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Hypothesis Atomistically-detailed models of surfactants provide quantitative information on the molecular interactions and spatial distributions at fluid interfaces. Hence, it should be possible to extract from this information, macroscopical thermophysical properties such as interfacial tension, critical micelle concentrations and the relationship between these properties and the bulk fluid surfactant concentrations. Simulations and Experiments Molecular-scale interfacial of systems containing n- dodecyl β -glucoside (APG 12) are simulated using classical molecular dynamics. The bulk phases and the corresponding interfacial regions are all explicitly detailed using an all-atom force field (PCFF+). During the simulation, the behaviour of the interface is analyzed geometrically to obtain an approximated value of the critical micelle concentration (CMC) in terms of the surfactant area number density and the interfacial tension is assessed through the analysis of the forces amongst molecules. New experimental determinations are reported for the surface tension of APG 12 at the water/air and at the water/ n -decane interfaces. Findings We showcase the application of a thermodynamic framework that inter-relates interfacial tensions, surface densities, CMCs and bulk surfactant concentrations, which allows the in silico quantitative prediction of interfacial tension isotherms. • We present a method to relate molecular observations to interfacial tensions. • Atomistic simulation results are provided for n -dodecyl β -glucoside; (APG 12). • New experimental determinations are reported for the water/air and water/ n -decane interfacial tension. • Consistency is seen between simulations, experiments and machine learning predictions. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning based time-domain inversion method is proposed to reconstruct high-contrast scatterers from the measured electromagnetic fields. The scatterers investigated in this study include four kinds of geometry shapes, which cover the arbitrary geometrical shapes, handwritings and lossy medium. After being well trained, the performance of the proposed method is evaluated from the perspective of accuracy, noise interference, and computational acceleration. It can be proven that the proposed framework can realize high-precision inversion in several milliseconds. Compared with typical reconstruction methods, it avoids the iterative calculation by utilizing the parallel computing ability of GPU and thus significantly reduce the computing time. Besides, the proposed method has shown the potential to be applied in practical scenarios with experimental results. Herein, it is confident that the proposed method has the potential to serve as a new path for real-time quantitative microwave imaging for various practical scenarios. In the end, the limitation of the method is also discussed. [ABSTRACT FROM AUTHOR]

This work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response. [ABSTRACT FROM AUTHOR]

Ventilation, cooling, heating, and air conditioning (HVAC) systems represent a substantial portion, approximately 40%, of energy consumption in buildings. Among the key components of HVAC/R systems, the compressor, fans, and electrical resistances are major consumers of electricity. In this context, the compressor stands out due to its significant dynamic behaviour, serving as the pivotal element in heat pump operations. However, many manufacturers solely provide empirical data regarding compressor performance, lacking comprehensive models for assessing performance across various operational conditions. This paper addresses the methodology for evaluating mathematical models utilizing experimental compressor data. Through this approach, we aim to bridge the gap between empirical observations and predictive modelling, enabling a more nuanced understanding and optimization of HVAC/R system performance. The proposed model uses interpolation and it takes as input the evaporation and condensation temperatures. It shows an R -- square and adjusted R -- square value of 0.99. By using the experimental data and an open -- source library for interpolation this paper paves the way for a flexible and practical compressor modelling. [ABSTRACT FROM AUTHOR]

In this paper, we further develop a novel, efficient approach to the problem of signal detection against background noise based on a nonlinear residual functional called the neuron-like criterion function (NCF). A detailed comparison of the NCF-based technique and the conventional correlation criterion function (CCF)-based matched-signal detection is performed. For this purpose, we calculated the detection performance curves for both techniques and found the range of the problem parameters in which the NCF-based detector shows a certain advantage. The latter consists of achieving a fixed value of detection probability at a lower threshold value of the input signal-to-noise ratio (SNR) compared to the CCF-based detector. Special attention is given to the practically important scenario of receiving a weak signal against the background of non-stationary noise with a certain trend (positive or negative) of its intensity. For these two specific cases, modified NCFs are given, which are then used for computer simulation. For both broadband and narrow-band signals, the quantitative bounds of the most effective use of the derived NCFs are established and interpreted. The real sea noise data obtained from two underwater acoustic arrays, one stationary on the sea bottom and the other towed near the sea surface, are used for experimental validation. The experimental data processing results confirm the simulation results and make it possible to demonstrate the advantage of the NCF if the noise intensity shows a significant trend over the signal observation interval. The latter case obviously corresponds to the use of the towed array in the coastal area. [ABSTRACT FROM AUTHOR]

In this article, the performances of the SPH method to solve Shallow Water Equations SWEs with three investigation parameters were studied, such as the type of kernel functions, namely: cubic spline, Gaussian and quintic spline kernels, the number of particles used and the stabilization terms injected, specifically: Lax Friedrichs flux, artificial viscosity and two shocks Riemann solver. Three benchmarking tests make the subject of unsteady free surface flow in this study. It is 1D typical dam-break on wet and dry bottom; 2D partial dam-break on a dry floodplain and 2D partial dam-break on channel with 90° bend. The analysis of the different errors between the numerical and analytical solutions and/or the experimental data shows that the SPH method gives reliable values with the selected optimal parameters which are the cubic kernel function and the artificial viscosity term. The increase in the number of particles increases the precision but also the calculation time. [ABSTRACT FROM AUTHOR]

The purpose of the research is to study the allergenic and immunotoxic properties of pantohematogen obtained from the blood of female Gorno-Altai maral deer as a functional ingredient of specialized products. Allergenic and immunotoxic properties were assessed in 2020–2022 by intragastric administration of pantohematogen to 395 CBA mice weighing 15–18 g, who received the drug at a therapeutic dose of 50 mg/kg and a maximum single dose of 500 mg/kg. The sensitization of the animal body after a course of administration of pantohematogen was determined by deregulation of mast cells. It has been established that when using the maximum single dose, the degranulation reaction does not exceed 0.2. It is necessary to emphasize that no changes in the leukocyte lysis coefficient were recorded in mice receiving different doses of pantohematogen. The effect of pantohematogen on the mass and cellularity of the immunocompetent organs of animals is also shown. It was noted that even the use of a dose (500 mg/kg) 10 times higher than the therapeutic dose does not affect the cellularity of immunocompetent organs. In general, the materials of experimental studies indicate the absence of allergenic and immunotoxic properties of the tested pantohematogen as a functional ingredient in the development of specialized products with specified therapeutic and prophylactic properties.

AbstractThe safe operation of nuclear power plants relies on maintaining the structural integrity of various systems and components, such as equipment-piping systems. Ageing and degradation from flow-accelerated erosion and corrosion can lead to cracks and leakages, posing risks like loss of coolant accidents (LOCA). To prevent such incidents, regular monitoring and maintenance are vital. Recent efforts to implement Artificial Intelligence (AI)-driven condition monitoring aim to enhance safety and efficiency. However, the effectiveness of simulation-based degradation detection models needs to be validated using experimental or real-time data from nuclear power plants. This research explores an AI-based monitoring framework’s validity for nuclear equipment-piping systems through experimentation. A piping system is designed and subjected to harmonic excitations representing typical pump-induced vibrations in nuclear plants. Degradation levels are classified based on wall thickness loss as minor, moderate or severe. Non-uniform degradation is implemented at structural discontinuities such as the elbows. Sensor response is collected from accelerometers installed on the experimental system and its corresponding digital twin. Deep neural networks such as multilayer perceptron and convolutional neural networks are developed to detect the degraded locations and their severity. The results from the experimental data, as well as the simulated data, are compared for accuracy. [ABSTRACT FROM AUTHOR]

An equibiaxial tension test could be necessary to set up hyperelastic material constants for elastomers exactly. Unfortunately, very often, only uniaxial tension experimental data are available. It is possible to use only uniaxial data to compute hyperelastic constants for a hyperelastic model, but the prediction of behavior in different deformation modes (as is equibiaxial or pure shear) will not work correctly with this model. It is quite obvious that there is some relation between uniaxial and equibiaxial behavior for the elastomers. Thus, we could use uniaxial data to predict equibiaxial behavior. If we were able to predict (at least approximately) equibiaxial data, then we could create a hyperelastic model usable for the general prediction of any deformation mode of elastomer. The method of the appropriate processing of experimental data for such prediction is described in the article and is verified by the comparison with the experiment. The presented results include uniaxial and equibiaxial experimental data, the created average curve of both the deformation modes, and the predicted equibiaxial data. Using Student's t-test, a close coincidence of the real and predicted equibiaxial data was confirmed. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study on the main parameters and performance evaluation of microjet engines, at take-off regime and at various engine working regimes, based on thermodynamic analysis of a particular engine data library, from different engine manufacturers such as JetCat and AMT Netherlands. The studied engines have the same spool design but different thrust classes ranging from 97 to 1569 N. The particular data library includes engine specifications from catalogs or data sheets as well as our own experimental data from the JetCat P80 microjet engine, obtained using the ET 796 Jet Turbine Module, a complete testing facility for gas turbine education purposes. Various ratios and differences between certain engine main parameters and performances are studied in order to calculate values through which the analyses can be performed. Even if the engines have different thrust classes, the study examines if there are close values of the ratios and differences of parameters, that can be defined as reference parameters through which the engine performance can be compared and evaluated. [ABSTRACT FROM AUTHOR]

Behind a retaining wall, the mean effective stress mainly decreases during an excavation phase following an unloading stress path. The volumetric strains generated by purely elastic soils are systematically dilative which induces aberrant ground uplifts. The introduction of plasticity along with a nonlinear elastic domain turns out to be essential for a realistic prediction of ground movements. In this paper, a numerical analysis is carried out using a finite element code considering an advanced soil constitutive model called "Generalized Hardening Soil" which has been recently developed. This model contains the exact same set of features as the Hardening Soil Small Strain model but with the possibility to activate each of its plastic and nonlinear elastic mechanisms independently. The role of these mechanisms are investigated to assess their impact on the shape and the amplitude of the ground movements. Numerical results demonstrated that plasticity triggers the main contractive volumetric strains leading to settlements. Nevertheless it cannot fully compensate the elastic uplifts due to unloading. The insertion of strain dependent stiffness is essential as well as the stress dependency. A back analysis of the historical excavation of the Taipei National Enterprise Center permitted to validate these findings. [ABSTRACT FROM AUTHOR]

The larval development of a fish parasite, Acanthocephalus tenuirostris, has been studied under experimental conditions. This species is common in the eastern regions of Russia, from Primorye to the upper reaches of the Kolyma River. The material for the experiments has been collected from different areas of Magadan oblast, which is located mainly in the subarctic climate zone. In several series of experiments, intermediate hosts (Asellus hilgendorfii) are infected and maintained at a constant temperature of 15°C. In all cases, no more than 45 days are required for the formation of the final cystacanth stage. Along with cystacanths, a large number of other larvae at earlier developmental stages are simultaneously recorded in the hemocoel of the isopods. An additional experiment, carried out at 15°C for the first 45 days and 4–6°C for the next 207 days, has shown the possibility of the further development of larvae at low temperatures. A comparison with the available literature data on the timing of development of European Acanthocephalus species living in temperate climate has suggested that A. tenuirostris has adapted to cold climate conditions, which provides a twofold acceleration of the rate of its development compared to larval A. lucii in Europe. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, a mathematical model of the transfer process in a layer under the condition of given experimental data on a part of the layer boundary is presented and investigated. Such research is important for the mathematical description of the objects and systems for which, based on physical considerations, it is impossible to correctly impose boundary or initial conditions, even in a sufficiently general form, but there are experimental data on the desired function or its derivative at the boundary of the body or at the initial time. The values of the desired function at the boundary are known at certain moments in time. The boundary condition is constructed by the experimental data and the initial-boundary value problem, with such a boundary condition, is formulated and solved. The influence of the statistical characteristics of the sample of experimental data on the solution to the initial-boundary value problem is analyzed, and a two-sided statistical estimation of the solution is determined. The confidence intervals for the coefficients of the regression equation and the corresponding confidence intervals for the sought function are established. The influence of the statistical characteristics of the sample on the sought function at the lower boundary of the layer is investigated. Numerical analysis of the solution to the initial-boundary value problem is carried out depending on the statistical characteristics of the sample. Various cases of samples by size and variance are considered. Numerical solutions are studied under the conditions of large and small time intervals of the considered process. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) is a powerful and promising tool for data analysis, but yet not exploited enough. It is a multidimensional method which can extract the topological features contained in a given dataset. An original TDA-based method allowing to monitor the health of structures when equipped with piezoelectric transducers (PZTs) is introduced here. Using a Lamb wave based Structural Health Monitoring (SHM) approach, it is shown that with specific pre-processing of the measured time-series data, the TDA (persistent homology) for damage detection and classification can be greatly improved. The TDA tool is first applied directly in a traditional manner in order to use homology classes to assess damage. After that, another method based on slicing the temporal data is developed to improve the persistence homology perception and to leverage topological descriptors to discriminate different damages. The dataset used to apply both methods comes from experimental campaigns performed on aeronautical composite plates with embedded PZTs where different damage types have been investigated such as delamination, impacts and stiffness reduction. The proposed approach enables to consider a priori physical information and provides a better way to classify damages than the traditional TDA approach. In summary, this article demonstrates that manipulating the topological the features of PZTs time-series signals using TDA provides an efficient mean to separate and classify the damage natures and opens the way for further developments on the use of TDA in SHM. [ABSTRACT FROM AUTHOR]

It is found that without any additional assumptions, Nernst's equation can be re-deduced from the experimental data obtained from the thermodynamic systems at ultra-low temperatures, which is different from the derivation processes in textbooks and literature, and consequently, the physical content included by Nernst's equation should not be referred to as Nernst's postulate or Nernst's theorem. It should be renamed as the Nernst statement. This discovery will play an important role in improving the theoretical framework of thermodynamics. It can effectively prevent some artificial assumptions into the third law of thermodynamics, making it a true reflection of the objective world. It solves the awkward problem caused by using a thermodynamic theorem as the core contents of a thermodynamic law for over one hundred years. [ABSTRACT FROM AUTHOR]

In the present review, the merits and demerits of machine learning (ML) in materials science are discussed, compared with first principles calculations (PDE (partial differential equations) model) and physical or phenomenological ODE (ordinary differential equations) model calculations. ML is basically a fitting procedure of pre-existing (experimental) data as a function of various factors called descriptors. If excellent descriptors can be selected and the training data contain negligible error, the predictive power of a ML model is relatively high. However, it is currently very difficult for a ML model to predict experimental results beyond the parameter space of the training experimental data. For example, it is pointed out that all-dislocation-ceramics, which could be a new type of solid electrolyte filled with appropriate dislocations for high ionic conductivity without dendrite formation, could not be predicted by ML. The merits and demerits of first principles calculations and physical or phenomenological ODE model calculations are also discussed with some examples of the flexoelectric effect, dielectric constant, and ionic conductivity in solid electrolytes. [ABSTRACT FROM AUTHOR]

Validated CO2 ejector models are essential for developing high-performance refrigeration and heat pump cycles. This study focuses on assessing the Homogeneous Equilibrium Model’s applicability to simulate a CO2 flashing ejector at a reduced pressure of 0.47. The model was implemented in FLUENT, integrating a user-defined real gas model. Simulation results with different boundary condition options were compared to experimental data. The analysis was carried out to evaluate the predictive capabilities of the model and assess the experimental data quality. The results indicate that the developed model accurately estimated the motive mass flow rate, with a maximum relative error of 5.7%, showing better performance than previously reported data. The entrained flow rate, assuming double choking operation, was significantly higher than the experimental measurement, and the CFD-predicted wall static pressure underestimated the experimental profile, suggesting single-choked ejector operation. In contrast, the outflow density was better predicted under the same assumption, with an average error of 8.6%. Nevertheless, the simulated temperature profiles showed good agreement with the experimental data, especially when using the experimental entrained mass flow rate as a boundary condition.

Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time simulations (MLTSs) has been proposed. The main purpose of this work is to demonstrate how significantly different results from a minimum lap time optimal control problem can be obtained when using inappropriate elevation data sources in the track reconstruction problem, and how the road model reconstructed using poor input data can lead to misleading conclusions when analyzing real vehicle and driver performances. Two road models derived from high- and low-resolution digital elevation models (DEMs) are compared and their impact on the optimal maneuver of a racing motorcycle is examined. The essentials of track identification are presented, as well as vehicle positioning on the 3D road and the generalized QSS motorcycle model. Obtained 3D and 2D road models are analyzed in detail, on a case example of the Road Atlanta racetrack, and used in minimum lap time simulations, which are validated by the experimental data recorded on the Supersport motorcycle. The comparative analysis showed that great care should be taken when selecting the elevation dataset in the track reconstruction process, and that the 1 m resolution local DEMs seem to be sufficient to obtain MLTS results close to the measured ones. The example of using the 3D free-trajectory QSS minimum lap time problem to localize the track segments where real driver actions can be improved is also presented. The differences between simulation results on different road models of the same racetrack can be large and influence the interpretation of optimal maneuver. [ABSTRACT FROM AUTHOR]

The preservation of foods such as milk, meat, and vegetables through fermentation results in products like yogurt, cheese, pickles, sausages, and silage with an extended shelf life compared to their natural unprocessed counterparts. This work aims to formulate a mathematical model of first-order ordinary differential equations (ODEs) that accounts for both the physicochemical and microbiological parameters affecting biomass kinetics [B(t)], acidity [A(t)], and viscosity [V(t)] as a function of temperature across different yogurt samples. In order to validate the efficacy of the model in predicting yogurt shelf life, we compared its fitting results with commonly employed systems or equations, including the Weibull model, the Reaction Order model, the Arrhenius Equation, and the Q10 Factor. Our evaluation, based on R-squared (R2) values exceeding 0.95, demonstrates the robustness of the proposed model. Furthermore, all parameters were estimated along with their corresponding 95 % confidence intervals. The mathematical model estimates the dynamic of each of the physicochemical and microbiological parameters which will help to predict the behavior over time of the shelf life of yogurt at different temperatures. [ABSTRACT FROM AUTHOR]

Since the beginning of the 20th century, many studies have focused on the possibility of considering the sky as a body characterized by an apparent temperature, and several correlations to quantify the apparent sky temperature have been proposed. However, the different models were obtained for specific meteorological conditions and through measurements at specific sites. The available models do not cover all locations in the world, although the evaluation of the sky temperature is fundamental for estimating the net radiative heat transfer between surfaces and the sky. Here, experimental data logged from a regional micrometeorological network (in Italy, within the Lazio region) were processed and used to identify an empirical model for the estimation of the sky temperature in the Mediterranean area. Data relating to atmospheric infrared radiation were used to compute the sky temperature, aiming at identifying a direct correlation with the ambient temperature. Climatic data acquired during 2022 were processed. The proposed correlations were compared with other models available in the literature, including the standard ISO 13790. This study proposes an annual-based direct correlation in its initial phase, demonstrating a superior fit to the measured data compared to well-known direct empirical models from the literature. Subsequently, quarterly-based correlations are introduced further in a secondary phase of the work to improve the model's adaptation to experimental observations. The results reveal that quarterly-based correlations improve goodness-of-fit indexes compared to annual-based and well-known direct empirical correlations. Finally, a detached building was modeled via a dynamic code to highlight the influence of different correlations on annual energy needs. [ABSTRACT FROM AUTHOR]

The results of measurements of the thermal conductivity carried out in a gas phase for six binary mixed refrigerants, consisting of hydrofluorocarbons R-125, R-134a, and R32, are analyzed. For each mixture, an equation is given for calculating its thermal conductivity depending on temperature and pressure. The previously proposed model for predicting the thermal conductivity of vapors of mixed refrigerants in a wide range of state parameters has been refined in application to the R-125/R-134a and R32/R-125 systems. [ABSTRACT FROM AUTHOR]

Model updating procedures based on experimental data are commonly used in case of historic buildings to identify numerical models that are subsequently employed to assess their structural behaviour. The reliability of these models is closely related to their ability to account for all the uncertainties that are involved in the knowledge process. In this regard, to handle these uncertainties and quantify their propagation, Bayesian inference is frequently employed being able to deal with the effects of parameter uncertainty, observation errors and model inadequacy. The computation of the posterior distribution through Bayesian inference needs–however–the evaluation of the likelihood function, which requires solving complex multi-dimensional integration problems. To bridge this shortcoming, the paper compares two Bayesian inference approaches to show how different approximations affect the results of simulated inference: a discrete approach for the likelihood computation in the Bayesian Model Updating (BMU) and a Monte Carlo likelihood-free method known as Approximate Bayesian Computation (ABC) are reported. As reference, the typology of historic masonry towers was considered by using their natural frequencies as experimental data for model updating. The two procedures provide very similar results supporting the validity of both methods despite ABC turns out to be a more flexible approach. [ABSTRACT FROM AUTHOR]

AbstractThe presented work deals with the improvement of the evaporation model of the ATHLET (Analysis of Thermal and Hydraulics of Leaks and Transients) system code to be applied to a passive containment cooling system of a nuclear power plant. For the model validation, INTRAVIT (Investigation of Passive Heat Transfer in a Variably Inclined Tube) test facility setup at the University of Luxembourg was used. The first part of the paper presents a review of the existing literature on evaporation models that revealed that those models significantly simplify the physical processes that occur. Next, a modified evaporation model is proposed that offers a realistic description of various evaporation processes and the start of bubble formation using a nucleation model, and a surface density calculation model is introduced that is necessary for evaporation simulation. The final part of this work explored five different system configurations to test the evaporation model: three condenser tube inclinations (5 deg, 60 deg, and 90 deg), two riser lengths (1 m and 2.5 m), and different thermal loads. They made it possible to simulate several experiments for stable and unstable natural circulation and to verify the proposed model. [ABSTRACT FROM AUTHOR]

Measurements of the viscosity of pure hydrogen and a binary (hydrogen + methane) mixture with a nominal composition 90 mol % hydrogen are presented. The measurements were conducted with a two-capillary viscometer relative to helium along three isotherms of (298.15, 323.15, and 348.15) K and at pressures of up to 18 MPa. Expanded relative combined uncertainties in viscosity range from (0.65 to 2.7) % (k = 2) for the hydrogen data, and from (0.91 to 3.2) % (k = 2) for the (hydrogen + methane) data. The viscosity data are compared to experimental literature data and viscosity correlations implemented in the NIST REFPROP v10.0 database. Good agreement between this work's data, literature data, and the viscosity correlation was achieved for pure hydrogen. The (hydrogen + methane) mixture was compared to the Extended Corresponding States (ECS) model implemented in REFPROP v10.0. Relative deviations between the experimental data and the ECS model exceed the experimental uncertainty and were found to exhibit a positive trend with increasing density and a weakly pronounced negative trend with increasing temperature. No experimental literature data are available at overlapping state regions. Nonetheless, deviations to the ECS model imply reasonable consistency of this work's data and literature data. In addition to experimental viscosities, experimental zero-density viscosity ratios of the fluids under investigation and helium are reported. Fairly good agreement within the experimental uncertainty of this work with a highly accurate literature value and a value obtained from accurate ab initio calculated data was achieved for hydrogen. [ABSTRACT FROM AUTHOR]

This paper reports a novel investigation in applying commercial Ultra High-Frequency RFID tags (UHF-RFID tags), which are widely used in logistics as sensing elements in civil engineering structures, particularly for monitoring out-of-plane displacements of brick masonry walls. Both laboratory tests and in situ experimental tests assessed the feasibility of the proposed application. Laboratory tests showed a very satisfactory response while the in situ experiments showed a weaker response. Nevertheless, the potential reliability of the proposed technique can be stated. The authors traced back the causes of the performance decrease to environmental interference, mainly due to the extensive presence of a rigid steel frame surrounding the out-of-plane loaded panels. Measurements of displacements, in fact, are obtained indirectly from the phase of UHF-RFID signals that strongly suffer from multipath generated by metallic surfaces. Despite some limitations, the proposed measurement technique permits a reliable and sustainable approach to the monitoring of structures. The use of commercial UHF-RFID wireless tags, in fact, assures easy and fast installation operations and assures the possibility of placing a large number of sensors over the structure with very low maintenance costs with respect to the more traditional monitoring techniques. Moreover, using very thin and small commercial UHR-RFID tags on cultural heritage structures can represent an opportunity for sustainable long-time monitoring with reduced costs. Overall, the results of this study are sufficiently satisfactory to be considered as the opening of new possible scenarios in wireless structural monitoring in the civil engineering field. The authors propose as future work to use UHF-RFID tags for the real-time monitoring of an existing masonry facade that, not being characterized by the presence of a steel frame, can potentially assure an adequate response and properly transmit the electromagnetic signal. [ABSTRACT FROM AUTHOR]

Cellular Automata have successfully been successfully applied to the modeling and simulation of pedestrian and crowd dynamics. In particular, the investigated scenarios have often been focused on the evaluation of medium–high population density situations, in which the motivation of pedestrians to reach a certain location overcomes their tendency to naturally respect proxemic distances. The global COVID-19 outbreak, though, has shown that sometimes it is crucial to contemplate how proxemic tendencies are emphasized and amplified by the affective state of the individuals involved in the scenario, representing an important factor to take into consideration when investigating the behaviour of a crowd. In this paper we present a research effort aimed at integrating results of quantitative analyses regarding the effects of affective states on the perception of distances maintained by different types of pedestrians with the modeling of pedestrian movement choices in a cellular automata framework. [ABSTRACT FROM AUTHOR]

Numerical methods are very useful for predicting water levels, velocities and flow rates in hydraulic systems. The Saint-Venant system of equations, which is a hyperbolic partial differential system, is widely used in the modelling of a flood wave due to a dam failure. In this study, two explicit finite difference Lax-Friedrichs and modified Lax-Friedrichs numerical schemes are used to simulate the failure wave of a dam. The calibration of the obtained results is done in relation to experimental measurements and numerical results existing in the literature specialized in this field of research. The experimental set-up consists of a water reservoir that simulates the reservoir of a dam, followed downstream by a horizontal dry bottom section, a triangular bottom sill with steep slopes and a small tide of water at rest after the triangular obstacle which ends with a vertical diaphragm. The simulation obtained results with the new formulation of the Lax-Friedrichs numerical scheme showed good agreement with the simulated, experimental and numerical results of other researchers. [ABSTRACT FROM AUTHOR]

Green roofs are considered sustainable practice s in the urban environment as they provide several benefits to the city and to the building such as mitigating the urban heat island effect and optimizing the energy performance of buildings. The impact of green roofs on the energy balance of urban building surfaces in warmer climates, such as the Mediterranean, is still under investigation. In this study, 5 years of experimental data recorded between 2015 and 2020 were analyzed in comparison between a non-insulated flat roof and a green roof. The aim of this study is to investigate the thermal regulation capacity of green roofs on the energy balance of buildings and their efficiency as potential UHI mitigation strategies to make cities more resilient. Results of the study demonstrated that despite the limited depth of the substrate and plant diversity, the green roof provided thermal regulation to the building by reducing temperature fluctuations and moderating temperatures for both the building and its surrounding environment. Practical application: This study provides evidence that the implementation of a green roof could effectively moderate roof temperatures and reduce fluctuations, especially during the summer season. Furthermore, the study demonstrated that even a simple and shallow extensive green roof could provide thermal benefits in a Mediterranean climate. Therefore, due to its structural lower weight, this type of green roofs could be installed on existing buildings and could provide thermal benefits to the building and the city. [ABSTRACT FROM AUTHOR]

This study employs an inverse grey-box (IGB) modeling approach, which combines measured data and the physics of systems to predict the performance of shallow underground thermal energy storage (UTES) with top and side insulation. A simplified IGB model of the shallow UTES was developed using thermal network analysis. Experimental studies were conducted for shallow vertical and horizontal UTES configurations. Detailed models representing the field experiments were developed using the TRNSYS simulation tool and calibrated with the measured data from the experimental setup. The 4 Resistance 2 Capacitance (4R2C) IGB model was trained and tested in MATLAB using field experimental data and the data from the calibrated TRNSYS model. In comparing experimental data with the TRNSYS model for UTES, the prediction of outlet water temperature showed good agreement, with root mean square error (RMSE) and coefficient of variation of RMSE (CVRMSE) values of 0.94 °C and 3.16 % for vertical, and 0.99 °C and 2.97 % for horizontal configurations. The IGB model also aligned well with the experimental data, showing CVRMSE of 7.91 % and 3.17 % for vertical and horizontal systems, respectively. Sensitivity analysis revealed that model performance improves with longer training durations and closer testing times to training periods. However, a convergence point of 20 weeks of training data was achieved for making long-term performance predictions.

Environmental policies and commitment is forcing a strong push towards electrification of human activities. Efforts are oriented to progressively dismiss old and outdated Internal Combustion Engines, replacing oil energy sources with electricity vectors. An important switch is involving transportation sector, responsible for 16% of global emissions produced, where the electrification process is converting the private mobility to the use of more sustainable electric vehicles. The drawbacks related to the battery capacity and range is limiting the diffusion among the whole territory, especially where a lack of charging infrastructure is persistent. Therefore, a focus on the adopted driving behaviour must be set to carry benefits in energy saving and potentially extending range. The present paper provides an analysis on different driving behaviour based on real-world data. An experimental campaign is described to acquire different driving styles during test-runs on a real route. An electric vehicle model is also developed in parallel and validated on the real dataset. A test route is identified in the southern region of Puglia in Italy, centered around the city of Lecce, and altitude and speed profiles are acquired and processed into the model. Test-runs were first clustered based on the driver type, then according to the level of auxiliary power consumption. Then, with the help of EV model, a more detailed analysis could be set to retrieve meaningful motivations behind the trends gathered between the two driving styles, especially focusing on the type of road. Results allowed to extrapolate trends between energy consumption and driving styles, showing a considerable impact on the battery usage. A +5% SoC is estimated if an aggressive driving style is adopted, while the impact of auxiliary power is predominant with an eco-driving style, taking into account uneven testing conditions.

Future traffic contexts will likely involve the coexistence of human-driven vehicles and connected and automated vehicles (CAVs). To assess the impact of CAVs, especially in large-scale applications, intermediate hybrid multi-scale models can be used. These models are easily adaptable to traffic control strategies by employing disaggregated modeling in regions where such strategies are implemented and macroscopic modeling in other regions indirectly affected by the controlled infrastructure. This paper focuses on a model previously established in the literature, the H - CA&CTM (Hybrid Cellular Automata -CA- Cell Transmission Model-CTM), with an emphasis on the micro model that can be implemented in the hybrid traffic flow model. The research has two primary aims: 1) Investigate the calibration of the CA model with respect to various cell lengths using two distinct approaches: simulating all vehicles together in a closed ring layout and simulating each vehicle using data obtained from its respective follower; 2) Utilize vehicle trajectory data for the calibration procedure, enabling a comprehensive comparison of methods. Two detailed approaches were considered: 1. Measured Leader – Simulated Follower interaction approach. 2. Simulated Leader – Simulated Follower interaction approach. The major finding of the paper is that the calibrated parameters obtained using the Simulated Leader approach display greater regularity across different cell lengths.

Reinforced concrete slab-column connections are vulnerable to punching shear failure when subjected to combined gravity and lateral loadings during earthquakes. Over the years, many experimental campaigns have been conducted and assessed the seismic performance of flat slabs. The experimental findings were used to derive empirical equations for the design provisions. The paper aims to provide a detailed description of the code development for seismic punching shear. Two current code provisions (ACI 318-19, EC 2 & EC 8-2004) for seismic punching shear are presented and discussed. Relevant and updated experimental data of interior slab-column connections without and with shear reinforcement are selected and assessed against the current design provisions with a focus on key response parameters such as the drift ratio and the gravity shear ratio. The ACI 318-19 limit drift line is considered to assess the lateral deformation demand for the connections with respect to the updated database. The gravity shear ratio is shown to have a considerable impact on the limit drift ratio of slab-column connections without shear reinforcement. The majority of the test specimens with no shear reinforcement experienced punching shear failure, followed by the specimens which showed a combined flexure–punching failure. Punching shear failures occurred for a range of gravity shear ratios of 0.2 to 0.9, while all combined flexure–punching shear failures occurred for gravity shear ratio values below 0.5. The shear-reinforced concrete slab-column connections can achieve much higher drift ratios. Most of the slabs were reinforced with shear studs, particularly in the range of gravity shear ratios of 0.4 to 0.6. Most test specimens failed by flexure for gravity shear ratio values of 0.1 to 0.7, followed by the specimens which failed in combined flexure–punching for gravity shear ratio values that ranged from 0.3 to 0.9. Finally, the available numerical and analytical models for seismic punching shear are presented with the objective of observing their potential strengths and limitations. [ABSTRACT FROM AUTHOR]

This paper considers a linear continuous- or discrete-time dynamic object in the absence of its mathematical model. As is demonstrated below, a control law that suboptimally damps initial and (or) exogenous disturbances of such objects can be implemented based on experimental and a priori data. The approach involves the methods of robust control design and duality theory as well as the technique of linear matrix inequalities. [ABSTRACT FROM AUTHOR]

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