Reassessing the transport properties of fluids: A symbolic regression approach.

The viscosity and thermal conductivity coefficients of the Lennard-Jones fluid are extracted through symbolic regression (SR) techniques from data derived from simulations at the atomic scale. This data-oriented approach provides closed form relations that achieve fine accuracy when compared to well-established theoretical, empirical, or approximate equations, fully transparent, with small complexity and high interpretability. The novelty is further outlined by suggesting analytical expressions for estimating fluid transport properties across the whole phase space, from a dilute gas to a dense liquid, by considering only two macroscopic properties (density and temperature). In such expressions, the underlying physical mechanisms are reflected, while, at the same time, it can be a computationally efficient alternative to costly in time and size first principle and/or molecular dynamics simulations.

Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods.

In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic expressions. The outcome is a set of mathematical equations, with reduced complexity and increased accuracy, that adhere to existing domain knowledge and can be exploited in fluid property interpolation and extrapolation, bypassing timely simulations when possible.

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.

Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s11831-023-09922-z.

Investigation of Inlet Conditions in The Mixing Process of Nanoparticles and Blood in a T-Shaped Microfluidic Reactor with Small Rectangular Cavities.

During the metastasis of cancer cells, circulating tumor cells (CTCs) are released from the primary tumor, reach the bloodstream, and colonize new organs. A potential reduction of metastasis may be accomplished through the use of nanoparticles in micromixers in order to capture the CTCs that circulates in blood. In the present study, the effective mixing of nanoparticles and the blood that carries the CTCs are investigated. The mixing procedure was studied under various inlet velocity ratios and several T-shaped micromixer geometries with rectangular cavities by using computational fluid dynamics techniques. The Navier-Stokes equations were solved for the blood flow; the discrete motion of particles is evaluated by a Lagrangian method while the diffusion of blood substances is studied by using a scalar transport equation. Results showed that as the velocity ratio between the inlet streams increases, the mixing rate of nanoparticles with the blood flow is increased. Moreover, nanoparticles are uniformly distributed across the mixing channel while their concentration is decreased along the channel. Furthermore, the evolution in time of the blood substances in the mixing channel increases with the increase of the velocity ratio between the two streams. On the other hand, the concentration of both the blood substances and the nanoparticles is decreased in the mixing channel as the velocity ratio increases. Finally, the differences in the dimensions of the rectangular cavities seems to have an insignificant effect both in the evolution in time of the blood substances and the concentration of nanoparticles in the mixing channel.

Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis.

We construct recurrence plots (RPs) and conduct recurrence quantification analysis (RQA) to investigate the dynamic properties of the new Center for Financial Stability (CFS) Divisia monetary aggregates for the United States. In this study, we use the latest vintage of Divisia aggregates, maintained within CFS. We use monthly data, from January 1967 to December 2020, which is a sample period that includes the extreme economic events of the 2007-2009 global financial crisis. We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin. The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008. Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates, we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

Effects of channel size, wall wettability, and electric field strength on ion removal from water in nanochannels.

Molecular dynamics simulations are employed to estimate the effect of nanopore size, wall wettability, and the external field strength on successful ion removal from water solutions. It is demonstrated that the presence of ions, along with the additive effect of an external electric field, constitute a multivariate environment that affect fluidic interactions and facilitate, or block, ion drift to the walls. The potential energy is calculated across every channel case investigated, indicating possible ion localization, while electric field lines are presented, to reveal ion routing throughout the channel. The electric field strength is the dominant ion separation factor, while wall wettability strength, which characterizes if the walls are hydrophobic or hydrophilic has not been found to affect ion movement significantly at the scale studied here. Moreover, the diffusion coefficient values along the three dimensions are reported. Diffusion coefficients have shown a decreasing tendency as the external electric field increases, and do not seem to be affected by the degree of wall wettability at the scale investigated here.

Heavy Metal Adsorption Using Magnetic Nanoparticles for Water Purification: A Critical Review.

Research on contamination of groundwater and drinking water is of major importance. Due to the rapid and significant progress in the last decade in nanotechnology and its potential applications to water purification, such as adsorption of heavy metal ion from contaminated water, a wide number of articles have been published. An evaluating frame of the main findings of recent research on heavy metal removal using magnetic nanoparticles, with emphasis on water quality and method applicability, is presented. A large number of articles have been studied with a focus on the synthesis and characterization procedures for bare and modified magnetic nanoparticles as well as on their adsorption capacity and the corresponding desorption process of the methods are presented. The present review analysis shows that the experimental procedures demonstrate high adsorption capacity for pollutants from aquatic solutions. Moreover, reuse of the employed nanoparticles up to five times leads to an efficiency up to 90%. We must mention also that in some rare occasions, nanoparticles have been reused up to 22 times.

Investigation of speed and trajectory of motorcycle riders at curved road sections of two-lane rural roads under diverse lighting conditions.

INTRODUCTION: Vehicular accidents at horizontal curves are over-represented compared to accidents that occur at tangent sections. Investigations have been conducted aimed at identifying the major causes that result in higher accident risk, both in terms of severity and rate, at curved road sections. Excessive or abrupt changes in speeding and improper vertical position are cited as major factors of lane departure, whereas other factors (either human or environmental) have also been documented. However, most research involves 4-wheel vehicles rather than other modes of transport that behave differently. More specifically, while motorcyclist fatalities occur more frequently than passenger vehicles, when accounting for vehicle distance traveled only a limited number of research studies address their behavior at curved road sections. METHOD: This paper presents the findings of field operational tests carried out by motorcyclists along two-lane rural roads with a wide range of horizontal curves using an instrumented motorcycle. Key objectives of the research included the conditions under which the motorcyclists differentiate their trajectory in regards to the direction of the horizontal curves, the correlation between the trajectory and the geometry of the road, and the impact of the lighting conditions on riders' behavior. RESULTS: The research showed that motorcyclists tend to ride closer to the centerline of the road, neglect the hazards associated with dim lighting conditions, and maintain constant speed in the left hand and the right-hand horizontal curves.

Analysis of magnetohydrodynamic channel flow through complex network analysis.

Velocity time series of hydrodynamic and magnetohydrodynamic (MHD) turbulent flow are analyzed by means of complex network analysis in order to understand the mechanism of fluid patterns modification due to the external magnetic field. Direct numerical simulations of two cases are used, one for the plane hydrodynamic turbulent channel flow at the low Reynolds number of 180, based on the friction velocity, and the corresponding MHD flow with an external streamwise magnetic field with a magnetic interaction number of 0.1. By applying the visibility graph algorithm, we first transformed the time series into networks and then we evaluated the network topological properties. Results show that the proposed network analysis is not only able to identify and detect dynamical transitions in the system's behavior that identifies three distinct fluid areas in accordance with turbulent flow theory but also can quantify the effect of the magnetic field on the time series transitions. Moreover, we find that the topological measures of networks without a magnetic field and as compared to the one with a magnetic field are statistically different within a 95% confidence interval. These results provide a way to discriminate and characterize the influence of the magnetic field on the turbulent flows.

Nanoscale slip length prediction with machine learning tools.

This work incorporates machine learning (ML) techniques, such as multivariate regression, the multi-layer perceptron, and random forest to predict the slip length at the nanoscale. Data points are collected both from our simulation data and data from the literature, and comprise Molecular Dynamics simulations of simple monoatomic, polar, and molecular liquids. Training and test points cover a wide range of input parameters which have been found to affect the slip length value, concerning dynamical and geometrical characteristics of the model, along with simulation parameters that constitute the simulation conditions. The aim of this work is to suggest an accurate and efficient procedure capable of reproducing physical properties, such as the slip length, acting parallel to simulation methods. Non-linear models, based on neural networks and decision trees, have been found to achieve better performance compared to linear regression methods. After the model is trained on representative simulation data, it is capable of accurately predicting the slip length values in regions between or in close proximity to the input data range, at the nanoscale. Results also reveal that, as channel dimensions increase, the slip length turns into a size-independent material property, affected mainly by wall roughness and wettability.

Growth of Staphylococcus epidermidis on the Surface of Teatcups from Milking Parlours.

The growth of two Staphylococcus epidermidis isolates (one biofilm-forming and one not) on teatcups for cattle (made of rubber) or sheep (made of silicone) were assessed in nine multiplicates for 24 h post-smearing on the teatcup surface. Staphylococci were smeared on an area of 0.0003142 m2 on the material and their growth and expansion further on were monitored for 24 h. There were no differences in the frequency of recoveries between the two isolates (p > 0.82 for all comparisons). There were more recoveries from sheep teatcups than from cattle teatcups: 1280/1728 (74.1%) versus 942/1728 (54.5%), for both isolates (p < 0.0001). Significance was observed only 6 h to 15 h after smearing (p < 0.0001 for all comparisons). The median speed of linear dissemination of the isolates was 0.00000021 m s-1 on cattle teatcups and 0.00000033 m s-1 on sheep teatcups (p < 0.0001). The increased growth and faster expansion of staphylococci on silicone teatcups raise important points from a clinical viewpoint. The model could be used in the testing of staphylococcal growth in the material of milking parlours in various conditions.

Molecular Dynamics Simulations of Ion Drift in Nanochannel Water Flow.

The present paper employs Molecular Dynamics (MD) simulations to reveal nanoscale ion separation from water/ion flows under an external electric field in Poiseuille-like nanochannels. Ions are drifted to the sidewalls due to the effect of wall-normal applied electric fields while flowing inside the channel. Fresh water is obtained from the channel centerline, while ions are rejected near the walls, similar to the Capacitive DeIonization (CDI) principles. Parameters affecting the separation process, i.e., simulation duration, percentage of the removal, volumetric flow rate, and the length of the nanochannel incorporated, are affected by the electric field magnitude, ion correlations, and channel height. For the range of channels investigated here, an ion removal percentage near 100% is achieved in most cases in less than 20 ns for an electric field magnitude of E = 2.0 V/Å. In the nutshell, the ion drift is found satisfactory in the proposed nanoscale method, and it is exploited in a practical, small-scale system. Theoretical investigation from this work can be projected for systems at larger scales to perform fundamental yet elusive studies on water/ion separation issues at the nanoscale and, one step further, for designing real devices as well. The advantages over existing methods refer to the ease of implementation, low cost, and energy consumption, without the need to confront membrane fouling problems and complex electrode material fabrication employed in CDI.

On a topological criterion to select a recurrence threshold.

In this work, a topological criterion is proposed for selecting a recurrence threshold for constructing a recurrence plot of a time series. It is based on a metric structure of the set of the recurrence plots that is defined by the recurrence plot deviation distance among recurrence plots, introduced in a previous paper by the authors. In this process for a range of threshold values, the corresponding recurrence plots are constructed. Then, a value of the threshold is considered to be optimal when the image of its recurrence plot remains close to images of the other recurrence plots constructed for close values of thresholds based on the topological criterion introduced in the present work. The results are applied both to time series emanating from the Lorenz dynamical system and to Molecular Dynamic simulations.

On a topological classification of recurrence plots: Application to noise perturbed molecular dynamics time series.

In the present paper, a topological classification of recurrence plots of time series that are constructed with equal embedding dimension and delay time is proposed by defining a metric structure in the set of those recurrence plots. To achieve this, the Recurrence Plot deviation distance and the Recurrence deviation plot are introduced along with qualitative and quantitative indices, which allows for a detailed comparison of the recurrence plots of two time series and their corresponding dynamical systems. In the range of values studied, the application of additive noise on the thermostat results in a less deterministic system behavior, quite close to that of the system with the unperturbed thermostat. On the other hand, multiplicative noise of small strength results in a less deterministic system quite close to the unperturbed system behavior. For a larger strength, the system presents a behavior which is significantly different from that of the unperturbed system. The new methodology introduced here provides detailed quantitative and graphical insights for the way that noise, additive and multiplicative, influences on the morphology of recurrence plots of the unperturbed and noise perturbed time series. The results are encouraging for potential future applications on the analysis of complex dynamical systems.

Effects of wall roughness on flow in nanochannels.

Nonequilibrium molecular dynamics simulation is applied to investigate the effect of periodic wall roughness on the flow of liquid argon through krypton nanochannels. The effect of the length of a rectangular protrusion on the flow is investigated and compared to the case of nanochannels with flat walls. The results show a clear trapping of fluid atoms inside the rectangular cavities that are formed between successive protrusions. The size of the cavities affects the potential energy map and, consequently, fluid atom localization. This localization results in a reduction of velocity values inside the cavities, as well as a reduction of the slip length near the rough wall.