Elevated emotion network connectivity is associated with fluctuations in depression.

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

Jerne's "immune network theory", of interacting anti-idiotypic antibodies applied to immune responses during COVID-19 infection and after COVID-19 vaccination.

Niels Kaj Jerne has proposed the "immune network theory" of interactions among anti-idiotypic antibodies, able to interfere with humoral responses to certain antigens. After the occurrence of the primary generation of antibodies, against an antigenic epitope, idiotypes of these antibodies induce anti-idiotypic antibodies that modulate the intensity of the first response, and so on. Adverse effects following SARS-COV-2 COVID-19 vaccines are occasionally similar to the symptoms of COVID-19 infection. Rare events linked to SARS-CoV-2 vaccines also resemble some rarely reported COVID-19 complications. Safety data from product information by European Medicines Agency suggest that spectra do overlap for four main vaccines. The proposition is that vaccine events and COVID-19 complications are related to anti-idiotypic antibodies whose spatial shape can lead to interactions with ACE2 molecules, in individuals with a prolonged Spike protein synthesis. The vaccines target cells by their affinity to the vaccine vector, or to engulf lipid nanoparticles. Anti-idiotypic antibodies shaped similarly to the Spike protein possibly interact with ACE2 molecules and cause diverse signs and symptoms.

Design principles of PI(4,5)P2 clustering under protein-free conditions: Specific cation effects and calcium-potassium synergy.

Phosphatidylinositol 4,5-bisphosphate (PIP2) clustering is a key component in cell signaling, yet little is known about the atomic-level features of this phenomenon. Network-theoretic analysis of multimicrosecond atomistic simulations of PIP2 containing asymmetric bilayers under protein-free conditions, presented here, reveals how design principles of PIP2 clustering are determined by the specific cation effects. Ca2+ generates large clusters (6% are pentamer or larger) by adding existing PIP2 dimers formed by strong O‒Ca2+‒O bridging interactions of unprotonated P4/P5 phosphates. In contrast, monovalent cations (Na+ and K+) form smaller and less-stable clusters by preferentially adding PIP2 monomers. Despite having the same net charge, the affinity to P4/P5 is higher for Na+, while affinity toward glycerol P1 is higher for K+. Consequently, a mixture of K+ and Ca2+ (as would be produced by Ca2+ influx) synergistically yields larger and more stable clusters than Ca2+ alone due to the different binding preferences of these cations.

An inclusive anatomical network analysis of human craniocerebral topology.

The human brain's complex morphology is spatially constrained by numerous intrinsic and extrinsic physical interactions. Spatial constraints help to identify the source of morphological variability and can be investigated by employing anatomical network analysis. Here, a model of human craniocerebral topology is presented, based on the bony elements of the skull at birth and a previously designed model of the brain. The goal was to investigate the topological components fundamental to the craniocerebral geometric balance, to identify underlying phenotypic patterns of spatial arrangement, and to understand how these patterns might have influenced the evolution of human brain morphology. Analysis of the craniocerebral network model revealed that the combined structure of the body and lesser wings of the sphenoid bone, the parahippocampal gyrus, and the parietal and ethmoid bones are susceptible to sustain and apply major spatial constraints that are likely to limit or channel their morphological evolution. The results also showcase a high level of global integration and efficient diffusion of biomechanical forces across the craniocerebral system, a fundamental aspect of morphological variability in terms of plasticity. Finally, community detection in the craniocerebral system highlights the concurrence of a longitudinal and a vertical modular partition. The former reflects the distinct morphogenetic environments of the three endocranial fossae, while the latter corresponds to those of the basicranium and calvaria.

Centrality statistics of symptom networks of schizophrenia: a systematic review.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Biodiversity conservation, human-animal interactions, and zootherapy in ecological knowledge of Indonesian Healers.

We examined the entanglement of biodiversity conservation, human-animal interactions, zootherapy, and local beliefs among Sumatran Healers and their local community by completing an ethnography of 43 Indigenous Healers across 8 tribes in Bengkulu Province, Sumatra, Indonesia. Data collection tools were interviews, observations, videos, photographs, and a researcher journal. Of the 43 Healers, 30 used animals and mentioned 62 species. Of the animals identified, the International Union for Conservation of Nature Red List lists 34% (n = 21) as endangered, decreasing, or vulnerable, including Sumatran tiger (Panthera tigris sumatrae), Sumatran elephant (Elephas maximus sumatranus), and Sumatran rhinoceros (Dicerorhinus sumatrensis). Of the 30 Healers using animals, 50% (n = 15) practiced healing with at least one endangered, decreasing, or vulnerable animal. We defined 3 personas: Healer self-persona, Healer-imposed persona, and community-imposed persona. A persona represented a group's opinions and sentiments related to Healers killing animals for medicinal purposes. Using an iterative data analysis process, we grouped the data across the 3 personas into 5 themes: ease of killing and preparing animals, emotions related to killing animals, animal value, relationship to religion, and Healers are tricksters. The complexity of merging the identities of Healers and the community within an actor-network embodies the relationality of actions, interactions, and feelings among Healers, between Healers and animals, and between Healers and the community. Conservationists should be cognizant of Healers' medicinal use of animals, views of human-animal interactions, and zootherapy from all social and emotional perspectives. The data led to defining Indigenous Healer ecological knowledge components of zootherapy, human-animal interactions, and biodiversity conservation.

Conservación de la biodiversidad, interacciones humano­fauna y zooterapia dentro del conocimiento ecológico de los curanderos indonesios Resumen Analizamos la complejidad de la conservación de la biodiversidad, las interacciones humano­fauna, la zooterapia y las creencias locales de los curanderos y su comunidad local mediante una etnografía de 43 curanderos indígenas de ocho tribus en la provincia de Bengkulu en Sumatra, Indonesia. Usamos entrevistas, observaciones, videos, fotografías y una bitácora de investigador como herramientas de recolección de datos. De los 43 curanderos, 30 usaban animales y mencionaron 62 especies. El 34% (n = 21) de los animales identificados están catalogados como en peligro, en disminución o vulnerables en la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza, incluidos el tigre de Sumatra (Panthera tigris sumatrae), el elefante de Sumatra (Elephas maximus sumatranus) y el rinoceronte de Sumatra (Dicerorhinus sumatrensis). De los 30 curanderos que usan animales, el 50% (n = 15) practica la sanación con al menos una especie en peligro, en disminución o vulnerable. Definimos tres percepciones: autopercepción de curandero, percepción impuesta de curandero y percepción impuesta de comunidad. Cada percepción representó las opiniones y sentimientos de un grupo con respecto a la matanza de animales por cuestiones medicinales. Usamos un proceso de análisis de datos repetitivos para agrupar la información de las tres percepciones en tres temas: facilidad para matar y preparar animales, emociones relacionadas con matar animales, valor del animal, relación con la religión y los curanderos son estafadores. La complejidad de combinar las identidades de los curanderos dentro de una red de actores representa cómo se relacionan las acciones, interacciones y sentimientos dentro de la comunidad de curanderos, entre los curanderos y los animales y entre los curanderos y la comunidad. Los conservacionistas deben ser conscientes desde todas las perspectivas sociales y emocionales del uso medicinal que los curanderos dan a los animales, las percepciones sobre las interacciones humano­fauna y la zooterapia. Esta información nos llevó a definir los componentes del conocimiento ecológico de los curanderos indígenas en materia de zooterapia, interacciones humano­fauna y conservación de la biodiversidad.

A network model of legal relations.

From at least the early twentieth century, legal scholars have recognized that rights and other legal relations inhere between individual legal actors, forming a vast and complex social network. Yet, no legal scholar has used the mathematical machinery of network theory to formalize these relationships. Here, we propose the first such approach by modelling a rudimentary, static set of real property relations using network theory. Then, we apply our toy model to measure the level of modularity-essentially, the community structure-among aggregations of these real property relations and associated actors. In so doing, we show that even for a very basic set of relations and actors, law may employ modular structures to manage complexity. Property, torts, contracts, intellectual property, and other areas of the law arguably reduce information costs in similar, quantifiable ways by chopping up the world of interactions between parties into manageable modules that are semi-autonomous. We also posit that our network science approach to jurisprudential issues can be adapted to quantify many other important aspects of legal systems. This article is part of the theme issue 'A complexity science approach to law and governance'.

A network analysis of exhaustion disorder symptoms throughout treatment.

BACKGROUND: Stress-induced Exhaustion Disorder (ED) is associated with work absenteeism and adverse health outcomes. Currently, little is known regarding how the symptoms of ED are interrelated and whether the patterns of symptoms influence treatment outcomes. To this end, the current study applied network analyses on ED patients participating in a multimodal intervention. METHODS: The first aim of the study was to explore the internal relationships between exhaustion symptoms and identify symptoms that were more closely related than others. A second aim was to examine whether the baseline symptom network of non-responders to treatment was more closely connected than the baseline symptom networks of responders, by comparing the sum of all absolute partial correlations in the respective groups' symptom network. This comparison was made based on the hypothesis that a more closely connected symptom network before treatment could indicate poorer treatment outcomes. Network models were constructed based on self-rated ED symptoms in a large sample of patients (n = 915) participating in a 24-week multimodal treatment program with a 12-month follow-up. RESULTS: The internal relations between self-rated exhaustion symptoms were stable over time despite markedly decreased symptom levels throughout participation in treatment. Symptoms of limited mental stamina and negative emotional reactions to demands were consistently found to be the most closely related to other ED symptoms. Meanwhile, sleep quality and irritability were weakly related to other exhaustion symptoms. The symptom network for the full sample became significantly more closely connected from baseline to the end of treatment and 12-month follow-up. The symptom network of non-responders to treatment was not found to be more closely connected than the symptom network of responders at baseline. CONCLUSIONS: The results of the current study suggest symptoms of limited mental stamina and negative emotional reactions to demands are central ED symptoms throughout treatment, while symptoms of irritability and sleep quality seem to have a weak relation to other symptoms of ED. The implications of these findings are discussed in relation to the conceptualization, assessment, and treatment of ED. TRIAL REGISTRATION: The clinical trial was registered on Clinicaltrials.gov 2017-12-02 (Identifier: NCT03360136).

Graph-theoretical chirality measure and chirality-property relations for chemical structures with multiscale mirror asymmetries.

Chirality is an essential geometric property unifying small molecules, biological macromolecules, inorganic nanomaterials, biological microparticles, and many other chemical structures. Numerous chirality measures have attempted to quantify this geometric property of mirror asymmetry and to correlate these measures with physical and chemical properties. However, their utility has been widely limited because these correlations have been largely notional. Furthermore, chirality measures also require prohibitively demanding computations, especially for chiral structures comprised of thousands of atoms. Acknowledging the fundamental problems with quantification of mirror asymmetry, including the ambiguity of sign-variable pseudoscalar chirality measures, we revisit this subject because of the significance of quantifying chirality for quantitative biomimetics and describing the chirality of nanoscale materials that display chirality continuum and scale-dependent mirror asymmetry. We apply the concept of torsion within the framework of differential geometry to the graph theoretical representation of chiral molecules and nanostructures to address some of the fundamental problems and practical limitations of other chirality measures. Chiral gold clusters and other chiral structures are used as models to elaborate a graph-theoretical chirality (GTC) measure, demonstrating its applicability to chiral materials with different degrees of chirality at different scales. For specific cases, we show that GTC provides an adequate description of both the sign and magnitude of mirror asymmetry. The direct correlations with macroscopic properties, such as chiroptical spectra, are enhanced by using the hybrid chirality measures combining parameters from discrete mathematics and physics. Taking molecular helices as an example, we established a direct relation between GTC and optical activity, indicating that this chirality measure can be applied to chiral metamaterials and complex chiral constructs.

Network ecology: Tie fitness in social context(s).

Social relations are embedded in material, cultural, and institutional settings that affect network dynamics and the resulting topologies. For example, romantic entanglements are subject to social and cultural norms, interfirm alliances are constrained by country-specific legislation, and adolescent friendships are conditioned by classroom settings and neighborhood effects. In short, social contexts shape social relations and the networks they give rise to. However, how and when they do so remain to be established. This paper presents network ecology as a general framework for identifying how the proximal environment shapes social networks by focusing interactions and social relations, and how these interactions and relations in turn shape the environment in which social networks form. Tie fitness is introduced as a metric that quantifies how well particular dyadic social relations would align with the setting. Using longitudinal networks collected on two cohorts each in 18 North American schools, i.e., 36 settings, we develop five generalizable observations about the time-varying fitness of adolescent friendship. Across all 252 analyzed networks, tie fitness predicted new tie formation, tie longevity, and tie survival. Dormant fit ties cluster in relational niches, thereby establishing a resource base for social identities competing for increased representation in the relational system.

Emergent dual scaling of riverine biodiversity.

A prevailing paradigm suggests that species richness increases with area in a decelerating way. This ubiquitous power law scaling, the species-area relationship, has formed the foundation of many conservation strategies. In spatially complex ecosystems, however, the area may not be the sole dimension to scale biodiversity patterns because the scale-invariant complexity of fractal ecosystem structure may drive ecological dynamics in space. Here, we use theory and analysis of extensive fish community data from two distinct geographic regions to show that riverine biodiversity follows a robust scaling law along the two orthogonal dimensions of ecosystem size and complexity (i.e., the dual scaling law). In river networks, the recurrent merging of various tributaries forms fractal branching systems, where the prevalence of branching (ecosystem complexity) represents a macroscale control of the ecosystem's habitat heterogeneity. In the meantime, ecosystem size dictates metacommunity size and total habitat diversity, two factors regulating biodiversity in nature. Our theory predicted that, regardless of simulated species' traits, larger and more branched "complex" networks support greater species richness due to increased space and environmental heterogeneity. The relationships were linear on logarithmic axes, indicating power law scaling by ecosystem size and complexity. In support of this theoretical prediction, the power laws have consistently emerged in riverine fish communities across the study regions (Hokkaido Island in Japan and the midwestern United States) despite hosting different fauna with distinct evolutionary histories. The emergence of dual scaling law may be a pervasive property of branching networks with important implications for biodiversity conservation.

Network-based forecasting of climate phenomena.

Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling.

Opening up conversations: Collaborative working across sociomaterial contexts in nursing in London.

AIM: To discuss nurses' use of networks to address nursing recruitment and retention in London, UK. DESIGN: Qualitative evaluation of the Capital Nurse programme reporting on 30 narrative interviews with executive, clinical and student nurses in 2019. RESULTS: Executive nurses within the Capital Nurse programme recognized the importance of sociomaterial contexts in the health and social care system in London and worked strategically across these contexts to achieve change. Supported through the Capital Nurse programme, executive nurses from health organizations across London initiated collaborative working to improve recruitment and retention. Primarily by designing and delivering sociomaterial products (organizational and educational) to support nurses to build a career in London. Drawing on ideas from actor network theory, in particular sociomaterial contexts, nurses' actions at all levels to develop and sustain networks to address nursing recruitment and retention across the NHS in London are described. CONCLUSIONS: Capital Nurse supported collaborative working both within single organizations and across organizations in London. There is evidence of change in how nurses across the capital work together to improve patient care, improve recruitment and retention. Findings may resonate with nurses in other settings who seek to address the problem of recruitment and retention. They show how nurses coming together in networks to effect changes in practice can work successfully. IMPACT: Nurses' use of networks led to novel models of communication and action to address the problems of recruitment and retention in London. We argue that sociomateriality should be considered outside the clinical practice setting, as part of nurses' professional development and organizational practice, that is how they plan their career, how they address recruitment and retention, how they communicate across organizations about nursing issues. NO PATIENT OR PUBLIC CONTRIBUTION: This was an evaluation of a staff development project in London, which sought to elicit nurses' experiences of participation in Capital Nurse.

A novel longitudinal connectivity index to evaluate reticular river networks based on the combination of network maximum flow and resistance distance.

Reticular river networks, essential for ecosystems and hydrology, pose challenges in assessing longitudinal connectivity due to complex multi-path structures and variable flows, exacerbated by human-made infrastructures like sluices. Existing tools inadequately track water flow's spatiotemporal changes, highlighting the need for targeted methods to gauge connectivity within complex river network systems. The Hydraulic Capacity Connectivity Index (HCCI) was developed adopting complex network theory. This involves river networks mapping, nodes and edges construstion, weight factor definition, maximum flow and resistance distance calculation. The connectivity between nodes is represented by the product of the maximum flow and the inverse of the resistance distance. The mean connectivity of each node with all other nodes, denoted as the node connectivity capacity Ci, and the HCCI of the whole river network is defined as the mean of the Ci for all nodes. The HCCI was firstly applied to a symmetrical virtual river network to investigate the factors influencing the HCCI. The results revealed that Ci showed a radial decreasing pattern from the obstructed river reach outwards, and the boundary rivers play the most significant role in regulating the flow dynamics. Subsequently, the HCCI was applied to a real river network in the Yandu district, followed by spatiotemporal statistical analysis comparing with 1D hydraulic model's simulated river discharge. Results showed a high correlation (Pearson coefficient of 0.89) between the HCCI and monthly average river discharge at the global scale. At the local scale, the geographically weighted regression model demonstrated the strong explanatory power of Ci in predicting the distribution of river reach discharge. This suggests that the HCCI addresses multi-path connectivity assessment challenge in reticular river networks, precisely characterizing spatiotemporal flow dynamics. Furthermore, since HCCI is based on a complex network model that can calculate the connectivity between all river node pairs, it is theoretically applicable to other types of river networks, such as dendritic river networks. By identifying low-connectivity areas, HCCI can guide managers in developing scientifically sound and effective strategies for restoring river network hydrodynamics. This can help prevent water stagnation and degradation of water quality, which is beneficial for environmental protection and water resource management.

Reassembling nursing in the digital age: An actor-network theory perspective.

This article explores the application of actor-network theory (ANT) to the nursing profession, proposing a novel perspective in understanding nursing in the context of modern digital healthcare. Traditional grand nursing theories, while foundational, often fail to encapsulate the dynamic and complex nature of nursing, particularly in an era of rapid technological advancements and shifting societal dynamics. ANT, with its emphasis on the relationships between human and nonhuman actors, offers a framework to understand nursing beyond traditional paradigms. This article makes two key arguments: first, that nursing can be viewed as a highly organised social assemblage, where both human (nurses, patients and policymakers) and nonhuman actors (technologies, medical equipment, institutional policies) play a crucial role, and second, that ANT can be used to enhance existing nursing theory to better understand the role of technology in nursing practice. The article considers how ANT can provide a more holistic and adaptable model for describing the nursing profession, particularly in an era where technology plays an integral role in healthcare delivery. It discusses the implications of viewing nursing through ANT, highlighting the need for nursing education and practice to adapt to the interconnected and technologically advanced nature of modern healthcare. The article also acknowledges the limitations of ANT, particularly its potential oversimplification of the complex ethical dimensions inherent in nursing and its focus on observable phenomena.

"Pray Observe How Time Slips By:" Collaborators, Assistants, and the Background Dynamics in the Publication of Darwin's Cirripedia Project.

This study investigates nineteenth century natural history practices through the lens of the Actor-Network Theory, which posits that scientific practice is shaped by an intricate network of interactions between human and non-human actors. At the core of this research is the analysis of correspondence between Charles Darwin and his collaborators during the Cirripedia Project, which unveils a complex landscape of negotiations with illustrators, funders, specimen owners, and translators, among other stakeholders and interested parties. The study goes beyond the final outcomes of scientific research, delving into behind-the-scenes interactions, and hidden constructions, shedding light on the complex dynamics and actors that conventional scientific narratives often overlook. In general, this approach provides a detailed and insightful view of the underlying processes of nineteenth-century scientific practice, underscoring the importance of epistolary correspondence as a central element in producing scientific knowledge at the time, and in particular it reveals to us how much Darwin was himself involved in the production of his famous work on barnacles. By emphasizing the intricacies of research, this study enriches our understanding of Darwin's work as well as natural history practices in the 19th century, highlighting the complexity and diversity of actors and agents involved in shaping scientific knowledge.

Exploring health inequities through the actor-network theory lens.

Social theory plays an important role in the nursing discipline and nursing inquiry as it helps conceptually embed nursing in the larger picture of the social world. For example, a broad category of critical theory provides a unique lens for uncovering social conditions of inequity and oppression. Among the sociological theories, actor-network theory (ANT) is an approach to research and analysis that has recently gained interest among nurse philosophers and researchers. Studies guided by ANT seek to understand phenomena of interest as constituted within the relationships between human and nonhuman actors to understand how care practices are co-created/enacted and how they can be made more humane. In this paper, we describe the benefits of ANT for examining healthcare access for incarcerated individuals with life-limiting illnesses accessing palliative care and for people using illicit drugs. We argue that attention to the materiality of care practices can contribute to efforts of advancing health equity for these groups.

Evolution of Telencephalon Anterior-Posterior Patterning through Core Endogenous Network Bifurcation.

How did the complex structure of the telencephalon evolve? Existing explanations are based on phenomena and lack a first-principles account. The Darwinian dynamics and endogenous network theory-established decades ago-provides a mathematical and theoretical framework and a general constitutive structure for theory-experiment coupling for answering this question from a first-principles perspective. By revisiting a gene network that explains the anterior-posterior patterning of the vertebrate telencephalon, we found that upon increasing the cooperative effect within this network, fixed points gradually evolve, accompanied by the occurrence of two bifurcations. The dynamic behavior of this network is informed by the knowledge obtained from experiments on telencephalic evolution. Our work provides a quantitative explanation for how telencephalon anterior-posterior patterning evolved from the pre-vertebrate chordate to the vertebrate and provides a series of verifiable predictions from a first-principles perspective.

Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs.

We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.