0.65, P < 0.001), correlating more strongly than non-core genes. Similar findings were observed with functional and cognitive outcomes, showing R2 values of 0.58, 0.7, 0.54, and 0.7 for neurological severity scores, corner tasks, passive avoidance, and novel object recognition tasks, respectively (P < 0.001). Using in-silico analysis, we identified a set of upstream regulators directly interacting with core network nodes, leading to simulations that highlighted C3-targeted therapy as a potential treatment. This hypothesis was then confirmed in vivo using a targeted C3 inhibitor (CR2-fH), which reversed gene dysregulation in the neuroinflammatory network and improved radiological and functional outcomes. Our findings underscore the significance of neuroinflammation in stroke pathology, supporting network-based therapeutic targeting and demonstrating the benefits of targeted complement inhibition in enhancing outcomes through modulation of the neuroinflammatory network core. This study's approach, combining graph theory analysis along with in-silico modeling, offers a promising translational pipeline applicable to stroke and other complex diseases. [ABSTRACT FROM AUTHOR]

Studying how we explore the world in search of novelties is key to understand the mechanisms that can lead to new discoveries. Previous studies analyzed novelties in various exploration processes, defining them as the first appearance of an element. However, novelties can also be generated by combining what is already known. We hence define higher-order novelties as the first time two or more elements appear together, and we introduce higher-order Heaps' exponents as a way to characterize their pace of discovery. Through extensive analysis of real-world data, we find that processes with the same pace of discovery, as measured by the standard Heaps' exponent, can instead differ at higher orders. We then propose to model an exploration process as a random walk on a network in which the possible connections between elements evolve in time. The model reproduces the empirical properties of higher-order novelties, revealing how the network we explore changes over time along with the exploration process. We explore the world in search of novelties, which are traditionally defined as the first appearance of a new element in a sequence of exploration. The authors show that novelty can also arise from combining already known elements, introducing the concept of higher-order novelties as the first appearance of such combinations in the sequence. They propose a measure and model to study the dynamics of these higher-order novelties. [ABSTRACT FROM AUTHOR]

Corpus methods are now established within the field of signed language linguistics. Empirical investigations of signed language corpora have challenged many early assumptions about the nature of deaf community signed languages, while making us more aware of the limitations of traditional corpus documentation methods. One limitation relates to insufficient documentation of ethnographic information that is often necessary for accurately understanding and interpreting corpus data. Linguistic ethnography offers unique possibilities for addressing this limitation. This article outlines a novel interview method developed to enrich the original Corpus of Finnish Sign Language (Corpus FinSL) with additional ethnographic information eight years after it was first documented and archived with standard IMDI corpus metadata. We interviewed 22 Corpus FinSL signers about their family and social networks, as well as their lifelong language, geographical, educational, and employment trajectories. Here we describe how this information illuminates the linguistic analysis and interpretation of constructed action – an enactment-based way to express meaning – in Corpus FinSL data. Our results reveal constructed action in FinSL discourse is influenced by factors like signer's educational background and age, but not exclusively by family networks or use of other sign languages. The interview materials demonstrate diversity and change in the communicative ecologies of FinSL, which is discussed in relation to the use of constructed action in FinSL. We argue that this kind of approach enables signed language corpus linguistics to "open up" more to signers' lived experiences, while still "tying down" empirical descriptions of FinSL. A major benefit is the enrichment of both machine-readable annotations and metadata, while supporting deeper engagement between deaf signing communities and signed language corpus projects. [ABSTRACT FROM AUTHOR]

In response to the harsh and limited conditions prevalent in remote areas, wireless sensor and actuator networks (WSANs) play an essential role in Internet-of-Things systems by monitoring and interacting with unattended environments. However, the sensors employed by the majority of WSANs are powered by batteries, ensuring the efficient use and conservation of energy is vital for guaranteeing network connectivity and efficiency. To address this challenge, we proposed a divisive hierarchical clustering method based on K-means++ to organize the sensors. The intra-class distance of the cluster is fully taken into account to achieve the balance and full utilization of node energy. Furthermore, we utilize unmanned aerial vehicles (UAVs) for simultaneous data collection and develop a modified improved partheno genetic algorithm incorporating the Davies–Bouldin index for UAV scheduling. This approach effectively reduces network delay and balances network load. Numerical simulations demonstrate that our proposed method not only extends network lifetime but also balances energy savings and data collection latency. [ABSTRACT FROM AUTHOR]

Charging infrastructure, represented by charging piles, is a critical component of the new infrastructure initiative, and its regulation is essential for the safety and stability of the new energy vehicle industry chain. This paper addresses the issue of illegal operations in the charging pile market by developing a game model involving three key stakeholders: charging pile operators, local governments, and new energy vehicle consumers, within the context of a dynamic reward and punishment mechanism. The study conducts a stability analysis to identify potential strategic equilibrium points and employs numerical simulations to assess the impact of parameter variations on system evolution. The results indicate that: (1) an increase in net revenue effectively encourages charging pile operators to engage in regulated operations; (2) both enhanced incentives and penalties for operators promote compliance, with local governments able to strengthen these measures by raising the upper limits and adjusting the incentive-to-penalty ratios; (3) a combined approach of incentives and penalties is more effective than using either policy independently; (4) under the dynamic reward-punishment framework, there is a threshold for the effectiveness of government interventions: when reward-punishment strength is below a critical value, punitive measures are more effective, while above this threshold, incentives yield better outcomes. Finally, improving compensation for consumers affected by violations and simplifying complaint procedures not only pressures operators to standardize their behavior but also encourages consumer involvement in the governance process of charging pile operations. [ABSTRACT FROM AUTHOR]

Purpose: This study examines the relationship between school ties among external auditors and audit committee members, and their joint impact on audit fee. We also examine how the monitoring and executive functions within companies moderate this relationship. Design/methodology/approach: This study employs a regression analysis model on a sample of companies listed on the Indonesia Stock Exchange from 2016 to 2019, followed by additional analyses using high-low growth and tech samples, as well as robustness tests involving coarsened exact matching (CEM) and Heckman's (1979) theory to address potential causality issues. Findings: This study reveals that school ties between external auditors and audit committees positively influence audit fee. The audit committee size weakens this relationship, while the presence of an internal audit enhances it. Research limitations/implications: This research contributes to the literature related to the relationship between school ties and audit fee in Indonesian public companies, providing insights for stakeholders and informing company policies. It aims to increase awareness of the significance of school ties among Indonesian companies. Originality/value: This research fills a knowledge gap by examining the link between audit committee-external auditor relationships and audit fees, aiming to generate new insights and empirical evidence to inform future research and regulatory decisions. [ABSTRACT FROM AUTHOR]

Small rodents can cause problems on farms such as infrastructure damage, crop losses or pathogen transfer. The latter threatens humans and livestock alike. Frequent contacts between wild rodents and livestock favour pathogen transfer and it is therefore important to understand the movement patterns of small mammals in order to develop strategies to prevent damage and health issues. Miniaturised proximity loggers are a newly developed tool for monitoring spatial behaviour of small mammals. The strength of the Bluetooth Low Energy (BLE) signal can be used as an indicator of close contacts between wild rodents and livestock feeding sites, which is relevant for identifying possible transmission routes. This method study focussed on the use of the technology in an agricultural setting as well as dry runs for testing and calibrating this technology in farming environments used for animal husbandry. Results show that the battery life of the loggers was mainly influenced by the pre-set scan interval. Short scan intervals resulted in reduced battery lifespan and should be maximised according to the activity patterns of the target species. Habitat affects BLE signal strength resulting in higher signal strength indoors than outdoors. The height of the location of the loggers positively affected signal strength in livestock stables. Signal reception generally decreased with increasing distance and differed among loggers making calibration necessary. Within habitat specific distances, BLE proximity logging systems can identify contacts among small mammals and between animals and particular structures of interest. These results support the use of BLE based systems in animal husbandry environments and contribute to a body of evidence of validated techniques. In addition, such approaches can provide valuable insights into possible pathogen transmission routes. [ABSTRACT FROM AUTHOR]

Although the citizens' satisfaction in smart cities (CSSC) has become a vital criterion for smart cities, which symbolizes the people-centric concept, the previous research on simulating CSSC only considered the technological innovation and policy changes at the macro level, resulting in the neglect of the micro emotional contagion between citizens. To address this gap, a simulation approach is proposed to consider the influence caused by emotional contagion through the agent-based model (ABM). Supported by the expectation confirmation model (ECM) and the emotional contagion theory (ECT), the ABM simulation is implemented through the Anylogic platform, based on survey data from 19 smart cities in China. The results of various scenarios show that citizens' expectations are the key factor affecting the CSSC, and the influence of emotional contagion cannot be ignored, especially in the acquaintance society. As social networks become more intimate, the impact of emotional contagion will turn the citizen group into an "antithetical society," which means a situation with severe polarization of CSSC. This study also discusses the possible threshold of this transition process. Based on the analysis of multiple scenarios, it is implied that CSSC should be evaluated by considering the emotional contagion, and smart cities prioritize quality over quantity. [ABSTRACT FROM AUTHOR]

Purpose: In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling. Design/methodology/approach: Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data. Findings: The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration. Originality/value: This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way. [ABSTRACT FROM AUTHOR]

Fairness stands as a pivotal element in maintaining social stability. It is still a highly challenging issue to explain how fairness emerges in society. The ultimatum game (UG) and the dictator game (DG) have become prominent paradigms for exploring the emergence of fairness. A great number of mechanisms have been put forward to promote the emergence of fairness in these two games. In this paper, we sort out the recent relevant evolutionary game models regarding fairness and categorize them according to the mechanisms for the emergence of fairness. The main mechanisms consist of network reciprocity, role assignment, spite, empathy, stake size, and indirect reciprocity. Especially for indirect reciprocity, we make a detailed introduction of a theoretical method and some recent works that use this theoretical approach to explore how fairness evolves. Finally, we suggest several potential avenues for future research. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the causal complexity of ECF investors' peer effect through two different paths of structural social influence. Design/methodology/approach: Using the fuzzy-set qualitative comparative analysis (fsQCA) approach, we employ 157 samples from a Chinese ECF source to explore how peer-effect are caused by both informational and normative mechanisms. Findings: The findings suggests that there are multiple configurations could lead to ECF investors' high level peer-effect through both informational and normative mechanisms, and the informational mechanism' role depends on the normative mechanism, while the normative mechanism could lead to peer-effect independently. Research limitations/implications: The findings enrich the literature on ECF investors' behaviors by revealing the diverse configurations resulting in investors' peer-effect and shedding new light on investigating the decision-making driven by information asymmetry and relationship settings for individuals at a disadvantage. Originality/value: This is the first study that investigates the multiple-driven of ECF investors' decision-making and the importance of mutual norms in individuals' decision-making by complex network analysis approach and qualitative comparative analysis from the perspective of complexity. The results reveal the complexity of investors' decision-making in ECF. [ABSTRACT FROM AUTHOR]

The article introduces a method of requirements-service mapping based on large-scale language models, utilizing the significant semantic understanding capability of large language models. It leverages multiple rounds of natural language question-answering to interact with users, achieve the transformation of users’ vague requirements into structured information, and eventually map to specific application services. Through combining large language models with traditional vector searching techniques, the micro-adjustment of large language models is realized for extracting and structuring requirements’ information without retraining or inputting massive data to build context. It presents classification of requirements and definition of service attributes to constrain and regulate content of user requirements, providing rules for large language models to express non-structured raw requirements into clear structured information. Upon obtaining the structured information, word embedding is further used to vectorize service information and requirements. The service mapping process is completed through vector matching algorithms, realizing the ultimate transformation from requirements to services. Finally, through industrial application service template, case studies have been conducted to analyze the accuracy of mapping under different requirement rules, thus ultimately demonstrating the effectiveness of the requirement-mapping method proposed in this article. [ABSTRACT FROM AUTHOR]

The temporary cessation of air travel imposed by the COVID-19 pandemic provided business travelers with an opportunity to mentally "zoom out" and reflect on their suspended hypermobile lifestyle. The present study used these circumstances to reexamine three key issues in the literature pre-pandemic: (a) the costs and benefits of a hypermobile lifestyle; (b) the justification of business trips given the option of online meetings; and (c) the role of tourism in shaping the business trip experience. An interpretive analysis of in-depth interviews with grounded business travelers conducted at the height of the pandemic revealed that the lack of frequent travel improved work-life balance but evoked a longing for tourism-related experiences, such as change, novelty, and pleasure. Grounded business travelers also proclaimed that the lack of physical proximity to colleagues caused by the suspension of travel was followed by difficulties in generating new business relationships and nurturing creativity at work. The study presented the costs and benefits of immobility as a reverse image of hypermobility, reinforcing the notion of hypermobility as a stressful but exciting lifestyle. It showed that physical proximity with colleagues cannot be fully replaced by online meetings, supporting the opinion that stresses the need for business trips. By suggesting that tourism-related experiences serve as anchor points for the reconstruction of memories and longing in the minds of grounded business travelers, the study confirmed that tourism-related experiences are important components of business trips. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming and rising sea levels, storm surge disasters occur frequently, often forming complex chains of events that lead to severe crises. However, systematic research on storm surge disaster chains is scarce. To characterize these chains, this research proposes a storm surge disaster chain analysis system based on complex networks and Bayesian networks. The system consists of three modules: evaluation, prediction, and measurement. The evaluation module uses a complex network model to quantitatively analyze the vulnerability, key nodes, and critical transmission paths of the disaster chain complex network. The prediction module establishes a Bayesian network-based model to forecast the complex network evolution process, forecasting the occurrence probability and loss scenarios of the disaster events. The measurement module measures and calculates the chain effect based on the dependence relationship and loss degree of the disaster event loss scenario. The results elucidate that most key nodes are primary and secondary disasters such as seawater flooding, flooding, dam damage, rainstorm, and house damage. Meanwhile, edges such as the sea wave–seawater flooding and house damage–human casualties have a critical impact on the storm surge disaster chain complex network. Key evolutionary paths such as strong winds–human casualties and over-warning tide level–social influence need to be focused on. Disaster reduction strategies such as maintaining dams, reinforcing houses, and removing disaster-bearing body can effectively break the chain and mitigate disasters. This research has a reference value for the scientific understanding of storm surge disaster chains and can serve as a scientific basis for comprehensive disaster reduction, disaster preparedness, and disaster relief. [ABSTRACT FROM AUTHOR]

The present study aimed to apply a network analysis model to provide an exploratory empirical conceptualization of dynamic networks of posttraumatic growth (PTG) symptoms in 7,434 people who experienced the death of a loved one from COVID-19 in 16 Latin American countries. The Post-Traumatic Growth Inventory: Short Form of Eight Items was used. A non-regularized network with partial correlation coefficients was estimated through the ggmModSelect algorithm. The network architecture was analyzed for each country through its local properties and global properties. The results indicated that the networks differed significantly between countries. The core dimensions in the networks were relating to others, personal strength, and life value and opportunities, which were more related dimensions that reinforce the emergence of PTG in all countries. The findings may be useful to motivate researchers and mental health professionals to consider the importance of the individual dimensions of PTG in groups of people who experienced the death of a loved one from COVID-19 in 16 Latin American countries, as well as their interrelationships. [ABSTRACT FROM AUTHOR]