Order parameter dynamics in complex systems: From models to data.

Collective ordering behaviors are typical macroscopic manifestations embedded in complex systems and can be ubiquitously observed across various physical backgrounds. Elements in complex systems may self-organize via mutual or external couplings to achieve diverse spatiotemporal coordinations. The order parameter, as a powerful quantity in describing the transition to collective states, may emerge spontaneously from large numbers of degrees of freedom through competitions.

Geometry of commutes in the universality of percolating traffic flows.

Traffic congestion is a major problem in megacities which increases vehicle emissions and degrades ambient air quality. Various models have been developed to address the universal features of traffic jams. These models range from microscopic car-following models to macroscopic collective dynamic models.

Arctic weather variability and connectivity.

The Arctic's rapid sea ice decline may influence global weather patterns, making the understanding of Arctic weather variability (WV) vital for accurate weather forecasting and analyzing extreme weather events. Quantifying this WV and its impacts under human-induced climate change remains a challenge. Here we develop a complexity-based approach and discover a strong statistical correlation between intraseasonal WV in the Arctic and the Arctic Oscillation.

Exploring the intensity, distribution and evolution of teleconnections using climate network analysis.

Teleconnections refer to long-range climate system linkages occurring over typically thousands of kilometers. Generally speaking, most teleconnections are attributed to the transmission of energy and propagation of waves although the physical complexity and characteristics behind these waves are not fully understood. To address this knowledge gap, we develop a climate network-based approach to reveal their directions and distribution patterns, evaluate the intensity of teleconnections, and identify sensitive regions using global daily surface air temperature data.

Large-area field application confirms the effectiveness of toxicant-infused bait for managing Helicoverpa armigera (Hübner) in maize fields.

Background: Maize is one of the world's most important crops, so its stable production and supply is crucial for food security and socioeconomic development. The cotton bollworm, Helicoverpa armigera (Hübner), is one of the major pests in maize. We evaluated the control effect of a bio-bait, an adult attractant, combined with insecticide, a 'toxicant-infused bait', on H.

Non-negative matrix factorization for overlapping community detection in directed weighted networks with sparse constraints.

Detecting overlapping communities is essential for analyzing the structure and function of complex networks. However, most existing approaches only consider network topology and overlook the benefits of attribute information. In this paper, we propose a novel attribute-information non-negative matrix factorization approach that integrates sparse constraints and optimizes an objective function for detecting communities in directed weighted networks.

Revealing spatiotemporal interaction patterns behind complex cities.

Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing general collective patterns behind spatiotemporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatiotemporal co-occurrence of individuals.

The Pulsed Electric Field Assisted-Extraction Enhanced the Yield and the Physicochemical Properties of Soluble Dietary Fiber From Orange Peel.

The study aimed to investigate the effects of pulsed electric field (PEF)-assisted extraction on the yield, physicochemical properties, and structure of soluble dietary fiber (SDF) from orange peel. The results showed that the optinal parameters of PEF assisted extraction SDF was temperature of 45C with the electric field intensity of 6.0 kV/cm, pulses number of 30, and time of 20min and SDF treated with PEF showed the higher water solubility, water-holding and oil-holding capacity, swelling capacity, emulsifying activity, emulsion stability, foam stability and higher binding capacity for Pb, As, Cu, and higher which resulted from the higher viscosity due to PEF treatment.

Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19.

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed.

Percolation analysis of the atmospheric structure.

The atmosphere is a thermo-hydrodynamical complex system and provides oxygen to most animal life at the Earth's surface. However, the detection of complexity for the atmosphere remains elusive and debated. Here we develop a percolation-based framework to explore its structure by using the global air temperature field.

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.

Single-cell immune profiling reveals distinct immune response in asymptomatic COVID-19 patients.

While some individuals infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) present mild-to-severe disease, many SARS-CoV-2-infected individuals are asymptomatic. We sought to identify the distinction of immune response between asymptomatic and moderate patients. We performed single-cell transcriptome and T-cell/B-cell receptor (TCR/BCR) sequencing in 37 longitudinal collected peripheral blood mononuclear cell samples from asymptomatic, moderate, and severe patients with healthy controls.

Eigen microstates and their evolution of global ozone at different geopotential heights.

Studies on stratospheric ozone have attracted much attention due to its serious impacts on climate changes and its important role as a tracer of Earth's global circulation. Tropospheric ozone as a main atmospheric pollutant damages human health as well as the growth of vegetation. Yet, there is still a lack of a theoretical framework to fully describe the variation of ozone.

Optimal resilience of modular interacting networks.

Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly.

Climate network approach reveals the modes of CO concentration to surface air temperature.

Increasing atmospheric carbon dioxide (CO) is expected to be the main factor of global warming. The relation between CO concentrations and surface air temperature (SAT) has been found related to Rossby waves based on a multi-layer complex network approach. However, the significant relations between CO and SAT occur in the South Hemisphere that is not that much influenced by human activities may offer not enough information to formulate targeted carbon reduction policies.

Statistical physics approaches to the complex Earth system.

Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks.

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier.

The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the "spring predictability barrier" remains a great challenge for long-lead-time (over 6 mo) forecasting.

Possible origin of memory in earthquakes: Real catalogs and an epidemic-type aftershock sequence model.

Earthquakes are one of the most devastating natural disasters that plague society. Skilled, reliable earthquake forecasting remains the ultimate goal for seismologists. Using the detrended fluctuation analysis (DFA) and conditional probability (CP) methods, we find that memory exists not only in interoccurrence seismic records but also in released energy as well as in the series of the number of events per unit time.

Percolation framework of the Earth's topography.

Self-similarity and long-range correlations are the remarkable features of the Earth's surface topography. Here we develop an approach based on percolation theory to study the geometrical features of Earth. Our analysis is based on high-resolution, 1 arc min, ETOPO1 global relief records.

Climate network percolation reveals the expansion and weakening of the tropical component under global warming.

Global climate warming poses a significant challenge to humanity; it is associated with, e.g., rising sea level and declining Arctic sea ice.

Resilience of networks with community structure behaves as if under an external field.

Although detecting and characterizing community structure is key in the study of networked systems, we still do not understand how community structure affects systemic resilience and stability. We use percolation theory to develop a framework for studying the resilience of networks with a community structure. We find both analytically and numerically that interlinks (the connections among communities) affect the percolation phase transition in a way similar to an external field in a ferromagnetic- paramagnetic spin system.

Network analysis reveals strongly localized impacts of El Niño.

Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña.

Percolation framework to describe El Niño conditions.

Complex networks have been used intensively to investigate the flow and dynamics of many natural systems including the climate system. Here, we develop a percolation based measure, the order parameter, to study and quantify climate networks. We find that abrupt transitions of the order parameter usually occur ∼1 year before El Niño events, suggesting that they can be used as early warning precursors of El Niño.

Continuous percolation phase transitions of random networks under a generalized Achlioptas process.

Using finite-size scaling, we have investigated the percolation phase transitions of evolving random networks under a generalized Achlioptas process (GAP). During this GAP, the edge with a minimum product of two connecting cluster sizes is taken with a probability p from two randomly chosen edges. This model becomes the Erdös-Rényi network at p=0.