Spatio-temporal Fluctuations Research Articles

Characterizing dynamic heterogeneities during nanogel degradation.

Understanding photodegradation of nanogels is critical for dynamic control of their properties and functionalities. We focus on nanogels formed by end-linking of four-arm polyethylene glycol precursors with photolabile groups and characterize dynamic heterogeneities in these systems during degradation. We use our recently developed dissipative particle dynamics framework that captures the controlled scission of bonds between the precursors and diffusion of degraded fragments at the mesoscale. To quantify spatiotemporal fluctuations in the local dynamic behavior, we calculate the self-part of the van-Hove correlation function for the reactive beads for nanogels degrading in various environments. We demonstrate strong deviations from the Gaussian behavior during the degradation and quantify variations in the non-Gaussian parameter as a function of the relative extent of degradation. We show that for the nanogels degrading in a good solvent, the peak values in the non-Gaussian parameter are observed significantly earlier than the reverse gel point, and earlier than the peak values in the dispersity of the broken off fragments. Further, our study shows that a systematic decrease in solvent quality significantly affects the behavior of the non-Gaussian parameter as a function of the relative extent of degradation. The findings of this study allow one to quantify the dynamic heterogeneities during degradation in various environments and can potentially provide guidelines for designing controllably degrading nanocarriers.

Multivariate time series weather forecasting model using integrated secondary decomposition and Self-Attentive spatio-temporal learning network

<p>Weather forecasting is an essential but complex task because of its substantial influence on human life and advanced atmospheric dynamics. In recent years, deep learning-based techniques have gain high attention for weather forecasting. Nevertheless, current forecasting models ignore the interdependent relationship between variables across regions and primarily examine temporal patterns of weather data. In this work, a new Multivariate time series weather forecasting model is proposed using integrated secondary decomposition and Self-Attentive spatio-temporal learning network (SASTLNet). Particularly, the weather data is filtered using a Singular Spectrum Decomposition with Fuzzy Entropy (SSD-FE) for removing the irrelevant components and screening the component containing vital information. Moreover, the spatiotemporal fluctuations in the meteorological variables are investigated using an enhanced empirical mode decomposition (E3MD) method and an SASTLNet model. The self-attention block of the SASTLNet model is discovered using ladder format to lower the processing costs while representing the temporal and spatial features through single memory cell. The simulation results prove that the suggested model can outperforms the baseline models in terms of efficacy and accuracy.</p>

GSADDQN: Combining GraphSAGE and reinforcement learning for routing optimization in software-defined optical transport network

As a result of rapid development of network communication technology, optical transport network (OTN) traffic has also experienced rapid growth in terms of information volume scale, traffic complexity, and spatiotemporal distribution dynamics. Because the OTN traffic demand has complex spatiotemporal fluctuations, the traditional deep reinforcement learning (DRL) algorithm has been applied to the routing optimization of software-defined OTNs. However, the traditional DRL algorithm has problems such as slow convergence, weak generalization ability, and load imbalance when performing routine tasks. To address these issues, we propose a graph sampling and aggregation (GraphSAGE)-based dueling deep Q-network (GSADDQN) algorithm for software-defined OTN routing optimization. First, we design a DRL-based routing decision model to find the best routing strategy for each optical network’s source–destination traffic demand. Second, considering the sparse connection characteristics of optical network nodes, we use sampling neighbors and a deep aggregation mechanism as the neural network model of the Dueling Deep Q-Network (Dueling DQN) algorithm so that the reinforcement learning agent can consciously aggregate important network information and improve the model’s convergence performance and generalization ability. Finally, we design simulation routing experiments based on Gym and evaluate the algorithm’s load balancing and generalization capabilities for different network topologies. The experimental results show that the GSADDQN algorithm has good convergence performance and load balancing ability in routing optimization of optical transmission networks and can generalize new network structures, maintaining good decision-making ability even during network node failures.

Climate and Coffee Production in Ethiopia: A Review

Climate is a complex system involving the atmosphere, land surface, snow and ice, oceans, and other water bodies. It is measured by variations in temperature, humidity, atmospheric pressure, wind, precipitation, atmospheric particles, and other meteorological variables over a 30-year period. Climate change has gained momentum due to anthropogenic disturbances, which may negatively impact human health and the biosphere. The complex relationships between humans, microbes, and the biosphere are causing an increase in greenhouse gases (GHGs), causing global warming and other cascade effects. Climate change is a key environmental concern, posing challenges to ecosystems, food security, water resources, and economic stability. Historical climate records and projected patterns across global regions have confirmed this, with Ethiopia experiencing significant increases in annual mean temperature, hot days, and nights. Climate variability refers to the Spatio-temporal fluctuation of climatic conditions, focusing on the variability dimensions. The Intergovernmental Panel on Climate Change (IPCC) has declared climate change a key environmental concern. Climate in Ethiopia is characterized by significant geographic variance in rainfall and temperature data. The country has three seasons: bega (dry season) from October to January, belg (short rain season) from February to May, and kiremt (long rainy season) from June to September. The country's climate is influenced by the seasonal migration of the Intertropical Convergence Zone and its varied geography, affecting landforms, natural landscapes, and local people's living situations. Coffee is susceptible to drought, over-wetting, and wind damage because its production and quality are largely dependent on temperature and rainfall levels. In key coffee-growing regions around the world, the yield of coffee is at risk due to climate change and unpredictability. The production of coffee is expected to be severely impacted by high temperatures and unpredictable rainfall patterns in terms of yield, quality, pests, and illnesses.

BRAG: Blind region-agnostic geolocation of LTE mobile users in urban areas

Geolocation is the backbone of many novel location-intelligent applications. Additionally, geodata analysis helps model and predict spatiotemporal fluctuations in data traffic, which is important for network optimization, operation cost reduction, and power saving. Furthermore, geodata analysis can be utilized in fields such as transportation, urban planning, tourism, marketing, epidemiology, national statistics, and censuses. Cellular geolocation is advantageous when Global Positioning System (GPS) readings are not available, especially since it does not require altering the network infrastructure or installing expensive equipment. However, cellular geolocation is challenged by the high randomness and dynamics of the environment. In this paper, we propose a blind region-agnostic algorithm to geolocate Long-Term Evolution (LTE) mobile users in urban areas. The algorithm uses timing and signal strength readings, which are readily available at the serving evolved Node B (eNB), to calculate initial estimated positions. Following that, the algorithm uses correlations between the initial estimates along the user’s path to improve its geolocation accuracy. The proposed method does not require training or prior data collection, making it easy to implement in different regions. We tested the method on real data from drive tests in different cities, and the method achieved a mean error of 132 meters and a median error of 91 meters, compared to a mean error of 203 meters and a median error of 125 meters achieved by basic time-advance-based geolocation.

Spatiotemporal Fluctuation in Water Quality Parameters and Correlation with Phytoplankton Community at Sambhar Salt Lake, India

Spatiotemporal Fluctuation in Water Quality Parameters and Correlation with Phytoplankton Community at Sambhar Salt Lake, India

Paleobiology and paleoecology of the woolly rhinoceros (Coelodonta antiquitatis) in Northern and Central Europe: New insights from multi-proxy data

The woolly rhinoceros, Coelodonta antiquitatis, was an emblematic component of Pleistocene faunas in Eurasia, which went extinct around ca. 12.5 ky BP. The loss of its tundra-steppe habitat due to climatic changes is considered the main cause for its extinction, whereas human impact was limited. In this study, we investigated the paleobiology and paleoecology of C. antiquitatis during the last glacial interval (Würm/Weichselian; 130 to 11.7 kya). We explored evolutionary trends for diet, physiology, and habitat via dental wear, enamel hypoplasia, body mass, age structure, mortality curves and stable isotopes (carbon and oxygen). Our results confirmed that C. antiquitatis was a large-sized species, with body mass around 2000–2500 kg, and with C3 grazing or mixed-feeding habits. Age structure and mortality curves revealed potential sampling and/or taphonomical biases at a few localities (e.g., Brixham cave, Ofnethöhle, North Sea), and indicated several vulnerability periods (birth, weaning, cow-calf separation/maturity) also retrieved by hypoplasia analyses. We observed some spatio-temporal fluctuations of body mass (1850–2955 kg), dietary preferences (strict to variable grazing) and hypoplasia prevalence (7.41–47.06 %) of C. antiquitatis depending on the locality, but correlation to specific climatic events (stadials-interstadials) is difficult without exact datation. These variations were however limited, highlighting a rather strict climatic niche and suggesting a high vulnerability to climatic and vegetation changes.

Spatiotemporal fluctuation induces Turing pattern formation in the chemical Brusselator

Chemical reactions are embedded in spatiotemporal fluctuations instead of a constant environment. Here, we aimed to assess reaction–diffusion (RD) with dichotomous noise‐controlling system parameters in the Brusselator and examine the effect of these fluctuations on the dynamic behavior of chemical reactions. By performing a multiscale perturbation analysis, we demonstrated that the correlated noise can broaden the Turing region even if molecular memory (autocorrelation time) exists. However, for small noise, short‐term memory promotes Turing instability. The instability of the Brusselator is determined by the noise strength, which belongs to the optimal region if the diffusion coefficient is fixed. Turing pattern selection and stability are also governed by the dynamic character of the amplitude equation, and the entire Turing instability region shifts to the right in the phase space with noise perturbation. Finally, numerical simulations validate the theoretical derivation that correlated noise can amplify Turing pattern formation to maintain distinct patterns.

Small-scale Helmholtz resonators with grazing turbulent boundary layer flow

ABSTRACT Helmholtz resonators flush-mounted in a wall beneath turbulent boundary layer flow are studied by focusing on their flow-induced excitation and effect on the grazing turbulent flow. A particular focus lies on single resonators tuned to the most intense spatio-temporal fluctuations in the near-wall vertical velocity and wall-pressure, residing at a spatial scale of λ x + ≈ 250 (or temporal scale of T + ≈ 25 ). Resonators are examined in a boundary layer flow at R e τ ≈ 2280 . Two neck-orifice diameters of d + ≈ 68 and 102 are considered, and for each value of d + three different resonance frequencies are studied (corresponding to a period of T + ≈ 25 , as well as one lower, and one higher, period). The response of the TBL flow is analysed by employing velocity data from hot-wire anemometry and particle image velocimetry measurements. Passive resonance only affects streamwise velocity fluctuations in the region y + ≲ 25 , while vertical velocity fluctuations due to resonance reach up to y + ≈ 100 . A narrow-band increase in streamwise turbulence kinetic energy at the resonance scale co-exists with a more than 20% attenuation of lower-frequency energy. Current findings on single resonator cases will aid in the development of passive surfaces with distributed resonators for boundary-layer flow control.

Dynamical stiffening of dsDNA confined and stretched in a nanochannel

The internal dynamics of double-stranded DNA (dsDNA) confined within channels of diameters ranging from the persistence length to twice that length were investigated using fluorescence microscopy. By analysing spatiotemporal intensity fluctuations, we derived the intermediate dynamic structure factor. The structure factor consistently exhibited behaviour characteristic of Rouse dynamics, regardless of the channel diameter. Notably, as the channel diameter decreased, the DNA molecules became increasingly elongated along the channel, leading to shorter relaxation times. These findings indicate a dynamic stiffening effect, where the effective spring constant of a stretched polymer chain increases. We propose that this effect has significant implications for controlling DNA motion in biological and biotechnological applications.

Odd Viscosity Suppresses Intermittency in Direct Turbulent Cascades.

Intermittency refers to the broken self-similarity of turbulent flows caused by anomalous spatiotemporal fluctuations. In this Letter, we ask how intermittency is affected by a nondissipative viscosity, known as odd viscosity (also Hall viscosity or gyroviscosity), which appears in parity-breaking fluids such as magnetized polyatomic gases, electron fluids under magnetic field, and spinning colloids or grains. Using a combination of Navier-Stokes simulations and theory, we show that intermittency is suppressed by odd viscosity at small scales. This effect is caused by parity-breaking waves, induced by odd viscosity, that break the multiple scale invariances of the Navier-Stokes equations. Building on this insight, we construct a two-channel helical shell model that reproduces the basic phenomenology of turbulent odd-viscous fluids including the suppression of anomalous scaling. Our findings illustrate how a fully developed direct cascade that is entirely self-similar can emerge below a tunable length scale, paving the way for designing turbulent flows with adjustable levels of intermittency.

Analysis of long-term dynamics of air temperature fluctuations in the Nura River basin

In recent decades, a different frequency of dry years has been observed in the Nura River basin in Central Kazakhstan. This article examines the trend of changes in temperature fluctuations over a long period using materials from a separate climate element from the Besoba and Karaganda meteorological stations located in the Nura River basin and the Arkalyk meteorological station located outside the basin, to the west of it, but characterized by similar natural conditions. In the course of research over an 81-year time interval from 1939 to 2019, the absolute and relative frequency of average monthly air temperatures in July in the Nura River basin were established. The meteorological stations under consideration are part of the global international network of meteorological data of Kazhydromet and have constant, accurate series of values of meteorological indicators. Based on the use of the method of statistical analysis, an assessment of the spatio-temporal fluctuations of the temperature regime over a long-term period in the Nura River basin was identified. The study expressed the formation of an arid climate associated with an increase in the average monthly air temperature in the basin of the study area.

Nonequilibrium Membrane Dynamics Induced by Active Protein Interactions and Chemical Reactions: A Review

AbstractBiomembranes wrapping cells and organelles are not only the partitions that separate the insides but also dynamic fields for biological functions accompanied by membrane shape changes. In this review, we discuss the spatiotemporal patterns and fluctuations of membranes under nonequilibrium conditions. In particular, we focus on theoretical analyses and simulations. Protein active forces enhance or suppress the membrane fluctuations; the membrane height spectra are deviated from the thermal spectra. Protein binding or unbinding to the membrane is activated or inhibited by other proteins and chemical reactions, such as ATP hydrolysis. Such active binding processes can induce traveling waves, Turing patterns, and membrane morphological changes. They can be represented by the continuum reaction‐diffusion equations and discrete lattice/particle models with state flips. The effects of structural changes in amphiphilic molecules on the molecular‐assembly structures are also discussed.

Harnessing an elastic flow instability to improve the kinetic performance of chromatographic columns

Despite decades of research and development, the optimal efficiency of slurry-packed HPLC columns is still hindered by inherent long-range flow heterogeneity from the wall to the central bulk region of these columns. Here, we show an example of how this issue can be addressed through the straightforward addition of a semidilute amount (500 ppm) of a large, flexible, synthetic polymer (18 MDa partially hydrolyzed polyacrylamide, HPAM) to the mobile phase (1% NaCl aqueous solution, hereafter referred to as “brine”) during operation of a 4.6 mm × 300 mm column packed with 10μm BEHTM 125 Å particles. Addition of the polymer imparts elasticity to the mobile phase, causing the flow in the interparticle pore space to become unstable above a threshold flow rate. We verify the development of this elastic flow instability using pressure drop measurements of the friction factor versus Reynolds number. In prior work, we showed that this flow instability is characterized by large spatiotemporal fluctuations in the pore-scale flow velocities that may promote analyte dispersion across the column. Axial dispersion measurements of the quasi non-retained tracer thiourea confirm this possibility: they reveal that operating above the onset of the instability improves column efficiency by greater than 100%. These experiments thereby suggest that elastic flow instabilities can be harnessed to mitigate the negative impact of trans-column flow heterogeneities on the efficiency of slurry-packed HPLC columns. While this approach has its own inherent limitations and constraints, our results lay the groundwork for future targeted development of polymers that can impart elasticity when dissolved in commonly used liquid chromatography mobile phases, and can thereby generate elastic flow instabilities to help improve the resolution of HPLC columns.

Increased anaerobic conditions promote the denitrifying nitrogen removal potential and limit anammox substrate acquisition within paddy irrigation and drainage units

Microbial nitrogen (N) removal is crucial for purifying surface water quality in paddy irrigation and drainage units (IDUs). However, the spatiotemporal microbial N removal potential characteristics within these IDUs and the effects of changing anaerobic conditions on this potential remain insufficiently studied. In this study, we investigated the microbial N removal potential of conventional rice-wheat rotation and anaerobically enhanced rice-crayfish rotation IDUs using field measurements, isotope tracing techniques, and quantitative PCR. Our findings reveal that paddy fields were identified as hotspots for anammox activity, contributing to 76.0 %–97.4 % of the total anammox N removal potential in the IDU, while denitrification processes in ditches accounted for 43.5 %–77.4 % of the IDU's denitrification potential. During the rice transplanting period, the anammox N removal potential peaked, representing 35.8 % and 71.8 % of the total anammox N removal potential of the paddy fields in rice-wheat and rice-crayfish IDUs, respectively. An increase in anaerobic conditions diminished the anammox N removal potential while amplifying denitrification capabilities. The N removal potential in paddy fields decreased with increasing depth, contrasting with the relative stability in ditches. Spatiotemporal fluctuations in N removal potentials within these units are influenced by Fe2+ concentration, carbon and N content, WFPS, and pH levels. This study provides a scientific basis for improving nitrogen removal and water quality treatment in IDUs.

Characterizing multifarious hydroclimatic patterns using geodetic measurements in the Australian mainland

Benefiting from the tremendous advance of modern geodetic technologies, accurate measurements provide us with novel means to weigh terrestrial water storage (TWS) and shed critical light on hydroclimatic interaction mechanisms. In this study, GNSS and GRACE observations are jointly used to investigate spatiotemporal TWS fluctuations and their connections with large-scale climate patterns in the Australian mainland. A variational Bayesian principal component analysis-based inversion method is designed to handle random data missing in GNSS and long-term data gaps in GRACE. The jointly inferred TWS estimates manifest more spatial details compared to the GRACE Mascon solutions and good resistance to non-hydrological mechanisms in GNSS data. The joint inversion results are compared with other water-related storage/flux estimates (i.e., GLDAS, GRACE, and precipitation), which have considerable coherence with TWS at both temporal and spatial scales. The results indicate obvious annual variations in TWS changes in north Australia in contrast to inappreciable seasonal hydrological signatures in other vast arid zones. Hydrological wet/dry conditions are quantified with drought severity index (DSI) datasets derived from multiple means and the joint-based DSI datasets temporally agree with the other datasets and correspond well with precipitation anomalies in most watersheds. In addition, the jointly inverted TWS estimates are used to declassify multifarious hydroclimatic features, and multi-source hydrometeorological datasets are used to attribute interannual TWS changes. Our study contributes to better observing the water cycles for the hydrological community and illuminates more application prospects in the climate sciences.

Spatial and seasonal assessment of surface water quality for domestic use in a semi-arid area of the Upper Kebir Sub-basin, NE Algeria

ABSTRACT The present study was conducted out in the Upper Kebir Sub-basin [North East (NE) Algeria] aiming to evaluate the status and spatial–seasonal variability of water quality for domestic purposes using hydrogeochemical parameters and water quality indices. Surface water samples were collected from selected 27 sites, in the period 2020–2021 during both the wet and dry seasons and were analysed for 22 parameters. The findings were compared with standards and the water quality indices were calculated. The analysis revealed that the basin was mainly polluted and showed significant seasonal and spatial variations in water quality, considerably influenced by climatic conditions (surface runoff) and human activities (urban sewage and agricultural activities). The results reveal significant spatio-temporal fluctuations and highlight areas likely to be affected by anthropogenic activities.

Air mycobiome in the National Library of Greece following relocation to novel premises

The aim of this work was to study the diversity and spatiotemporal fluctuations of airborne fungi in the National Library of Greece after its relocation from the Vallianeio historic building in the center of Athens to entirely new premises at the Stavros Niarchos Foundation Cultural Center, and also to compare the fungal aerosol in between the two sites. The air mycobiota were studied by a volumetric culture-based method, during the year 2019 in order to assess their diversity and abundance and to compare with those previously reported in the historic building. Twenty-eight genera of filamentous fungi were recovered indoors and 17 outdoors, in addition to yeasts registered as a group. The number of fungal genera recovered was almost similar in both premises, whereas seventeen genera indoors were identical, dominated by Penicillium, Cladosporium and Aspergillus. The mean daily fungal concentration was found to be 66 CFU m−3 indoors and 927 CFU m−3 outdoors in the new location vs 293 and 428 CFU m−3 indoors and 707 and 648 CFU m−3 outdoors in the previous one. The mean daily concentration indoors was consistently and significantly lower (P < 0.05) in the new building than in the historic one, although it was higher outdoors. The indoor/outdoor ratio for the total fungi was 0.07 in the new vs 0.41 and 0.66 in the previous one and reveals a superior indoor air quality in the new site. Air temperature and occupancy had a statistically significant impact on the concentration of indoor fungi. The remarkably reduced concentration of the mycobiota in the new premises indicated a considerable decline in fungal burden, mainly due to technological excellency of the facility and continuous preventive measures to ensure an enhanced indoor air quality in the National Library of Greece. This case study provides a paradigm about upgrading of indoor air after re-establishment of a facility in another setting.

Spatiotemporal response of the optical characteristics of dissolved organic matter to seasonality and land use in tropical island rivers.

Dissolved organic matter (DOM), a pivotal component in the global carbon cycle, plays a crucial role in maintaining the productivity and functionality of aquatic ecosystems. However, the driving factors of variations in the properties of riverine DOM in tropical islands still remain unclear. In this study, the spatiotemporal response of the optical characteristics of riverine DOM to seasonality and land use on Hainan Island in southern China was investigated. Our results revealed that DOM in the rivers of Hainan Island exhibited a relatively high proportion of fulvic acid and demonstrated strong terrestrial sources. The optical properties of DOM exhibited significant variations both seasonally and spatially. Land use exerted a dominant influence on riverine DOM. Specifically, during the wet season, riverine DOM exhibited larger molecular weight, increased chromophoric DOM (CDOM) abundance, and higher Fmax compared to the dry season. Furthermore, riverine DOM influenced by grassland and farmland showed higher CDOM abundance, Fmax, and humification degree in contrast to those impacted by forest and urban. Random forest and correlation analysis results indicated that grassland and farmland enhanced the Fmax of DOM by increasing levels of TP, NO3--N, Chl a, and NH4+-N in the dry season. However, during the wet season, the increased Fmax of DOM induced by grassland and farmland relied on the increments of Chl a and TP concentrations. This study improves our understanding of the spatiotemporal fluctuations of DOM in the rivers of Hainan Island, highlighting the effects of season and land use on DOM. It offers valuable support for improving water quality and contributes to enhancing human comprehension of the global carbon cycle.

Spatiotemporal Evolution and Drivers of Carbon Storage from a Sustainable Development Perspective: A Case Study of the Region along the Middle and Lower Yellow River, China

Carbon storage (C-storage) is a critical indicator of ecosystem services, and it plays a vital role in maintaining ecological balance and driving sustainability. Its assessment provides essential insights for enhancing environmental protection, optimizing land use, and formulating policies that support long-term ecological and economic sustainability. Previous research on C-storage in the Yellow River Basin has mainly concentrated on the spatiotemporal fluctuations of C-storage and the investigation of natural influencing factors. However, research combining human activity factors to explore the influences on C-storage is limited. In this paper, based on the assessment of the spatiotemporal evolution of C-storage in the region along the Middle and Lower Yellow River (MLYR), the influences of anthropogenic and natural factors on C-storage were explored from the perspective of sustainable development. The findings reflected the relationship between socio-economic activities and the ecological environment from a sustainable development perspective, providing important scientific evidence for the formulation of sustainability policies in the region. We noticed the proportion of arable land was the highest, reaching 40%. The increase of construction land because of the fast urbanization mainly came from arable land and grassland. During the past 15 years, the cumulative loss of C-storage was 71.17 × 106 t. The high-value of C-storage was primarily situated in hilly areas, and the area of C-storage hotspots was shrinking. The aggregation effect of low-value C-storage was strengthening, while that of high-value C-storage was weakening. The dominant factors (q &gt; 0.5) influencing the spatiotemporal variation of C-storage in the region along the Middle Yellow River (MYR) were temperature and precipitation, while the primary factor in the region along the Lower Yellow River (LYR) was temperature. Overall, meteorological factors were the main determinants across the entire study area. Additionally, compared to the MYR, anthropogenic factors had a smaller impact on the spatiotemporal evolution of C-storage in the LYR, but their influence has been increasing over time.