Average Path Length Research Articles

Dominance of rock exposure and soil depth in leaf trait networks outweighs soil quality in karst limestone and dolomite habitats

Leaf trait networks (LTNs) visualize the intricate linkages reflecting plant trait-functional coordination. Typical karst vegetation, developed from lithological dolomite and limestone, generally exhibits differential communities, possibly due to habitat rock exposure, soil depth, and soil physicochemical properties variations, leading to a shift from plant trait variation to functional linkages. However, how soil and habitat quality affect the differentiation of leaf trait networks remains unclear. LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains. The differences between dolomite and limestone LTNs were compared using network parameters. The network association of soil and habitat quality was analyzed using redundancy analysis (RDA), Mantle's test, and a random forest model. The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN. It indicates LTN differentiation, with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network. The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN, as well as the leaf mass and leaf carbon contents of limestone LTN, significantly contributed to network degree and closeness, serving as crucial node traits regulating LTN connectedness. Additionally, both habitat LTNs significantly correlated with soil nitrogen and phosphorus, stoichiometric ratios, pH, and organic carbon, as well as soil depth and rock exposure rates, with soil depth and rock exposure showing greater relative importance. Soil depth and rock exposure dominate trait network differentiation, with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.

THREE PROPERTIES OF FRACTAL NETWORKS BASED ON BEDFORD–MCMULLEN CARPET

In this paper, we consider the networks modeled by several self-affine sets based on the Bedford–Mcmullen carpet. We calculate three properties of the networks, including the cumulative degree distribution, the average clustering coefficient and the average path length. We show that such networks have scale-free and small-world effects.

Resting state connectivity biomarkers of seizure freedom after epilepsy surgery

Alterations in brain networks may cause the lowering of the seizure threshold and hypersynchronization that underlie the recurrence of unprovoked seizures in epilepsy. The aim of this work is to estimate functional network characteristics, which may help predicting outcome of epilepsy surgery.Twenty patients were studied (11 females, 9 males, mean age 33 years) with scalp-recorded HD-EEG in resting state (eyes closed, no interictal discharges) before intracranial evaluation, which allowed the precise determination of the epileptogenic zone. Dipole source time courses in the brain were estimated using Weighted Minimum Norm Estimate based on HD-EEG signals. Information inflow and outflow of atlas-based brain regions were computed using partial directed connectivity. A set of graph measures for pairwise connections in standard EEG frequency bands was calculated.After epilepsy surgery 10 patients were seizure-free (Engel 1a) and 10 patients continued suffering from seizures (Engel outcome worse than 1a). Inflow of the regions containing the epileptogenic zone in the beta and delta frequency bands was significantly lower in patients who achieved seizure-freedom after surgery, compared with patients who continued to have seizures (p = 0.012, and p = 0.026, respectively). Average path length in the beta frequency band was significantly higher in patients who achieved seizure freedom (p = 0.012). In the delta frequency band, local efficiency and clustering coefficient were significantly higher in patients who achieved seizure freedom (0.033, 0.046).In patients who achieved seizure freedom after surgery, the preoperative analysis of the epileptic network exhibited stronger separation of the region containing the seizure onset zone, with less inflow of information. In contrast, shorter paths within the epileptic network may facilitate hypersynchronous neuronal activity and thus the recurrence of seizures in non-seizure free patients. This study supports the hypothesis that epileptic network properties might help to define suitable candidates for epilepsy surgery.

Multiple sclerosis is associated with differences in semantic memory structure.

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimation of Photon Path Length and Penetration Depth in Articular Cartilage Zonal Architecture Over the Therapeutic Window.

The key characteristics of light propagation are the average penetration depth, average maximum penetration depth, average maximum lateral spread, and average path length of photons. These parameters depend on tissue optical properties and, thus, on the pathological state of the tissue. Hence, they could provide diagnostic information on tissue integrity. This study investigates these parameters for articular cartilage which has a complex structure. We utilize Monte Carlo simulation to simulate photon trajectories in articular cartilage and estimate the average values of the light propagation parameters (penetration depth, maximum penetration depth, maximum lateral spread, and path length) in the spectral band of 400-1400 nm based on the optical properties of articular cartilage zonal layers and bulk tissue. Our findings suggest that photons in the visible band probe a localized small volume of articular cartilage superficial and middle zones, while those in the NIR band penetrate deeper into the tissue and have larger lateral spread. In addition, we demonstrate that a simple model of articular cartilage tissue, based on the optical properties of the bulk tissue, is capable to provide an accurate description of the light-tissue interaction in articular cartilage. The results indicate that as the photons in the spectral band of 400-1400 nm can reach the full depth of articular cartilage matrix, they can provide viable information on its pathological state. Therefore, diffuse optical spectroscopy holds significant importance for objectively assessing articular cartilage health. In this study, for the first time, we estimate the light propagation parameters in articular cartilage.

Quantum Network Communication Resource Optimization Scheme Based on Multi-Scale Entanglement Renormalization Ansatz

Quantum Key Distribution (QKD) is a pivotal technology in the field of secure communications, leveraging the principles of quantum mechanics to enable theoretically unbreakable encryption. However, despite its promise, QKD faces significant challenges in achieving large-scale deployment. The primary hurdle lies in the scarcity of quantum resources, especially entangled photon pairs, which are fundamental to protocols such as Ekert91. In traditional QKD implementations, only a fraction of the entangled pairs generated contribute to raw key production, leading to substantial inefficiencies and resource wastage. Addressing this limitation is crucial to the advancement and scalability of QKD networks.<br>This paper introduces an innovative approach to QKD by integrating the Multiscale Entanglement Renormalization Ansatz (MERA), a technique originally developed for many-body quantum systems. By utilizing MERA's hierarchical structure, the proposed method not only improves the efficiency of entanglement distribution but also reduces the consumption of quantum resources. Specifically, MERA compresses many-body quantum states into lower-dimensional representations, allowing for the transmission and storage of entanglement in a more efficient manner. This compression significantly reduces the number of qubits required, optimizing both entanglement utilization and storage capacity in quantum networks.<br>To evaluate the performance of this method, we conducted simulations under standardized conditions. The simulations assumed a 1024-bit encryption request, an 8\% error rate, an average path length of 4 hops in the quantum network, and a 95\% success rate for both link entanglement generation and entanglement swapping operations. These parameters mirror realistic physical conditions found in contemporary QKD networks. The results demonstrate that the MERA-based approach saves an impressive 124,151 entangled pairs compared to traditional QKD protocols. This substantial reduction in resource consumption underscores the potential of MERA to revolutionize the efficiency of QKD systems without compromising security. Importantly, the security of the key exchange process remains intact, as the method inherently adheres to the principles of quantum mechanics, particularly the no-cloning theorem and the use of randomness in decompression layers.<br>The paper concludes that MERA not only enhances the scalability of QKD by optimizing quantum resource allocation but also maintains the security guarantees essential for practical cryptographic applications. By integrating MERA into existing QKD frameworks, we can significantly lower the resource overhead, making large-scale, secure quantum communication more feasible. These findings contribute a new dimension to the field of quantum cryptography, suggesting that advanced quantum many-body techniques like MERA hold the potential to unlock the full potential of quantum networks in real-world scenarios.

Analysis of tourism destination centrality and structural properties of tourism system: Complex network perspective

ourism has become a new way of living with the living standard development. This study analyses the tourism destination centrality and spatial patterns of the tourism system using complex network analysis. An analysis of 245 destinations in the South of Thailand has found that the network has a low network density, large average path length and low clustering coefficient. Some a small number of high-degree destinations connect to each other, while most connect to others with a low degree. The network comprises 18 subnetworks that destinations densely connect to others in the same subnetwork but sparsely connect to others in different ones. Destinations play different roles in the network based on which a centrality measure is used, degree, betweenness and closeness centrality. 31 destinations with high hub and authority centrality are the centers playing as hubs of the network. The study’s findings draw implications for the sector.

COMPLEX NETWORKS GENERATED BY A SELF-SIMILAR PLANAR FRACTAL

Many complex networks have scale-free and small-world effects. In this paper, a family of evolving networks is constructed modeled by a non-symmetric self-similar planar fractal, using the encoding method in fractal geometry. Based on the self-similar structure, we study the degree distribution, clustering coefficient and average path length of our evolving network to verify their scale-free and small-world characteristics.

New Vocational Education Law on Artificial Intelligence Helps Build a Skilled Society

Abstract Under the guidance of the new vocational education method of artificial intelligence, this paper divides the skill-based society into skill-based organizations, and constructs a knowledge dynamic equilibrium model based on superimposed knowledge fields. The knowledge-explicit module of skill-based talent flow and the external dynamic equilibrium module are integrated to achieve the evolutionary modeling of knowledge resources in skill-based organizations. Based on the discussion of the new vocational education law, the characteristic models of intellectual resource development, knowledge dynamic growth, group evolution and talent motivation in skill-based organizations are proposed respectively. The simulation analysis of the knowledge dynamic equilibrium network is combined with the analysis of influencing factors to inspire the education of higher vocational colleges and universities under the new vocational education method. As measured by the data, the average path length mean values of the intra-organizational knowledge network when N=160, 260, 330 are 2.2761, 2.4437 and 2.5394, respectively, which indicates that skilled talents have a strong willingness to interact with each other and share knowledge. The construction of skill-based organizations should fully create a strong environment for talent exchange and focus on cultivating students’ willingness and ability to communicate in vocational education.

Beyond matchings: Dynamic multi-hop topology for demand-aware datacenters

State-of-the-art topologies for datacenters (DC) and high-performance computing (HPC) networks are demand-oblivious and static. Therefore, such network topologies are optimized for the worst-case traffic scenarios. However, recent optical circuit-switching technologies enable real-time dynamic topologies that change in microseconds or less. This capability opens the door for the design of self-adjusting networks: networks with demand-aware and reconfigurable topologies in which links can be re-adjusted online and in response to evolving traffic patterns.In this paper, we study self-adjusting networks using a recently proposed model of reconfigurable networks and present a novel algorithm, GreedyEgoTrees (GET), that dynamically changes the network topology. While previous algorithms used a local perspective, GET takes a global view and greedily builds ego-trees for nodes in the network, where nodes cooperate to help each other. In contrast to recent proposals, GET is optimized for multi-hop routing, and we show that it has nice theoretical guarantees as a function of the demand’s entropy. Empirical results also show that GET outperforms recently proposed algorithms (like static expander and greedy dynamic matching) and can significantly improve the average path length by up-to 65% for real DC, HPC, and other communication traces.

Effect of network size on comparing different stock networks

We analyzed complex networks generated by the threshold method in the Korean and Indian stock markets during the non-crisis period of 2004 and the crisis period of 2008, while varying the size of the system. To create the stock network, we randomly selected N stock indices from the market and constructed the network based on cross-correlation among the time series of stock prices. We computed the average shortest path length L and average clustering coefficient C for several ensembles of generated stock networks and found that both metrics are influenced by network size. Since L and C are affected by network size N, a direct comparison of graph measures between stock networks with different numbers of nodes could lead to erroneous conclusions. However, we observed that the dependency of network measures on N is significantly reduced when comparing larger networks with normalized shortest path lengths. Additionally, we discovered that the effect of network size on network measures during the crisis period is almost negligible compared to the non-crisis periods.

Changes in the Baltic Sea coastal food web: A case study on the invasion of Round goby Neogobius melanostomus (Pallas, 1814)

We used dynamic food web modeling to assess the trophic effects of the increased abundance of the invasive Round goby Neogobius melanostomus (Pallas, 1814) on network flows and trophic levels in the open coastal zone of the south-eastern Baltic Sea. Available data on biomass and diet changes were incorporated in the Ecopath with Ecosim model, which estimated non-available data. Flow changes were related to Round goby predation on Blue mussels Mytilus edulis (Linnaeus, 1758), as well as to its contribution to the diet of piscivorous groups. The model presented changes in Long-tailed duck Clangula hyemalis (Linnaeus, 1758) biomass and consumption and an increase in its trophic level. However, the results showed no changes in the trophic level of other compartments nor average path length, the trophic level of fishery catch or the average trophic level of the community. This demonstrates the network's ability to maintain flows within given input and retain stability under conditions of changes induced by an invasive species. In regards to validating the model results in the representation of the study, trophic levels estimated by the dynamic model strongly correlated to ones calculated from stable nitrogen isotope (δ15N) ratios of consumers.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p < 0.001), PFC increased (p < 0.001) and PCMI decreased (p < 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p < 0.001) and nodal efficiency (NE) (p < 0.001) decreased in these two spaces. Global network indicators normalized average path length () (p < 0.01) and modularity (Q) (p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient () and small-world index () did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicators and Q were not significantly different from the preoperative resting state in sensor space. Significance. GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

Exploring the molecular mechanism of cold-adaption of an alkaline protease mutant by molecular dynamics simulations and residue interaction network.

Psychrophilic proteases have attracted enormous attention in past decades, due to their high catalytic activity at low temperatures in a wide range of industrial processes, especially in the detergent and leather industries. Among them, H5 is an alkaline protease mutant, which featuring psychrophilic-like behavior, but the reasons that H5 with higher activity at low temperatures are still poorly understood. Herein, the molecular dynamics (MD) simulations combined with residue interaction network (RIN) were utilized to investigate the mechanisms of the cold-adaption of mutant H5. The results demonstrated that two loops involved in the substrate binding G100-S104 and S125-S129 in H5 had higher mobility, and the distance enlargement between the two loops modulated the substrate's accessibility compared with wild type counterpart. Besides, H5 enhanced conformational flexibility by weakening salt bridges and increasing interaction with the solvent. In particular, the absence of Lys251-Asp197-Arg247 salt bridge network may contribute to the structural mobility. Based on the free energy landscape and molecular mechanics Poisson-Boltzmann surface area of the wild type and H5, it was elucidated that H5 possessed a large population of interconvertible conformations, resulting in the weaker substrate binding free energy. The calculated RIN topology parameters such as the average degree, average cluster coefficient, and average path length further verified that the mutant H5 attenuated residue-to-residue interactions. The investigation of the mechanisms by which how the residue mutation affects the stability and activity of enzymes provides a theoretical basis for the development of cold-adapted protease.

Reforestation regulated soil bacterial community structure along vertical profiles in the Loess Plateau

IntroductionReforestation is a widely used strategy for ecological restoration in areas facing ecological degradation. Soil bacteria regulate many functional processes in terrestrial ecosystems; however, how they respond to reforestation processes in surface and deep soils remains unclear.MethodsArtificial Robinia pseudoacacia plantation with different stand ages (8, 22, and 32 years) in a typical fallow forest on the Loess Plateau was selected to explore the differential response of soil bacterial community to reforestation in different soil depths (surface 0–200 cm, middle 200–500 cm, and deep 500-100 cm). Soil bacterial diversity, community composition and the co-occurrence patterns, as well as the functions were analyzed.Results and discussionThe results showed that alpha diversity and the presence of biomarkers (keynote species) decreased with the increasing soil depth, with a sharp reduction in family-level biomarker numbers in 500–1,000 cm depth, while reforestation had a positive impact on bacterial alpha diversity and biomarkers. Reforestation induced a more loosely connected bacterial community, as evidenced by an increase of 9.38, 22.87, and 37.26% in the average path length of the co-occurrence network in all three soil layers, compared to farmland. In addition, reforestation reduced the hierarchy and complexity but increased the modularity of the co-occurrence network in top and deep soil layers. Reforestation also led to enrichment in the relative abundance of functional pathways in all soil layers. This study sheds light on the strategies employed by deep soil bacteria in response to reforestation and underscores the significant potential of deep soil bacteria in terrestrial ecosystems, particularly in the context of human-induced environmental changes.

Proximity in consumer network and company new products decisions

This paper examines the effects of proximity in the consumer network structure on the diffusion of firms' new products. We find that the average path length of network on new products diffusion is unrelated to consumer preferences. The average path length of consumer network decreases the efficiency of new product diffusion. But under the similar preferences of consumers, the trend of changes of consumers network average degrees and network rewiring probability are the same as the new products diffusion efficiency, and the results will be opposite under the differentiation preferences of consumers.

Effects of polyethylene microplastics and heavy metals on soil-plant microbial dynamics

Polyethylene (PE) microplastics are emerging pollutants that pose a significant threat to the environment and human health. However, little is known about the effects of PEs on soil‒plant interactions, especially in heavy metal (HM)-contaminated soil. In this study, the effects of PE on rhizosphere soil enzyme activities, microbial interactions and nutrient cycling processes were analyzed from ecological network and functional gene perspectives for the first time. The results indicated that PE-MP addition significantly reduced the biomass of Bidens pilosa L. In addition, the partial increase in carbon, nitrogen, and phosphorus enzyme activities suggested that the effects of PE as a carbon source on microbial functions in HM-contaminated soil should not be ignored. The average path length of bacterial network nodes was found to be higher than that of fungal network nodes, demonstrating that the bacterial ecological network in PE-MP and HM cocontaminated environments has good buffering capacity against changes in external environmental conditions. Furthermore, structural equation modeling demonstrated that particle size and dosage affect soil nutrient cycling processes and that cycling processes are acutely aware of changes in any factor, such as soil moisture, soil pH and soil nitrogen nutrients. Hence, PE-MP addition in HM-contaminated soil has the potential to alter soil ecological functions and nutrient cycles.

The impact of risk spillover network-based multidimensional systemic risks on the real economy

ABSTRACT Multidimensional systemic risk measures are constructed based on dynamic risk spillover networks to quantify the level, transmission speed, and transmission breadth. To investigate the significance, direction, magnitude, and persistence of the impacts of systemic risks on future real economic distribution, we combine the quantile regression model and Bootstrap quantile t test method. Finally, the heterogeneity of the impacts of systemic risks and SIFIs (systemically important financial institutions) risks is analysed. Our empirical results find that 1) most bank companies take the role of risk drivers in the financial system, while most security, insurance, trust, and real estate companies take the role of risk distributors and risk receivers. 2) the total spillover index, average shortest path length, and average clustering coefficient have significant impacts on the left tail and central tendency of future real economy, and the latter two measures also have significant impacts on the right tail. 3) SIFIs risks have earlier and greater impacts than Non-SIFIs (non-systemically important financial institutions) risks. Multidimensional systemic risks contain additional information to identify financial risks earlier than single institutions risks. 4) the impacts of financial risks on the real economy are persistent. The superimposition of impacts may trigger extreme risks.

Climate warming enhances microbial network complexity by increasing bacterial diversity and fungal interaction strength in litter decomposition

Microbial communities are important drivers of plant litter decomposition; however, the mechanisms of microbial co-occurrence networks and their network interaction dynamics in response to climate warming in wetlands remain unclear. Here, we conducted a 1.5-year warming experiment on the bacterial and fungal communities involved in litter decomposition in a typical wetland. The results showed that warming accelerated the decomposition of litter and had a greater effect on the diversity of bacteria than on that of fungi. Dominant bacterial communities, such as Bacteroidia, Alphaproteobacteria, and Actinobacteria, and dominant fungal communities, such as Leotiomycetes and Sordariomycetes, showed significant positive correlations with lignin and cellulose. Co-occurrence networks revealed that the average path length and betweenness centrality under warming conditions increased in the bacterial community but decreased in the fungal community. Both bacterial and fungal networks in the 2.0 °C warming treatment had the highest ratio of positive links (58.53 % and 98.14 %), indicating that moderate warming can promote the positive correlations and symbiotic relationships observed in the microbial community. This also suggests that small-world characteristics and weak-link advantages accelerate diffusion, and scale-free features facilitate propagation in microbial communities in response to climate warming. Logistic growth and Lotka-Volterra competition models revealed that climate warming enhances microbial network complexity mainly by increasing bacterial diversity and fungal interaction strength in litter decomposition. However, the symbiotic relationship decreased slightly under 4.0 °C warming, indicating that climate warming is a random attack rather than a targeted attack, and the microbial network has strong resistance to random attack, as shown by the highly robust dynamic performance of the microbial network in litter decomposition. Overall, the microbial community in litter decomposition responded to climate warming and shifted its network interactions, leading to further changes in emergent network topology and dynamics, thus accelerating litter decomposition in wetlands.

49 Leveraging Big Data Analytics to Improve Disease Surveillance and Welfare in Pig Production

Abstract Large amounts of data are routinely collected in pig production, including operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Furthermore, additional sources of information, such as demographic, economic, and weather variables, can be combined with farm data. Integrating and analyzing such data can generate valuable insights and data-driven decision-making tools for disease surveillance, animal welfare, and production system optimization. This presentation discusses two examples of big data integration and analysis in the context of optimizing management practices in pig production. The first example pertains to a data analytics project related to total transportation losses (TTL) of market-weight pigs, an important animal welfare concern that economically impacts producers and abattoirs. After merging and checking data for errors, the final dataset included 4.5M+ pigs from 420 farms loaded in 26,819 shipments delivered to two abattoirs. Results from a generalized additive mixed model (GAMM) technique showed that the risk of loss per shipment was constant for temperature-humidity index (THI) values between −10 and 10. Additionally, a strong interaction between wind speed and precipitation was associated with TTL. TTL were strongly associated with the type of driver and the distance traveled, in addition to weather variables. The second example pertains to swine disease surveillance, in which data from a multi-site system, where pigs are transported between farms after each particular production phase, were investigated. Reports of 76,566 shipments across sites were obtained and used to characterize pig movement flow, conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and assess the potential impact of each farm type on disease dissemination across the system. Topological properties of networks were investigated, such as the number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Overall, the results indicated that gilt development units, nursery, and sow farms have a more central role in the pig production hierarchical structure. As such, they are potential major factors in the introduction and spread of diseases in the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to infection. Additionally, the characteristics of a disease should have strong implications for biosecurity practices across production sites.