Periodic Fluctuations Research Articles

New online beam intensity synchronous monitoring system in scanning transmission X-ray microscopy.

The scanning transmission X-ray microscopy (STXM) platform based on synchrotron radiation has achieved nanoscale imaging with chemical sensitivity using spectro-microscopy techniques. However, the quality of STXM imaging is affected by the stability of the beam intensity. The top-up operation mode of synchrotrons to maintain a constant electron beam intensity introduces periodic fluctuations in the X-ray beam intensity, leading to notable imaging noise that decreases both contrast and precision. To address this issue, a high-speed real-time beam intensity monitoring system was designed and implemented at the BL08U1A beamline of the Shanghai Synchrotron Radiation Facility. This system utilizes an yttrium-aluminium-garnet crystal along with dual detectors having an acquisition frequency of up to 1 MHz and a synchronization error of less than 20 ns between them. This system can precisely and synchronously monitor the X-ray beam intensity variations which are used to remove noise due to electron injection from STXM images, thereby markedly improving the quality of STXM imaging.

Life history strategies complement niche partitioning to support the coexistence of closely related Gilliamella species in the bee gut.

The maintenance of bacterial diversity at both species- and strain-levels is crucial for the sustainability of honey bee gut microbiota and host health. Periodic or random fluctuation in diet typically alters the metabolic niches available to gut microbes, thereby continuously reshaping bacterial diversity and interspecific interactions. It remains unclear how closely related bacteria adapt to these fluctuations and maintain coexistence within the bee gut. Here, we demonstrate that the five predominant Gilliamella species associated with Apis cerana, a widely distributed Asiatic honey bee, have diverged in carbohydrate metabolism to adapt to distinct nutrient niches driven by dietary fluctuation. Specifically, the glycan-specialists gain improved growth on a pollen-rich diet, but are overall inferior in competition to non-glycan-specialist on either a simple sugar or sugar-pollen diet, when co-inoculated in the bee host and transmitted across generations. Strikingly, despite of their disadvantage in a high-sugar condition, the glycan-specialists are found prevalent in natural A. cerana guts. We further reveal that these bacteria have adopted a life history strategy characterized by high biomass yield on a low-concentration sugar diet, allowing them to thrive under poor nutritional conditions, such as when the bee hosts undergo periodical starvation. Transcriptome analyses indicate that the divergence in life history strategies is attributed to gene expression programming rather than genetic variation. This study highlights the importance of integrative metabolic strategies in carbohydrate utilization, which facilitate the coexistence of closely related Gilliamella species in a changing bee gut environment.

Temporal variability in mortality and recruitment jointly influence the periodic fluctuations in Antarctic krill populations.

Antarctic krill (Euphausia superba) is a key part of the food web in the Southern Ocean ecosystem. Significant inter-annual fluctuations in population dynamics make stock assessment and management of its population a significant challenge. To better understand the population dynamics and fluctuation of krill, a survey-based age-structured catch-at-length model (ACL) is used to estimate the periodic fluctuations, based on length data collected from scientific surveys under the US Antarctic Marine Living Resources (AMLR) Program between 1992 and 2011. Spectral analysis of the model estimates revealed periodic fluctuations of 5-6 years in the recruitment, total abundance, and total biomass of krill in the Antarctic Peninsula, while spawning stock biomass exhibited periodic fluctuations of both 5-6 years and 2-3 years. The variations in krill total abundance and total biomass were mainly driven by recruitment, but the variation in spawning stock biomass was likely driven by both recruitment and time-varying, age-specific mortality. Our study contributes to a better understanding of the patterns and drivers of the periodic dynamics of Antarctic krill, which may help lead to improved assessments and fishery management for this important stock.

Fast Bayesian spectral analysis using convolutional neural networks: Applications to GONG Hα solar data

Solar filament oscillations have been observed for many years, but recent advances in telescope capabilities now enable a daily monitoring of these periodic motions. This offers valuable insights into the structure of filaments. A systematic study of filament oscillations over the solar cycle can shed light on the evolution of the prominences. Only manual techniques were used so far to analyze these oscillations. This work serves as a proof of concept and demonstrates the effectiveness of convolutional neural networks (CNNs). These networks automatically detect filament oscillations by applying a power-spectrum analysis to data from the GONG telescope network. The proposed technique studies periodic fluctuations in every pixel of the data cubes. Using the Lomb-Scargle periodogram, we computed the power spectral density (PSD) of the dataset. The background noise fits a combination of red and white noise well. Using Bayesian statistics and Markov chain Monte Carlo (MCMC) algorithms, we fit the spectra and determined the confidence threshold of a given percentage to search for real oscillations. We built two CNN models to obtain the same results as with the MCMC approach. We applied the CNN models to some observations reported in the literature to prove its reliability in detecting the same events as the classical methods. A day with events that were not previously reported was studied to determine the model capabilities beyond a controlled dataset that we can check with previous reports. CNNs prove to be a useful tool for studying solar filament oscillations using spectral techniques. The computing times are significantly reduced for results that are similar enough to the classical methods. This is a relevant step toward the automatic detection of filament oscillations.

Aging Reduces ATP-Binding Cassette Transporter Expression in Brain Microvessels of Mice

Background: ATP-binding cassette (ABC) transporters are expressed in the vascular walls of brain capillaries and remove toxic chemicals from the brain. The expression of ABC transporters in peripheral organs is transcriptionally regulated by clock genes and exhibits 24 h periodic fluctuations. In addition, clock gene outputs diminish with aging. In this study, we evaluated whether the expression of ABC transporters in the blood–brain barrier (BBB) of young mice had a 24 h cycle, and whether the expression of ABC transporters in the BBB decreased with age. Methods: Brain microvascular (BMV) fractions from the cerebral cortex of male C57BL/6J mice were prepared using dextran. BMV fractions from young mice (12 weeks old) were prepared every four hours to evaluate 24 h rhythmicity. BMV fractions from both young and aged mice (85 weeks old) were prepared when protein expression peaked (Zeitgeber Time 5). Protein and mRNA expression of ABC transporters in BMV fractions were measured. Results: In young mice, protein expression of P-glycoprotein, breast cancer resistance protein, and multidrug resistance protein 4 showed time-dependent variations with a peak in the light phase (Zeitgeber Time 5); mRNA expression showed no time-dependent variation. The protein expression of these transporters was lower in the BBB of aged mice than in that of young mice, although mRNA expression did not differ between young and aged mice. Conclusions: ABC transporter protein expression levels in BMV endothelial cells decreased with aging; however, mRNA levels did not change, which suggests changes in protein expression did not result from diminished clock gene output. Further studies are needed to elucidate the mechanisms by which ABC transporter expression in the BBB decreases with aging.

Mach Number Scaling of Foreshock Magnetic Fluctuations at Quasi-parallel Bow Shocks and Their Role in Magnetospheric Driving Throughout the Solar System

Abstract Upstream of quasi-parallel bow shocks, reflected ions generate ion–ion instabilities. The resulting magnetic fluctuations can advect through the shock and interact with planetary magnetospheres. The amplitude of magnetic fluctuations depends on the strength of the shock, quantified by the Alfvén Mach number (M A), which is the ratio of solar wind velocity to the local Alfvén velocity. With increasing heliocentric distance, the solar wind M A generally increases, such that Mercury typically experiences a lower M A ∼ 5 compared to Earth (M A ∼ 8), and Mars a slightly higher M A ∼ 9. Farther out in the solar system, Saturn has even higher M A (∼10). However, the solar wind flow is highly irregular, and on top of solar cycle variations these values for average M A at each planet do not capture extreme events. Statistical analysis of OMNIWeb observations from 2015 to 2023 shows that sustained (30 minutes or more) high M A (30–100) occurs at Earth about once a month. Using a selection of events in the ion foreshock regions of Mercury, Earth, Mars, and Saturn, a linear scaling is calculated for the maximum magnetic fluctuation amplitude as a function of M A. The resulting slope is ∼0.2. Based on the dominant fluctuation frequency for the largest-amplitude events at each planet, it is found that Mars exists in a special regime where the wave period of the magnetic fluctuations can be similar to or longer than the magnetospheric convection timescale, making Mars more susceptible to space weather effects associated with foreshock fluctuations.

Joint Analysis of Cardiovascular Control and Shear Wave Elastography to Determine Carotid Plaque Vulnerability.

Background/Objectives: Carotid artery stenosis (CAS) is one of the main causes of stroke, and the vulnerability of plaque has been proved to be a determinant. A joint analysis of shear wave elastography, a radiofrequency echo-based wall tracking technique for arterial stiffness evaluation, and of autonomic and baroreflex function is proposed to noninvasively, preoperatively assess plaque vulnerability in asymptomatic CAS patients scheduled for carotid endarterectomy. Methods: Elastographic markers of arterial stiffness were derived preoperatively in 78 CAS patients (age: 74.2 + 7.7 years, 27 females). Autonomic and baroreflex markers were also assessed by means of an analysis of the beat-to-beat fluctuations in heart period and systolic arterial pressure, derived at rest in supine position (REST) and during active standing. Postoperative analysis identified 36 patients with vulnerable plaque (VULN) and 42 with stable plaque (STABLE). Results: Baroreflex sensitivity (BRS) at a respiratory rate decreased during STAND only in VULN patients, being much higher at REST compared to STABLE levels. Autonomic indexes were not helpful in separating experimental conditions and/or populations. The Young's modulus (YM) of the plaque was lower in the VULN group than in the STABLE one. Cardiovascular control and elastographic markers were significantly correlated only in VULN patients. A multivariate logistic regression model built combining YM and BRS at the respiratory rate improved the prediction of plaque vulnerability, reporting an area under the ROC curve of 0.694. Conclusions: Noninvasive techniques assessing shear wave elastography and baroreflex control could contribute to the early detection of plaque vulnerability in patients with asymptomatic CAS.

Evolutionary analysis of a coupled epidemic-voluntary vaccination behavior model with immunity waning on complex networks.

Vaccination is the most effective method of preventing and controlling the transmission of infectious diseases within populations. However, the phenomenon of waning immunity can induce periodic fluctuations in epidemic spreading. This study proposes a coupled epidemic-vaccination dynamic model to analyze the influence of immunity waning on the epidemic spreading within the context of voluntary vaccination. First, we establish an SIRSV (susceptible-infected-recovered-susceptible-vaccinated) compartment model to describe the transmission mechanism of infectious diseases based on the mean-field theory. Within this model, we incorporate a nonlinear infection rate with network topology and consider the waning natural and vaccine-induced immunity at the individual level. The evolutionary model of voluntary vaccination strategy is integrated into the SIRSV model to characterize the impact of vaccination behavior on the infectious disease transmission. We also consider two individual risk assessment methods, namely, the individual-based risk assessment (IB-RA) method and the society-based risk assessment (SB-RA) method, originating from local and global perspectives, respectively. Then, utilizing the next-generation matrix method, we derive the time-varying effective reproduction numbers of the model. Also, the theoretical analysis of optimal strategy thresholds in the individual decision-making process is also conducted. The results indicate that the thresholds obtained from the agent-based model (ABM) simulation method are consistent with the theoretical analysis, demonstrating the effectiveness of our model. Finally, we apply the coupled model to the COVID-19 pandemic in France, Germany, Italy, and the United Kingdom. This study analyzes the impact of waning immunity and provides early warning for the outbreak of the epidemics.

The effect of time delay on the dynamics of a fractional-order epidemic model

This study establishes a novel time-delay fractional SEIHR infectious disease model to investigate the effects of saturated incidence rates and time delays on different populations, including susceptibles, infected individuals, recovered individuals, and latent infected individuals. First, the existence and boundedness of the model’s solutions are verified, confirming its well-posedness. Subsequently, the existence of equilibria is analyzed, and the impact of parameter variations on the system is explored by examining the equilibria ϵ0 and ϵ∗, as well as the basic reproduction number R0. Additionally, the global dynamics of the equilibria are further analyzed using the Lyapunov method, while Hopf bifurcation theory is applied to explore the conditions under which the system shifts from stability to oscillatory behavior. Numerical simulations further validate the theoretical analysis, showing that time-delay effects significantly influence the system’s responsiveness and the rate of disease transmission. Moreover, when the time delay τ crosses the critical threshold τ0, the system exhibits periodic oscillations. By predicting periodic fluctuations and incorporating memory effects and persistent influences, we can better control epidemics, emphasizing the importance of time-delay adjustments and enhancing the system’s biological realism.

Ensuring Reliable Network Communication and Data Processing in Internet of Things Systems with Prediction-Based Resource Allocation

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users’ requests. The method considers the current resource load and predicted changes in resource utilization based on historical request data, which are then utilized to adjust the resource allocation optimization criteria for upcoming requests. The proposed method was developed for scenarios utilizing edge computing, e.g., autonomous vehicle data exchange, which can be susceptible to periodic resource demand fluctuations related to typical rush hours, predictable with the proposed approach. The results indicate that the proposed approach can increase the reliability of processes conducted in IoT systems.

The depth of the thermal influence zone of the surface in the snow cover

The subject of the study is the snow cover, which determines the formation of the thermal regime of soils in winter. The purpose of the work is to determine the depth of the zone of thermal influence of the surface in the snow cover. That is, the determination of the zone of temperature fluctuations (daily, decadal) in the snow cover when the temperature of the atmospheric air changes. Determining the depth of this zone is important both for taking into account the formation of the properties of the snow cover itself, and for choosing a method for modeling the process of thermal interaction of the atmosphere with the ground in the presence of snow cover. In particular, the possibility of taking into account snow cover as thermal resistance in modeling thermal processes. To assess the depth of thermal influence, the well-known Goodman formula was used, obtained by solving the corresponding problem of thermal conductivity by the integral method and representing the dependence of the depth of the zone of temperature change in a solid with an abrupt change in surface temperature on time and thermal conductivity of the material (in this case, snow of a certain density). To determine the thermal conductivity, the formulas of Abels and Osokin were used to determine the thermal conductivity coefficient of snow depending on density. At the same time, it was taken into account that the density of snow cover is a variable in depth, determined by the linearized Abe formula. Alternatively, a snow cover with a density equal to the average integral density in depth is considered. Dependences are obtained to determine the duration of the attenuation period of surface temperature fluctuations at a certain depth of snow cover. An indicator of the change in the depth of vibration attenuation (the depth of thermal influence) is proposed. To assess the effect of snow reclamation, a formula is proposed that allows us to determine the degree of change in the duration of the period of complete attenuation of temperature in depth during compaction of snow cover, depending on the compaction coefficient. A dependence has been obtained linking the depth of the zone of thermal influence with the duration of the period of daily temperature fluctuations on the surface of the snow cover and its density. Comparison of the calculated data according to the obtained formulas with the data on the depth of attenuation of daily temperature fluctuations in snow cover with different snow densities, given in the literature, showed good convergence. This allows us to recommend the obtained formulas for practical use in assessing the process of formation of the thermal regime of snow cover.

Tracing the source and behaviour of sulphate in karst reservoirs, using stable isotopes and Bayesian isotopic-mixing models.

Increases in sulphate concentrations in natural water bodies can lead to the deterioration of water quality. Human activities, such as coal mining and agricultural fertilisation, can generate sulphate, which can enter water bodies through surface runoff or underground pipelines. Owing to the widespread distribution of coal-bearing strata and an intensification of industrial and agricultural activities, the Pingzhai Reservoir is increasingly at risk of sulphate pollution. In this study, 42 water samples were collected from the Pingzhai Reservoir in April (normal season), July (wet season), and December (dry season) of 2022. Additionally, two precipitation samples, two sewage samples, and two acidic mine drainage samples were collected. Using hydrochemistry, multiple isotopes (δ34SSO4, δ18OSO4, δ13CDIC, and δ18OH2O) and Bayesian isotopic-mixing model methods, we qualitatively and quantitatively determined the source, contribution proportion, and behaviour of SO42- in the Pingzhai Reservoir watershed and evaluated the uncertainty of the estimated results. Isotope analysis and the Bayesian isotope-mixing model results indicated that the sources of SO42- in the Pingzhai Reservoir were coal sulphides and organic sulphur oxidation (64.2%), soil organic sulphur (18.7%), sewage (9.9%), and agricultural sulphur fertiliser (7.2%). The characteristics of karst landforms (thin soil that is easily eroded), combined with periodic fluctuations in water level (hydrofluctuation belts) in reservoirs, resulted in the release of organic matter from soil to water. The proportion of SO42- sources of coal sulphide and organic sulphur oxidation in the river was lower than that in the reservoir area, whereas the proportion of the SO42- sources of soil organic sulphur, sewage, and agricultural sulphur fertiliser was greater than that in the reservoir area. Isotope evidence and the aerobic conditions in water indicated that bacterial sulphate reduction processes did not play a major role. The uncertainty index (UI90) indicated that the contributions of agricultural sulphur fertiliser and sewage manure to SO42- were relatively constant. This study provides a reference for the protection of water environments and for the development of water pollution control strategies in the karst areas of southwestern China.

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling.

Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as "wiggling". Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Synthesis of Porous Anodic Alumina Featuring a Periodic Pore Structure by Regulating the Anode Temperature

Abstract Porous anodic alumina (PAA) with a periodic pore structure has been synthesized by using an innovative preparation method. The morphology of PAA pores can be modulated within the same electrolyte by adjusting the temperature of the aluminum anode, enabling periodic variations in pore size (Dp). The formation mechanism of PAA has been elucidated through analyses of micromorphology, anodization current density (ia), interpore distance (Dint), and Dp of the samples. Results indicate that the average Dint for the synthesized PAA is approximately 260–340 nm, while the average Dp ranges from 90 to 260 nm. Both ia and Dp exhibit periodic fluctuations corresponding to changes in anode temperature under consistent electrolyte conditions and anodization voltage (Ua). Lateral pores are generated via a phosphoric acid etching process, resulting in PAA with a distinctive three-dimensional interconnected pore architecture. Furthermore, ridges with an arc-like shape on the outer walls of PAA pores have been observed; their formation mechanism can be effectively explained by the convection model and the viscous flow model. These findings contribute significantly to achieving precise control over the pore structure of PAA.

Distribution of the creepex-magnitude correlation parameter for big-depth seismicity in the context of global tectonics

In a number of the author’s papers, the successful application of creepex-parameter of seismicity in the problems of geodynamic research of preparation areas of large events by time-changing both the creepex value in the vicinity of their sources and the pair correlation coefficient of creepex Cr(t) and magnitude MS(t) graphs (the creepex-magnitude correlation KCOR) in accompanying earthquakes swarms or during the preparation of a large shock was demonstrated. With the transition to regional and global levels of research on the KCOR change, the influence of big-depth seismicity (H≥50 km), distributed along the regional and global deep faults closest to the focal zone on the processes of source preparation was discovered. In this paper, the dynamics of KCOR parameter for the entire period covered by the CMT global catalog along the main Earth’s seismic belts is considered. The “main belts” are the global lineaments detected by the GIS-ENDDB system, in the 10% confidence band of which the 100% of all Globe earthquakes with MS≥7.5 are located. In KCOR dynamics a pattern of high correlation of MS(t) and Cr(t) sets was revealed on the eve of several major earthquakes (called “key ones”). This can indicate the creation of a special organized environment state during their preparation, possibly associated with the environment consolidation along the seismic belts (in the case of inverse correlation of MS(t) and Cr(t)), or with increased environmental variability (in the case of direct correlation). The periodic nature of KCOR time-change on a global scale is shown (in the case of its calculation with a fixed size of a time-sliding series), possibly associated with periodic fluctuations in the Earth’s rotation speed.

Fifty years of land use and land cover mapping in the United Arab Emirates: a machine learning approach using Landsat satellite data

This study analyses the spatiotemporal distribution of land use and land cover (LULC) in the United Arab Emirates (UAE) over the past 50 years (1972–2021) using 72 multi-temporal Landsat satellite images. Three machine learning (ML) classifiers, Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF), were tested, with RF finally chosen for its higher performance. Spectral, spatial, topographic, and object aspect attributes were extracted and used as input for the RF algorithm to enhance the classification accuracy. A dataset comprising 46,146 polygons representing four LULC classes was created, with 80% allocated for training and 20% for testing, ensuring robust model validation. The algorithm was trained to develop a machine learning model that classified the data into four LULC classes namely: built areas, vegetation, water, and desert and mountainous regions, producing eight thematic maps for the years 1972, 1986, 1992, 1997, 2002, 2013, 2017, and 2021. The results reveal the dominance of desert and mountainous regions, with their coverage gradually declining from over 97% in 1972 to nearly 91% in 2021. In contrast, built areas grew from less than 1% to nearly 6%, while vegetation cover increased from 0.71% to 2.85%. Water bodies have exhibited periodic fluctuations between 0.4% and 0.35%. These changes are attributed to extensive urbanization, agricultural expansion, forest plantation programs, land reclamation, and megaprojects. Accuracy assessment of the classified maps showed high overall accuracy, ranging from 85.11% to 98.4%. The study provides a unique long-term analysis of the UAE over 50 years, capturing key developments from the 1970s oil boom through subsequent megaprojects at the onset of the new millennium, leading to reduced reliance on oil. These findings underscore the role of machine learning and geospatial technologies in monitoring LULC distribution in challenging environments, and the results serve as a vital tool for policymakers to manage land resources, urban planning, and environmental conservation.

Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks

ABSTRACT During the COVID-19 pandemic, healthcare systems worldwide faced severe strain. This study, utilizing wastewater virus surveillance, identified that periodic spontaneous avoidance behaviours significantly impacted infectious disease transmission during rapid and intense outbreaks. To incorporate these behaviours into disease transmission analysis, we introduced the Su-SEIQR model and validated it using COVID-19 wastewater data from Beijing and Hong Kong. The results demonstrated that the Su-SEIQR model accurately reflected trends in susceptible populations and confirmed cases during the COVID-19 pandemic, highlighting the role of spontaneous collective avoidance behaviours in generating periodic fluctuations. These fluctuations helped reduce infection peaks, thereby alleviating pressure on healthcare systems. However, the effect of these spontaneous behaviours on mitigating healthcare overload was limited. Consequently, we incorporated healthcare capacity constraints into the model, adjusting parameters to further guide population behaviours during the pandemic, aiming to keep the outbreak within manageable limits and reduce strain on healthcare resources. This study provides robust support for the development of environmental and public health policies during pandemics by constructing an innovative transmission model, which effectively prevents healthcare overload. Additionally, this approach can be applied to managing future outbreaks of unknown viruses or “Disease X”.

Observations on the structure of turbulent boundary layers interacting with embedded propeller tip vortices

The study presents observations on the interaction of double-blade propeller tip vortices with a smooth-wall turbulent boundary layer (TBL). The wall-bounded helicoidal vortices from the propeller modify the velocity profiles and turbulence statistics. The effects of two different tip clearances, $\epsilon = 0.1\delta _0$ and $0.5\delta _0$ , at a matched thrust, are explored with particle image velocimetry to understand the dynamics of tip-vortex formation within the logarithmic and wake regions of the boundary layer. The measurements are performed with $\lambda =U_{tip}/U_{\infty }$ in the range 5.3–5.9, and a blade passing frequency ( $\,f_{prop}$ ) of the same order of the boundary-layer time scale ( $\,f_{TBL}$ ). Observations indicate a reduction in the extent of the log region and an enhancement of the wake parameter $\varPi$ , mirroring the behaviour seen in TBLs under adverse pressure gradient conditions. Notably, the slipstream most contracted region exhibits a significant reduction in the skin friction coefficient $C_f$ and an amplification of the velocity fluctuation statistics across the entire boundary layer. At a clearance of $\epsilon = 0.1\delta _0$ , there is evidence of the formation of paired coherent wall-bounded structures. The presence of the wall decreases the amplitude of both periodic and stochastic fluctuations obtained with a phase-locked triple decomposition. An exception is observed behind the propeller for the stochastic fluctuations of the wall-normal component of the flow, which become amplified as the blades move away from the wall. This leads to the creation of a more intense phase-locked two-point spatial coherence than that observed in fluctuations aligned with the streamwise direction. Furthermore, results reveal that reduced tip clearances lead to higher viscous dissipation and more active energy exchange between the mean flow and organized motions.