Small World Research Articles

Embodied carbon emission flow network analysis of the global nickel industry chain based on complex network

Nickel is an energy-intensive resource, and with the expansion of global demand for nickel, the embodied carbon emissions of its industrial chain in international trade are also increasing. The study of the embodied carbon emission flow of the global nickel industry chain can provide support for the low-carbon development and sustainable supply of nickel resources. Under the accounting framework of life cycle assessment, this study analyzes the embodied carbon emission flow of the global nickel industry chain from the perspective of network. The results show that the embodied carbon emission flow network (ECEFN) of all links of the global nickel industry chain presents small world nature. Some economies in the network have more flow relations, and the main flows carry 50.5 % to 92.2 % of the total embodied carbon emissions in each link. The ECEFN of the whole industrial chain is divided into four plates, of which plate C has the most significant spillover effect on the entire network. Key economies are identified through centrality and weight indicators, with China playing the most crucial role. Plate members should strengthen cooperation to achieve collaborative emission reduction. Differentiated and targeted mitigation measures are needed for key economies and main flow relationships.

Синхронизация в сетях фазовых осцилляторов с топологиями связей «кольцо» и «малый мир» при различных видах зависимости частоты осциллятора от его положения в сети

Синхронизация в сетях фазовых осцилляторов с топологиями связей «кольцо» и «малый мир» при различных видах зависимости частоты осциллятора от его положения в сети

Congestion control analysis of optical packet switch for optical data center applications

Abstract Optical data centers serve as the backbone of modern networking, facilitating seamless connectivity for users across the globe. The connection between users and the optical data centers is established through various network topologies, which play a critical role in determining the traffic characteristics. The design and implementation of these network topologies, along with the assortment of applications hosted on optical data centers, significantly influence the flow of data within the network. The advent of cloud computing has further revolutionized optical data center operations, leading to the coexistence of a wide range of applications on different optical data center switches. As a result, the traffic characteristics observed on each optical data center switch vary significantly. This diversity in traffic patterns necessitates a comprehensive understanding of how data arrival is managed and handled by the networking infrastructure. In this paper, we explore the concept of a random traffic model for data arrival on Top of Rack (ToR) switches, which represent a crucial component of optical data center networking. In the modeling, small world model is considered. The effect of buffering and packet priorities is observed on traffic shaping. Finally, to evaluate the effectiveness of the traffic shaping techniques, we measure the packet loss performance of ToR switches and found to be as low as 10−4 even at the higher loads. Blocking performance provides valuable insights into how effectively the optical data center network manages incoming data and avoids congestion or bottlenecks.

Host phylogeny shapes viral transmission networks in an island ecosystem

Virus transmission between host species underpins disease emergence. Both host phylogenetic relatedness and aspects of their ecology, such as species interactions and predator–prey relationships, may govern rates and patterns of cross-species virus transmission and hence zoonotic risk. To address the impact of host phylogeny and ecology on virus diversity and evolution, we characterized the virome structure of a relatively isolated island ecological community in Fiordland, New Zealand, that are linked through a food web. We show that phylogenetic barriers that inhibited cross-species virus transmission occurred at the level of host phyla (between the Chordata, Arthropoda and Streptophyta) as well as at lower taxonomic levels. By contrast, host ecology, manifest as predator–prey interactions and diet, had a smaller influence on virome composition, especially at higher taxonomic levels. The virus–host community comprised a ‘small world’ network, in which hosts with a high diversity of viruses were more likely to acquire new viruses, and generalist viruses that infect multiple hosts were more likely to infect additional species compared to host specialist viruses. Such a highly connected ecological community increases the likelihood of cross-species virus transmission, particularly among closely related species, and suggests that host generalist viruses present the greatest risk of disease emergence.

Effect of tDCS on corticomuscular coupling and the brain functional network of stroke patients.

Transcranial direct current stimulation (tDCS) is an emerging brain intervention technique that has gained growing attention in recent years in the rehabilitation area. In this paper, we investigated the efficacy of tDCS in the rehabilitation process of stroke patients, utilizing corticomuscular coupling (CMC) and brain functional network analysis. Specifically, we examined changes in CMC relationships between the treatment and control groups before and after rehabilitation by transfer entropy (TE), and constructed brain functional networks by TE. We further calculated features of the functional networks, including node degree, global efficiency, clustering coefficient, characteristic path length, and small world index. Our results demonstrate that CMC in patients increased significantly after treatment, with greater improvements in the tDCS group, particularly within the beta and gamma bands. In addition, the functional brain network analysis revealed enhanced connectivity between brain regions, improved information processing capacity, and increased transmission efficiency in patients as their condition improved. Notably, treatment with tDCS resulted in more significant improvements than the sham group, with a statistically significant difference observed after rehabilitation treatment (p < 0.05). These findings provide compelling evidence regarding the role of tDCS in the treatment of stroke and highlight the potential of this approach in stroke rehabilitation. The use of tDCS for therapeutic interventions in stroke rehabilitation can significantly improve the coupling of patients' functional brain networks. Also, using Transfer Entropy (TE) as a characteristic of CMC, tDCS was found to significantly enhance patients' TE, i.e. enhanced CMC.

Poster Session: Laminar and cell class distinctions for pre-saccadic attention in marmoset MT/MTC.

Pre-saccadic attention has been related to enhanced neural responses before saccades made into a neuron's receptive field in macaque visual cortex (Moore and Chang 2009). However, much remains unknown about the underlying circuit mechanisms. Using the marmoset, a small New World monkey with a smooth brain, we examined laminar and cell class distinctions during pre-saccadic attention in motion selective areas MT/MTC. In a saccade foraging task, marmosets made a saccade from a central fixation point to one of three equally eccentric random dot field stimuli. We positioned the stimuli such that one foraged location overlapped the receptive fields of neurons under study and examined how tuning functions for motion direction changed. Tuning curves were fit with an adjusted Von Mises curve that estimates baseline, gain, and tuning width. We found in two animals that neurons on average exhibited increases in baseline and gain with pre-saccadic attention, but no changes in tuning width. In a single animal we were able to dissect the population by cell class and layer. We found that increases in gain were predominantly among broad spiking neurons in superficial layers whereas additive increases in rate were shared across layers and cell types. This suggests that superficial layer broad spiking neurons, the putative projection neurons that would relay information to downstream cortical areas, have a privileged role for encoding enhanced motion sensitivity during pre-saccadic attention.

Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm.

The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike in-utero fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored. From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex. A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r2Adj range 0.143-0.401, p < 0048), with C and LE exhibited trending increases with age. This is the first direct investigation on the extra-uterine formation of functional brain architecture in preterm infants. Importantly, our results suggest that changes in functional architecture with increasing age exhibit a different trajectory relative to in utero fetus. Instead, they exhibit developmental changes more similar to the early postnatal period in term born infants.

Liquid-Liquid Interfacial Nanoarchitectonics.

Science in the small world has become a crucial key that has the potential to revolutionize materials technology. This trend is embodied in the postnanotechnology concept of nanoarchitectonics. The goal of nanoarchitectonics is to create bio-like functional structures, in which self-organized and hierarchical structures are working efficiently. Liquid-liquid interface like environments such as cell membrane surface are indispensable for the expression of biological functions through the accumulation and organization of functional materials. From this viewpoint, it is necessary to reconsider the liquid-liquid interface as a medium where nanoarchitectonics can play an active role. In this review, liquid-liquid interfacial nanoarchitectonics is classified by component materials such as organic, inorganic, carbon, and bio, and recent research examples are discussed. Examples discussed in this paper include molecular aggregates, supramolecular polymers, conductive polymers film, crystal-like capsules, block copolymer assemblies, covalent organic framework (COF) films, complex crystals, inorganic nanosheets, colloidosomes, fullerene assemblies, all-carbon π-conjugated graphite nanosheets, carbon nanoskins and fullerphene thin films at liquid-liquid interfaces. Furthermore, at the liquid-liquid interface using perfluorocarbons and aqueous phases, cell differentiation controls are discussed with the self-assembled structure of biomaterials. The significance of liquid-liquid interfacial nanoarchitectonics in the future development of materials will then be discussed.

Science in Art Networks. How art and education intertwine in the project “Rhizosphere: The Big Network of Small Worlds”

Science in Art Networks. How art and education intertwine in the project “Rhizosphere: The Big Network of Small Worlds”

Functional network structure supports resilience to memory deficits in cognitively normal older adults with amyloid-β pathology

Older adults may harbor large amounts of amyloid-β (Aβ) pathology, yet still perform at age-normal levels on memory assessments. We tested whether functional brain networks confer resilience or compensatory mechanisms to support memory in the face of Aβ pathology. Sixty-five cognitively normal older adults received high-resolution resting state fMRI to assess functional networks, 18F-florbetapir-PET to measure Aβ, and a memory assessment. We characterized functional networks with graph metrics of local efficiency (information transfer), modularity (specialization of functional modules), and small worldness (balance of integration and segregation). There was no difference in functional network measures between older adults with high Aβ (Aβ+) compared to those with no/low Aβ (Aβ−). However, in Aβ+ older adults, increased local efficiency, modularity, and small worldness were associated with better memory performance, while this relationship did not occur Aβ− older adults. Further, the association between increased local efficiency and better memory performance in Aβ+ older adults was localized to local efficiency of the default mode network and hippocampus, regions vulnerable to Aβ and involved in memory processing. Our results suggest functional networks with modular and efficient structures are associated with resilience to Aβ pathology, providing a functional target for intervention.

Fluid and particle retention in a small New World and a small Old World cervid, the southern pudu (Pudu puda) and Reeves's muntjac (Muntiacus reevesi)

Ruminants differ in the pattern how small particles and liquids pass through their gastrointestinal tract, and in particular their reticulorumen (RR). Based on that they may be classified into ‘moose-type’ and ‘cattle-type’ species (smaller and larger differences between particle and liquid passage, respectively). The ratio between the retention of particles and fluids is called the ‘selectivity factor’ (SF) and is a species-specific characteristic, studied in tragulids, giraffids and bovids, but not in many cervid species. Recently, it has been suggested that a high SF might also serve to wash digesta clean of external abrasives prior to regurgitation for rumination. In this study, we measured SF and passage kinetics (using a liquid marker and markers of different particle size, fed with the diet) in a capreoline deer, the southern pudu (Pudu puda, n = 5, 10.3 ± 2.9 kg, kept at two zoos) and a cervine deer, the Reeves's muntjac (Muntiacus reevesi, n = 6, 11.0 ± 1.7 kg, kept at a research facility). The relative daily dry matter intake (38 ± 3 g/kg0.75 for pudu and 76 ± 5 g/kg0.75 for muntjac) was higher, and the mean retention times (MRT) correspondingly shorter (e.g., MRT small particles in the total digestive tract 39 ± 8 h for pudu and 15 ± 2 h for muntjac), in the muntjac. The SF for small particles/liquid in the reticulorumen were, however, similar for both species, at 1.47 ± 0.21 for pudu and 1.66 ± 0.20 for muntjac, indicating a ‘moose-type’ physiology for both, irrespective of their different phylogenetic origin. To date, SF recorded in bovids attain distinctively higher values than the few reported for cervids. This situation reflects the degree of hypsodonty (tooth crown height) attained by these taxa, which is higher in bovids than in cervids. Together, constraints in hypsodonty as well as SF might limit cervids to more mesic habitats without distinct loads of external abrasives (such as dust or grit) on their food. In both species, some animals showed the typical ruminant pattern of a longer MRT for large than for small particle markers, but in some animals, this difference was not evident. This may be due to variable degrees of marker chewing during ingestion.

Towards hypergraph cognitive networks as feature-rich models of knowledge

Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading “airplane” makes one think of “air” and “pollution”, and this is represented by links “airplane”-“air” and “airplane”-“pollution”). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N= 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.

Graph network measures reveal distinct white matter abnormalities in motor and extra-motor brain regions of two UMN-predominant ALS subtypes

BackgroundRoutine clinical magnetic resonance imaging (MRI) shows bilateral corticospinal tract (CST) hyperintensity in some patients with upper motor neuron (UMN)-predominant ALS (ALS-CST+) but not in others (ALS-CST–). Although, similar in their UMN features, the ALS-CST+ patient group is significantly younger in age, has faster disease progression and shorter survival than the ALS-CST– patient group. Reasons for the differences are unclear. MethodIn order to evaluate more objective MRI measures of these ALS subgroups, we used diffusion tensor images (DTI) obtained using single shot echo planar imaging sequence from 1.5 T Siemens MRI Scanner. We performed an exploratory whole brain white matter (WM) network analysis using graph theory approach on 45 ALS patients (ALS-CST+) (n = 21), and (ALS-CST–) (n = 24) and neurological controls (n = 14). ResultsSignificant (p < 0.05) differences in nodal degree measure between ALS patients and controls were observed in motor and extra motor regions, supplementary motor area, subcortical WM regions, cerebellum and vermis. Importantly, WM network abnormalities were significantly (p < 0.05) different between ALS-CST+ and ALS-CST– subgroups. Compared to neurologic controls, both ALS subgroups showed hubs in the right superior occipital gyrus and cuneus as well as significantly (p < 0.05) reduced small worldness supportive of WM network damage. ConclusionsSignificant differences between ALS-CST+ and ALS-CST– subgroups of WM network abnormalities, age of onset, symptom duration prior to MRI, and progression rate suggest these patients represent distinct clinical phenotypes and possibly pathophysiologic mechanisms of ALS.

Common Marmoset Cell Lines and Their Applications in Biomedical Research.

Common marmosets (Callithrix jacchus; CMs) are small New World primates widely used in biomedical research. Early stages of such research often include in vitro experiments which require standardized and well-characterized CM cell cultures derived from different tissues. Despite the long history of laboratory work with CMs and high translational potential of such studies, the number of available standardized, well-defined, stable, and validated CM cell lines is still small. While primary cells and immortalized cell lines are mostly used for the studies of infectious diseases, biochemical research, and targeted gene therapy, the main current applications of CM embryonic stem cells and induced pluripotent stem cells are regenerative medicine, stem cell research, generation of transgenic CMs, transplantology, cell therapy, reproductive physiology, oncology, and neurodegenerative diseases. In this review we summarize the data on the main advantages, drawbacks and research applications of CM cell lines published to date including primary cells, immortalized cell lines, lymphoblastoid cell lines, embryonic stem cells, and induced pluripotent stem cells.

Perfect synchronization in complex networks with higher-order interactions.

Achieving perfect synchronization in a complex network, specially in the presence of higher-order interactions (HOIs) at a targeted point in the parameter space, is an interesting, yet challenging task. Here we present a theoretical framework to achieve the same under the paradigm of the Sakaguchi-Kuramoto (SK) model. We analytically derive a frequency set to achieve perfect synchrony at some desired point in a complex network of SK oscillators with higher-order interactions. Considering the SK model with HOIs on top of the scale-free, random, and small world networks, we perform extensive numerical simulations to verify the proposed theory. Numerical simulations show that the analytically derived frequency set not only provides stable perfect synchronization in the network at a desired point but also proves to be very effective in achieving a high level of synchronization around it compared to the other choices of frequency sets. The stability and the robustness of the perfect synchronization state of the system are determined using the low-dimensional reduction of the network and by introducing a Gaussian noise around the derived frequency set, respectively.

Disrupted Small-World Networks in Children with Drug-Naïve Attention-Deficit/Hyperactivity Disorder: A DTI-Based Network Analysis

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, while the potential neurological mechanisms are poorly understood. To explore the alterations in the white matter (WM) structural connectome in children with drug-naïve ADHD, forty-nine ADHD and 51 age- and gender-matched typically developing (TD) children aged 6–14 years were enrolled. WM structural connectivity based on deterministic diffusion tensor imaging (DTI) was constructed in 90 cortical and subcortical regions, and topological parameters of the resulting graphs were calculated. Network metrics were compared between two groups. The concentration index and the total cancellation test scores of digit cancellation test were used to evaluate clinical symptom severity in ADHD. Then, a partial correlation analysis was performed to explore the relationship between significant topologic metrics and clinical symptom severity. Compared to TD group, ADHD showed an increase in the characteristic path length (Lp), normalized clustering coefficient (γ), small worldness (σ), and a decrease in the global efficiency (Eglob) (all p < 0.05). Furthermore, ADHD showed reduced nodal centralities mainly in the regions of default mode network (DMN), central executive network (CEN), basal ganglia, and bilateral thalamus (all p < 0.05). After performing Benjamini-Hochberg’s procedure, only the left orbital part of superior frontal gyrus and the left caudate were statistically significant (p < 0.05, FDR-corrected). In addition, the concentration index of ADHD was negatively correlated with the nodal betweenness of the left orbital part of the middle frontal gyrus (r = −0.302, p = 0.042). Our findings revealed an ADHD-related shift of WM network topology toward “regularization” pattern, characterized by decreased global network integration, which is also reflected by changed nodal centralities involving DMN, CEN, basal ganglia, and bilateral thalamus. ADHD could be understood by examining the dysfunction of large-scale spatially distributed neural networks.

How does the global metal industry value network affect industrial structure upgrading ? The perspective of industry chain

The global metal industry value network (GMIVN) can reveal the status of an economy in the global metal industry chain and value chain, while the traditional metal trade networks cannot deeply reflect the global metal industry pattern and the competitive capacity of an economy's metal industry. Moreover, the GMIVN also performs an irreplaceable role in the process of the industrial structure upgrading. This paper uses network analysis and panel regression to investigate the characteristics of the GMIVN and its influence on the upgrading of industrial structure in the ways of the industry chain. The main findings are as follows: (1) The GMIVN has always been compacting shows “small world” characteristics. The network structure features of the value network of the middle and lower industry are highly consistent with those of GMIVN. (2) The distribution pattern of the core economies in the value network of the global metal industry is stable and concentrated. The U.S., Germany, China, and the U.K. exert significant impact in all links of the global metal industry chain and value chain. However, major metal resource exporters are at a lower position in the global value network of the metal industry and exert limited influence. (3) The higher the central and independent status of an economy in global metal trade or the more high-influence trading partners it has, the more advanced its industrial structure will be. Furthermore, the centrality of the value network of the middle and lower industry exerts a larger influence on the upgrading of industrial structure than that of the value network of the upper industry.

An Efficient Online Loop Closure Detection System With Local Spatial Co-Occurrence Information

As a vital component in simultaneous localization and mapping techniques, appearance-based loop closure detection (LCD) plays important roles in bounding the long-term drift errors. In this paper, an online LCD system based on the mutual co-occurrence information among visual features is proposed. First, a feature tracker module is designed to generate distinctive visual words, exploiting tracked words tool to improve efficiency. Then an incrementally built vocabulary is organized by a hierarchical navigable small world graph, where the visual words are indexed. To merge a homologous word into the existing one, the vocabulary applies an improved high dimensional online clustering method, which regards individual cluster as a normal distribution form. At the query phase, a list of candidate frames is located due to the co-occurrence constraint. Ultimately, the loop closure is specified by passing the temporal and similarity check, which avoids the memory consumption of historic image data. Validation tests based on public datasets and experimental sequence demonstrate the merit of low running time and memory cost, while the high-precision performance is retained in the system.

Learning by Hiring Or Hiring to Impede Learning? Small World Network and Learning after Hiring

Learning by Hiring Or Hiring to Impede Learning? Small World Network and Learning after Hiring

Evolutionary public good games based on the long-term payoff mechanism in heterogeneous networks

Over the past two decades, it has been of great concern to solve the puzzle of social dilemmas in spatial public goods games. It has been demonstrated that the introduction of memory effects can effectively promote cooperative emergence to some extent. In this paper, we focus on the impact of scale-free and high aggregation properties on the public goods game of memory heterogeneous groups. Here, we propose an optimal strategy selection mechanism based on long-term historical payoffs. In our model, a memory loss function ω is introduced to characterize the memory length of players, and accordingly, pairs determine the optimal strategy by comparing historical gains for the next game cycle. Experiments conducted on scale-free networks and small world networks show that the strategy selection mechanism based on long-term payoff can keep the proportion of cooperators at a high level. In addition, this study demonstrates that the heterogeneity of memory length in the network promotes the enhancement of network reciprocity and robustness, which is beneficial to further reveal the mechanism of cooperative behaviour emergence in complex networks.