Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

Despite being stored at 113 K and atmospheric pressure, LNG cold potential is not exploited to reduce green ships’ energy needs. An innovative system based on three Organic Ranking Cycles integrated into the regasification equipment is proposed to produce additional power and recover cooling energy from condensers. A first-law analysis identified ethylene and ethane as suitable working fluids for the first and the second ORC making available freshwater and ice. Propane, ammonia and propylene could be arbitrarily employed in the third ORC for air-conditioning. An environmental analysis that combines exergy efficiency, ecological indices and hazard aspects for marine environment and ship’s passengers, indicated propylene as safer and more environmentally friendly. The exergy analysis confirms more than 20% of the LNG potential can be recovered from every cycle to produce a net clean power of 76 kW, whereas 270 kW can be saved by recovering condensers' cooling power to satisfy some ship needs. Assuming the sailing mode, a limitation of 162 kg in LNG consumptions was determined, avoiding the emission of 1584 kg of CO2 per day. Marine thermal pollution is reduced by 3.5 times by recovering the working fluids' condensation heat for the LNG pre-heating.

When wildlife and motor vehicles collide, the result for the animals is often death (roadkill). A commercial roadkill Virtual Fence (VF) mitigation device (iPTE Traffic Solutions) was used in a field trial to test its effectiveness along a 4.9-km segment of road on Bruny Island, Tasmania. A total of 585 days of monitoring of roadkill by species was conducted, with six sections alternatively switched on or off, according to a variation of a Crossover and Multiple Before-After-Control-Impact (MBACI) experimental designs that divided monitoring into “off-on” then “on-off” blocks of periods with each “on” period followed by a 16 d “wash-out” period. For the six sections over the four periods that exclude wash-outs, the 24 aggregated values of daily counts of roadkill of Tasmanian pademelons (Thylogale billardierii), that gave a total count of 222, were modelled. Other species were killed in insufficient numbers for analysis to be informative. The statistical analysis exploited the MBACI design to estimate the VF effect using a log-odds ratio parameter (LORP) while accounting for local spatio-temporal effects. Both versions of the analysis, either averaging over the three spatial replicates (paired sections) or two temporal replicates (blocks) gave no statistically significant effect of the VF judged as a LORP estimate not sufficiently below zero. Corresponding percentage reduction estimates of 9% and 16% were derived from the LORP estimates and given parameter uncertainty estimates the power to detect as significant a LORP corresponding to a 50% reduction in rate were 0.5 and 0.6, respectively. This study confirms results from a similar field trial carried out in 2018 also in southern Tasmania and adds to the evidence that this VF is likely to give if anything only a minor reduction in roadkill for a commonly killed species for which researchers have the best chance of detecting any practically useful reduction.

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

This study revisits the Reynolds-averaged Navier--Stokes equations (RANS) and finds that the existing literature is erroneous regarding the primary unknowns and the number of independent unknowns in the RANS. The literature claims that the Reynolds stress tensor has six independent unknowns, but in fact the six unknowns can be reduced to three that are functions of the three velocity fluctuation components, because the Reynolds stress tensor is simply an integration of a second-order dyadic tensor of flow velocity fluctuations rather than a general symmetric tensor. This difficult situation is resolved by returning to the time of Reynolds in 1895 and revisiting Reynolds' averaging formulation of turbulence. The study of turbulence modeling could focus on the velocity fluctuations instead of on the Reynolds stress. An advantage of modeling the velocity fluctuations is, from both physical and experimental perspectives, that the velocity fluctuation components are observable whereas the Reynolds stress tensor is not.

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

The role of energy and entropy in decomposition of turbulent velocity flow-fields is to be shown in the paper. The decomposition methods based on energy concept are taken into account, namely the Proper Orthogonal Decomposition (POD) and its extension Bi-Orthogonal Decomposition (BOD). Entropy motivated view on the decomposed modes could offer new possibilities in the modes physical interpretation and in Reduced Order Modelling (ROM) strategy efficiency evaluation.

The expansion of renewable energies is progressing strongly. The influence on the power supply networks by the volatility of the infeed must be met with new concepts. In this paper we investigate the possibilities of integrating microgrids as a cooperating unit in the power supply network to support further expansion of RES power plants. In this paper a differentiation of microgrids from similar network structures is established, a classification of proposed groups is made. Then, after the description of simulation of components in a microgrid, with practical advice, an example model is shown, which aids the dimensioning of the components within a microgrid to achieve a specified goal.

Cancers are complex, adaptive ecosystems. They remain the leading cause of disease-related death among children in North America. As we approach computational oncology and Deep Learning Healthcare, our mathematical models of cancer dynamics must be revised. Recent findings support the perspective that cancer-microenvironment interactions consist of turbulent flows. As such, cancer pattern formation, protein-folding and metastatic invasion are discussed herein as processes driven by chemical turbulence within the framework of complex systems theory. Current state-of-the-art quantitative approaches used in reconstructing cancer stem cell networks are reviewed. To conclude, cancer stem cells are presented as strange attractors of the Waddington landscape.

The strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a Lean Learning Factory was developed in the Pitesti University Center, which allows students to learn, experiment, and research, in an environment similar to that of enterprises, the new Lean Manufacturing methods and techniques, and Industry 4.0. The research presented in this study had as its objective identifying the minimum number of repetitions necessary to train operators who serve two differently organized workstations, one classic, the other including digital techniques, but on which the same assembly operation is performed. Several indicators were considered in the analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percentage ratio between the standard duration of cyclical activities and the effective duration of the work cycle. The evolution of these indicators was mathematically modelled by regression functions, using the least squares method. The obtained results also highlight the usefulness of applying the DOJO method as a Lean manufacturing-specific learning technique and the efficiency of implementing digital techniques in work organization.

This paper brings to fruition recently found \cite{migdal2023exact} exact reduction of decaying turbulence in the Navier-Stokes equation in $3 + 1$ dimensions to a Number Theory problem of finding the statistical limit of the Euler ensemble. We reformulate the Euler ensemble as a Markov chain and show the equivalence of this formulation to the \textbf{quantum} statistical theory of $N$ fermions on a ring, with an external field related to the random fractions of $\pi$. We find the solution of this system in the turbulent limit $N\to \infty, \nu \to 0$ in terms of a complex trajectory (instanton) providing a saddle point to the path integral over the density of these fermions. This results in an analytic formula for the observable correlation function of vorticity in wavevector space. This is a full solution of decaying turbulence from the first principle without approximations or fitted parameters. The energy spectrum decays as $k^{-\frac{7}{2}}$. We compute resulting integrals in \Mathematica{} and present effective index $n(t) = -t\partial_t \log E $ for the energy decay as a function of time Fig.\ref{fig::NPlot}. The asymptotic value of the effective index in energy decay $n(\infty) = \frac{5}{4}$, but the universal function $n(t)$ is neither constant nor linear.

We have found an infinite dimensional manifold of exact solutions of the Navier-Stokes loop equation for the Wilson loop in decaying Turbulence in arbitrary dimension $d >2$. This solution family is equivalent to a fractal curve in complex space $\mathbb C^d$ with random steps parametrized by $N$ Ising variables $\sigma_i=\pm 1$, in addition to a rational number $\frac{p}{q}$ and an integer winding number $r$, related by $\sum \sigma_i = q r$. This equivalence provides a \textbf{dual} theory describing a strong turbulent phase of the Navier-Stokes flow in $\mathbb R_d$ space as a random geometry in a different space, like ADS/CFT correspondence in gauge theory. From a mathematical point of view, this theory implements a stochastic solution of the unforced Navier-Stokes equations. For a theoretical physicist, this is a \textbf{quantum} statistical system with integer-valued parameters, satisfying some number theory constraints. Its long-range interaction leads to critical phenomena when its size $N \rightarrow \infty$ or its chemical potential $\mu \rightarrow 0$. The system with fixed $N$ has different asymptotics at odd and even $N\rightarrow \infty$, but the limit $\mu \rightarrow 0$ is well defined. The energy dissipation rate is analytically calculated as a function of $\mu$ using methods of number theory. It grows as $\nu/\mu^2$ in the continuum limit $\mu \rightarrow 0$, leading to anomalous dissipation at $\mu \propto \sqrt{\nu} \to 0$. The same method is used to compute all the local vorticity distribution, which has no continuum limit but is renormalizable in the sense that infinities can be absorbed into the redefinition of the parameters. The small perturbation of the fixed manifold satisfies the linear equation we solved in a general form. This perturbation decays as $t^{-\lambda}$, where the anomalous dimensions $\lambda$ satisfy the spectral equation \eqref{spectralEq}. The spectrum becomes a continuum in the statistical limit $N \to \infty$, presumably leading to multifractal phenomena.

Energy consumption modelling is of critical importance for improving energy efficiency in the drive towards sustainable manufacturing. Modern manufacturing processes and machine tools are very complex as they involve interplay of several advanced technologies. Therefore, energy modelling of modern machine tools is a complex multi-disciplinary task. This paper proposes a systematic and practical framework to choose the appropriate modelling approach for a given industrial problem. The framework was validated by applying it to diverse industrial scenarios.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Performing optimisation and scale-up studies of crystallisation systems requires accurate and computationally efficient mathematical models. The assumption of the ideal mixing conditions in batch reactors typically produce inaccurate results while the computational expense of CFD models is still prohibitively high. Therefore, in this work, a new intermediary approach is proposed that takes into account the non-ideal mixing conditions in the reactor and requires less computational resources than full CFD simulations. Starting with the Danckwerts concept of the intensity of segregation, an analogy between its application to chemical reactions and the kinetics of the crystallisation phenomena (such as nucleation and growth) has been made. As a result, the modified kinetics expressions have been derived which incorporate the effect of non-idealities present in stirred reactors. This way, based on the experimental measurements of the mixing time using the Laser Induced Fluorescence (LIF) technique, computationally more efficient mathematical models can be developed in two ways: (1) the accurate semi-empirical correlations are available for standard mixing configurations with the most often used types of impellers, (2) CFD simulations can be utilised for estimation of the mixing time; in this case it is necessary to simulate only the mixing process. The benefits offered by the LIF experimental technique have been demonstrated and some frequent problems in its application analysed. The mixing time results for configurations with and without baffles for three types of impellers and four different rotational speeds have been presented. The false shorter mixing times in the non-baffled configurations have been observed and this phenomena explained by the existence of two segregated zones in the reactor and confirmed by additional experiments. The precise measurements in these cases have been shown as difficult using the LIF technique, particularly for higher rpms. The experimental data has been compared to the preliminary simulation results obtained from the Smoothed Particle Hydrodynamics method and the standard k-ε turbulence model with the modest success. The shortcomings of the SPH model have been recognized and the directions for the future work discussed.

Atmospheric turbulence characteristics are essential in determining the quality of astronomical images and implementing adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol observatory (43.27472 ° N 42.50083 ° E, 3127 m a.s.l.) using the Era-5 re-analysis and scintillation measurements are investigated. For the closest reanalysis grid node to the observatory, vertical profiles of the structural constant of the air refractive index turbulent fluctuations Cn2 were obtained. The calculated Cn2(z) vertical profiles are compared with the vertical distribution of turbulence intensity obtained from tomographic measurements with Shack-Hartmann sensor. The atmospheric coherence length at the location of Terskol Peak was estimated. Using combination of atmospheric models and scheme paramaterization of turbulence, Cn2(z) profiles at Mt. Kurapdag were obtained. The values of atmospheric coherence length at the Peak Terskol are compared with estimated values of this length at the ten astronomical sites including Ali, Lenghu and Daocheng.

We develop a quantitative microscopic theory of decaying Turbulence by studying the dimensional reduction of the Navier-Stokes loop equation for the velocity circulation. We have found an infinite dimensional manifold of solutions of the Navier-Stokes loop equation\cite{M93, M23PR} for the Wilson loop in decaying Turbulence in arbitrary dimension $d >2$. This family of solutions corresponds to a fractal curve in complex space $\mathbb C^d$, described by an algebraic equation between consecutive positions plus a nonlinear periodicity condition. We derive the constrained SDE for the evolution of the fractal curve at a fixed moment of physical time as a function of an auxiliary stochastic time. We expect this stochastic process to cover our fixed manifold of the solutions of the decaying Turbulence. The energy density of the fluid decays as $\mathcal E_0/t$, where $\mathcal E_0$ is an initial dissipation rate. Presumably, we have found a new phase of extreme Turbulence yet to be observed in real or numerical experiments.

While laminar flow heat transfer and mixing in microfluidic geometries has been investigated experimentally, as has the effect of geometry-induced turbulence in microfluidic flow (it is well documented that turbulence increases convective heat transfer in macrofluidic flow), little literature exists investigating the effect of electrokinetically-induced turbulence on heat transfer at the micro scale. Using recently observed experimental data, this work employed computational fluid dynamics coupled with electromagnetic simulations to determine if electrokinetically-forced, low-Reynolds number turbulence could be observed in a rectangular microchannel with using Newtonian fluids. Analysis of the results was done via comparison to the experimental criteria defined for turbulent flow. This work shows that, even with a simplified simulation setup, computational fluid dynamics (CFD) software can produce results comparable to experimental observations of low-Reynolds turbulence in microchannels using Newtonian fluids. In addition to comparing simulated velocities and turbulent energies to experimental data this work also presents initial data on the effects of electrokinetic forcing on microfluidic flow based on entropy generation rates.

Schistosomiasis, acquired by skin-penetrating cercariae of dioecious digenean schistosomes during freshwater contact, afflicts nearly 260 and 440 million people with active infections and residual morbidity, respectively; about 10 million women at reproductive age contract schistosomiasis during gestation every year. Acute schistosomiasis is characterized by pre-patent pro-inflammatory CD4+ T-helper 1 or CD4+ Th1/T-helper 17 reactivity against immature schistosomulae. Chronic schistosomiasis is dominated by post-patent anti-inflammatory CD4+ T-helper 2 reactivity against ova epitopes; flukes co-exist in immunocompetent definitive hosts as they are capable of evading their defense mechanisms. Preventive measures should be complemented by vaccination, inducing long-term protection against transmission, infection, and disease recurrence, given the latest advancements in schistosomal vaccines. Transmission models incorporating vaccination available in PubMed, Embase and Web of Science up to December 31, 2023 are presented. Besides conceptual model differences, predictions meant to guide decision- and policymaking reveal continued worm harboring facilitating transmission besides residual infections, and increased susceptibility to re-infection and rebound morbidity, both shifted to later life stages following the intervention. Consequently, a vaccination schedule is pivotal considering the optimal age for initial immunization, i.e., pre-schoolchildren or schoolchildren, in a cohort-based or population-based manner while incorporating potential non-adherers promoting ongoing transmission; longevity over magnitude of vaccine protection to antigenic schistosomal moieties is crucial including accounting for existing pre-acquired immunity from natural exposure, in utero priming besides herd immunity, and induced by chemotherapy. Combining as one multi-component approach long-term effects of vaccination with short-term effects of chemotherapy as regular repeated vaccine-linked therapy in contrast to a single-component intervention seems most promising for achieving WHO’s endpoints of transmission elimination and morbidity control.

We are witnessing a progressive divestment of some institutions with strong traditions and skills in physical modelling and their consequent impoverishment, to the detriment of numerical modelling. For many reasons, the economic imperatives and the exponential growth of computational means and numerical methods should certainly not be excluded. In this work, we aimed to highlight the new requirements of the recent sophisticated developments in physical modelling, precisely due to the new needs imposed on them by mathematical and numerical modelling and the growing risks in civil construction works. In this context, reflections are reported, justified by scientific and real-world examples, on the need for maintenance and reinforcement of investments in physical modelling, both to support the scientific community and to design buildings of significant economic, social and environmental impact.

The electric vehicle (EV) industry challenges battery joining technologies by requiring higher energy density both by mass and volume. Improving the energy density via new battery chemistry would be the holy grail but is seriously hindered and progresses slowly. In the meantime, alternative ways, such as implementing more efficient cell packaging by minimizing the electrical resistance of joints are of primary focus. In this paper we discuss the challenges associated with the electrical characterisation of laser soldered joints in general, and the minimization of their resistive losses, in particular. In order to assess the impact of the joint resistance on the overall resistance of the sample, the alteration in resistance was monitored as a function of voltage probe distance and modelled by finite element simulation. The experimental measurements showed two different regimes: one far from of the joint area and another in its vicinity and within the joints cross section. The presented results confirm the importance of the thickness of the filler material, the effective and the total soldered area, and the area and the position of the voids within the total soldered area in determining the electrical resistance of joints.

We consider the developed turbulence of capillary waves on shallow water. Analytic theory shows that an isotropic cascade spectrum is unstable with the respect to small angular perturbations, in particular, to spontaneous breakdown of the reflection symmetry and generation of nonzero momentum. By computer modeling we show that indeed a random pumping, generating on average zero momentum, produces turbulence with a nonzero total momentum. A strongly anisotropic large-scale pumping produces turbulence whose degree of anisotropy decreases along a cascade. It tends to saturation in the inertial interval and then further decreases in the dissipation interval. Surprisingly, neither the direction of the total momentum nor the direction of the compensated spectrum anisotropy is locked by our square box preferred directions (side or diagonal) but fluctuate.

Crisis environments, which are becoming systemic, pose significant challenges to smart government strategies. The paper aims to contribute to academic debate by proposing an analytical framework for examining the institutional capacities of smart government systems in addressing local crises. The paper focuses on the recent approach of robust governance and highlights a set of variables that promote effective smart government: contingency planning capacity, analytical capacity, organizational management capacity, and collaborative capacity. The study presents an analytical model for evaluating the robustness and effectiveness of local smart government systems in crises. One of the significant findings of this study has been the identification of critical indicators that inform institutional capacities of smart government systems. By analyzing these indicators, the proposed analytical framework provides a comprehensive approach to assess the preparedness of smart government systems in dealing with crises. Moreover, it can be used to benchmark the performance of local smart government systems in similar contexts and identify best practices for improving crisis management.

The structure of turbulent flow over non-flat surfaces is a topic of major interest in practical applications in both engineering and geophysical settings. A lot of work has been done in the fully rough regime at high Reynolds numbers where the effect on the outer layer turbulence structure and the resulting friction drag is well documented. It turns out that surface topology plays a significant role on the flow drag especially in the transitional roughness regime and therefore, hard to characterize. Survey of literature shows that roughness function depends on the interaction of roughness height, flow Reynolds number and topology shape. In addition, if the surface topology contains large enough scales then it can impact the outer layer dynamics and in turn modulate the total frictional force. Therefore, it is important to understand the mechanisms underlying drag increase from systematically varied surface undulations in order to better interpret quantifications based on mean statistics such as roughness function. In this study, we explore the mechanisms that modulate the turbulence structure over a two-dimensional (2D) sinusoidal wavy surface with a fixed amplitude, but varying slope. To accomplish this, we model the turbulent flow between two infinitely wide 2D wavy plates at a friction Reynolds number, $Re_{\tau}=180$. We pursue two different but related flavors of analysis. The first one adopts a roughness characterization flavor of such wavy surfaces. The second one focuses on understanding the non-equilibrium near surface turbulence structure and their impact on roughness characterization. Analysis of the different statistical quantifications show strong dependence on wave slope for the roughness function indicating drag increase due to enhanced turbulent stresses resulting from increased production of vertical velocity variance from the surface undulations.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

This paper introduces a three-layer system, proposing a comprehensive model of granular mixture transport over a mobile sloped bed in a steady flow. This system, consisting of the bottom, contact and upper zones, provides complete, continuous sediment velocity and concentration vertical profiles. The model considers gravity’s effect on sediment transport in the bottom (dense) layer, a crucial factor often overlooked in previous studies. The induced movement in the mobile sublayer of the dense layer causes an increase in velocity at the upper boundary of this layer, significantly affecting the vertical distributions of velocity and concentration above this layer. We described the interactions between fractions and an intensive sediment mixing and sorting process in the contact layer. The proposed shear variation, with an increase in its maximum value at the bed, adds to the novelty of our approach. The data sets used for the model’s validation cover various conditions, including slopes, grain diameters, densities and grain mobility conditions, from incipient motion to a fully mobilised bed. This extensive validation process instils confidence in the theoretical description and its applicability to real-world scenarios.

Reservoirs created by damming rivers significantly modify the environment and problems may arise: the storage of nutrients and organic matter in reservoirs often leads to decreased water quality due to eutrophication. Reservoirs built on lowland rivers are exposed to significant nutrient loads, often leading to eutrophication. In this article, the Sulejów Reservoir, in Central Poland, was the subject of the research, which focused on the use of Sentinel-2 remote sensing data to detect phytoplankton blooms, and on understanding the pattern of wind-driven surface currents using the CCHE2D model (a depth-averaged hydrodynamic model). The calculation of the total phosphorus load has shown that the reservoir mainly acts as nutrient storage. Still, during the low flow and intensive phytoplankton bloom, it can be a source of nutrients. The distribution of phytoplankton bloom on the Sulejów Reservoir was documented using eight Sentinel-2 satellite images from the vegetation season of 2020 and the Normalized Difference Chlorophyll Index (NDCl). Coupling remote sensing data and numerical modelling helps to interpret the hydrodynamic model results and understand nutrients and sediment dynamics within the reservoir. Hydrodynamic modelling combined with remote sensing data can be a valuable tool for selecting appropriate locations for water quality measurements at monitoring stations.

The partial discharge (PD) event in high voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well-established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of acoustic pulse and the acoustic reflections pose fundamental challenges to the Acoustic Emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse's shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system.

This is the second paper in a cycle investigating the exact solution of loop equations in decaying turbulence. We perform numerical simulations of the Euler ensemble, suggested in the previous work, as a solution to the loop equations. We designed novel algorithms for simulation, which take a small amount of computer RAM so that only the CPU time grows linearly with the size of the system and its statistics. This algorithm allows us to simulate systems with billions of discontinuity points on our fractal curve, dual to the decaying turbulence. For the vorticity correlation function, we obtain \textbf{quantum fractal laws} with regime changes between universal discrete values of indexes. The traditional description of turbulence with fractal or multifractal scaling laws does not apply here. The measured conditional probabilities of fluctuating variables are smooth functions of the logarithm of scale, with statistical errors being negligible at our large number of random samples $T = 167,772,160$ with $N = 200,000,000$ points on a fractal curve. The quantum jumps arise from the analytical distribution of scaling variable $X = \frac{\cot^2(\pi p/q)}{q^2}$ where $\frac{p}{q}$ is a random fraction. This distribution is related to the totient summatory function and is a discontinuous function of $X$. In particular, the energy spectrum is a devil's staircase with tilted uneven steps with slopes between $-1$ and $-2.03$. The logarithmic derivative of energy decay $n(t)$ as a function of time is jumping down the stairs of universal levels between $1.50$ and $1.53$. The quantitative verification of this quantization would require more precise experimental data.

This article examines the wavelet transforms and the machine learning methods in accelerating large eddy simulations of turbulent flows. An overarching challenge of large eddy simulations is to accurately represent the cascade of kinetic energy across the cut-off length scale. Taylor hypothesis suggests that the energy cascade occurs through the process of vortex stretching. However, Richardson hypothesis assumes the self-amplification of the strain field and a hierarchical break down of large eddies lead the energy cascade. The large eddy simulations typically employs the self-amplification of the strain in formulating subgrid models. However, several studies also proposed subgrid models based on vortex stretching. The wavelet-based coherent vortex simulation of turbulence directly accounts for vortex stretching in overall forward scatter of energy, while allowing local backscatter. The wavelet-based large eddy simulation adapts the grid to capture the creation of small-scale eddies, while adopting subgrid models based on the self-amplification of strain. The advancement of artificial intelligence in turbulence modelling is currently evolving around accelerating the numerical simulations of turbulent flow. However, there is a clear connection between the application of wavelet transfroms and neural networks for directly solving the Navier-Stokes equation, indicating some potential benefits of wavelet methods over the neural networks.

Industrial fires at facilities including waste management sites, warehouses, factories, chemical works and fuel storage depots are relatively frequent occurrences. Often, these fires occur adjacent to urban communities and result in ground-level airborne pollutant concentrations that are well above guideline values. Land, water, livestock and crops may also be contaminated by the emissions and by firefighting activities. Moreover, impacted communities tend to have a higher proportion of minority ethnic populations, individuals with underlying health vulnerabilities, as well as those of lower socio-economic status. Nevertheless, this is an aspect of air quality that is under-researched, and so this review aims to highlight the public health hazards associated with industrial fires and the need for an effective, coordinated, public health response. We also review the range of monitoring techniques that have been utilised in such fires and highlight the role of dispersion modelling in predicting plume trajectories and in estimating population exposure. We recommend establishing 1-h guideline values for particulate matter to facilitate timely public health interventions and we highlight the need to review regulatory and technical controls for sites prone to fires, particularly in the waste sector.

Elderly individuals frequently perceive companion robots devoid of warmth, resulting in a reluctance to accept these robots' services. This perception has become a significant barrier to the introduction of companion robots into households. To address this issue, this study employs KE methods to translate the elderly's need for warmth perception from companion robots into elements of modelling design. Initially, 20 pairs of affective words and 22 sample images of companion robots were chosen. The "rational-emotional" and "dim-bright" dimensions were identified as positively influencing the elderly's warmth perception of companion robots through SD method analysis. Subsequently, a hierarchical analysis table was established, with warmth perception as the root layer, modelling style and color matching as the basic layer, "rational-emotional" and "dim-bright" as the expansion layer, and the eight characteristic elements of companion robots as the morphological layer. The link between affective imagery and design elements was then established from the sample robots with higher warmth perception scores. Based on the correlation between the two, various design schemes were formed through random combinations. Finally, the design schemes were evaluated using the warmth perception scale, and the warmth perception scores of each sample model were obtained. This research provides design references for designing companion robots for the elderly with a "sense of warmth".

Turbulent dissipation is a central issue in the star and galaxy formation process. Its fundamental property of space-time intermittency, well characterised in incompressible laboratory experiments, remains elusive in cosmic turbulence. Progress requires the combination of state-of-the-art modelling, numerical simulations and observations. The power of such a combination is illustrated here where the statistical method intended to locate extrema of velocity shears in a turbulent field is applied to numerical simulations of compressible magneto-hydrodynamical (MHD) turbulence dedicated to dissipation scales and to a nearby turbulent diffuse molecular cloud. We demonstrate that short-spacing increments of observables can detect strongly dissipative structures. In our simulations, we compute structure functions of various synthetic observables and show that they verify Extended Self-Similarity. This allows to compute their intermittency exponents and we show how they could help constraining some properties of the underlying three-dimensional turbulence. In observations of a turbulent cloud close to the Sun in our Galaxy, a remarkable coherent structure of velocity shear extremum is disclosed . At the location of the largest velocity shear, this coherent structure is found to foster 10 × more carbon monoxide molecules than standard diffuse molecular gas, an enrichment supported by models of non-equilibrium warm chemistry triggered by turbulent dissipation. The power of the combination between modelling and observations is also illustrated by observations of the CH+ cation that provide unique quantitative informations on the kinetic energy trail in the massive, multi-phase and turbulent circum-galactic medium of a galaxy group at redshift z = 2.8.

This paper seeks to model the crack propagation in of AAs (aluminum-alloys) 2524-T3 and 7050-T7452 subjected to fatigue loading at low temperature. Fatigue crack growth tests were performed on AAs 2524-T3 and 7050-T7452 subjected to constant-amplitude and actual random-spectra loading histories at room temperature of about 25 °C and at cryogenic temperature of -70 °C, respectively, to determine their crack growth properties and residual lives. The interaction mechanisms between cryogenic temperature and the fatigue load were deduced on the basis of the results of fractographic analysis. Temperature-dependent residual lives under actual random-spectrum load history were modeled based on a modified accumulation damage rule by accounting for the load interaction. Good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed method.

The nonlocality (superdiffusion) of turbulence is expressed in the empiric Richardson t3 scaling law for the mean square of the mutual separation of a pair of particles in a fluid or gaseous medium. The development of the theory of nonlocality of various processes in physics and other sciences based on the concept of Lévy flights resulted in the idea of Shlesinger and colleagues about the possibility of describing the nonlocality of turbulence using a linear integro-differential equation with a slowly falling kernel. The close approach developed by us made it possible to establish the closeness of the superdiffusion parameter of plasma density fluctuations moving across a strong magnetic field in a tokamak to the Richardson law. In this paper, we show the possibility of a universal description of the characteristics of nonlocality of transfer in a stochastic medium (including turbulence of gases and fluids) using the Biberman-Holstein approach to the transfer of excitation of a medium by photons, generalized to take into account the finiteness of the velocity of excitation carriers. This approach enables us to propose a scaling that generalizes Richardson's t3 scaling law to the combined regime of Lévy flights and Lévy walks.

Tropical cyclones have long been known to be powered by turbulent enthalpy fluxes from the ocean surface and retarded by turbulent momentum fluxes into the surface. Here were review evidence that the development and structure of these storms are also partially controlled by turbulence in the outflow near the storm top. Finally, we present new research that shows that tropical cyclone-like, low-aspect-ratio vortices are most likely in systems in which the bottom heat flux is controlled by mechanical turbulence and the top boundary is insulating.

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to main regularities in the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov-Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

Species distribution models (SDMs) are usually used to predict current species’ geographic distributions and to forecast the impact of climate change, with different aims, such as conservation, and biodiversity management. SDMs use has been increasing in the last decades, however, they are vulnerable to parametrization and data quality input. Thus, inappropriate input can lead to potential unreliability in results. In this context, the most used data and methodologies in SDM, and putative deviations from the consensual best practices, were identified, by analysing recent literature (2018 to 2022). Results show that the parameters presented more consistently are the chosen algorithm (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. Many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers also fail to provide information about the target species: only a fraction of the species' range is considered, or no justification for including specific variables in the model is provided. These options can decrease reliability in predictions under future scenarios since data provided to the model is inaccurate from the start or there is insufficient information for outputs discussion.

Problem solving is one of the most important components of the human cognition that affects for ages the progress of the human society. Mathematical modelling is a special type of problem solving concerning problems related to science or everyday life situations. The present study is a review of the author’s earlier works on problem solving and mathematical modelling from the scope of Education. Its real goal is that it presents in a systematic way and in a few pages only the results of many years research on the subject. This helps the reader to get a comprehensive idea about a very important topic belonging to the core of Mathematics Education, which is very useful to those wanting to study deeper the subject and get directions for further research in the area.

An aneurysm is a vascular malformation, it can be classified according to its location (cerebral, aortic); and according its shape (saccular, fusiform, and mycotic). The study of blood flow interaction with the aneurysms has gained attention from physicists an engineers in recent years. Shear stresses, oscillatory shear index (OSI), gradient oscillatory number (GON), and residence time had been used as potential variables to describe the hemodynamics as well as the origin and evolution of aneurysm. However, the causes of aneurysms and the hemodynamics conditions that promote the growth of these structures are still under debate. The present work presents numerical simulation on three aneurysms: 2 aortic and 1 cerebral. The results shows that the blood rheology is not relevant for aortic aneurysms, however, for cerebral aneurysm could play a relevant role in the hemodynamics. The turbulence models evaluated show equivalent results in both cases. Lastly, a simulation considering the fluid-structure interaction (FSI) shows that this phenomenon is the dominant factor for aneurysms simulation.

The e-commerce sector is in a constant state of growth and evolution, particularly within its subdomain of online food delivery. As such, ensuring customer satisfaction is critical for companies working in this field. One way to achieve this is by providing accurate delivery time estimation. While companies can track couriers via GPS, they often lack real-time data on traffic and road conditions, complicating delivery time predictions. To address this, a range of statistical and machine learning techniques are being employed, including neural networks and specialized expert systems, with different degree of success. One issue with neural networks and machine learning models is their heavy dependence on vast, high quality data. To mitigate this issue we propose two Bayesian generalized linear models to predict time of the delivery. Utilizing a linear combination of predictor variables, we generate practical range of outputs with Hamiltonian Monte Carlo sampling method. These models offer a balance of generality and adaptability, allowing for tuning with expert knowledge. They were compared with PSIS-LOO and WAIC criteria. Results show that both models accurately estimated delivery times from the dataset, while maintaining numerical stability. Model with more predictor variables proved to be more accurate.

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from the weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make FSO links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

The devastating effects of COVID-19 permeates virtually all aspects of life across geographic boundaries. As the lockdown is eased, and reinstated, across many parts of the world, attention is now shifting towards how to navigate the ‘now normal’ rather than the ‘next normal,’ of doing business with a view to achieving sustainable development. This study highlights some of the challenges and opportunities for micro, small, and medium enterprises (MSMEs) in times of crisis such as COVID-19. Conceptual by orientation, the study provides a broad overview of trends in crises times, drawing upon a range of qualitative accounts. The overarching message is the current direction of travel for MSMEs in sustaining their bottom-line, not after the crisis, but at this particular moment in time. The study highlights some implications for ‘pivoting,’ in order to cushion the impact of the pandemic on efforts to capacitate MSMEs in leveraging value co-creation with the consumers. The implications of the here and now are discussed.

The problem of simulating wakes in a stratified oceanic environment with active background turbulence is considered. Anisotropic RANS turbulence models are tested against laboratory and eddy-resolving models of the problem. An important aspect of our work is to acknowledge that the environment is not quiescent; therefore, additional sources are included in the models to provide a non-zero background turbulence. The RANS models are found to reproduce some key features from the eddy-resolving and laboratory descriptions of the problem. Tests using the freestream sources show the intuitive result that background turbulence causes more rapid wake growth and decay.

Tidal stream energy is a low carbon energy source. Tidal stream turbines operate in a turbulent environment, and the effect of the structure between the turbine and seabed on this environment is not fully understood. An experimental study using 1:72 scale models based on a commercial turbine design was carried out to study the support structure influence on turbulence intensity around turbine blades. The study was conducted using the wave-current tank at LABIMA, University of Florence. A realistic flow environment (ambient turbulent intensity = 11%) was established. Turbulence intensity was measured upstream and downstream of a turbine mounted on two different support structures (one resembling a commercial design, the other the same with an additional vertical element), in order to quantify any variation in turbulence and performance between the support structures. Turbine drive power was used to calculate power generation. Acoustic Doppler Velocimetry was used to record and calculate upstream and downstream turbulence intensity. In otherwise identical conditions, performance variation of only 4% was observed between two support structures. Turbulent intensity at 1, 3 and 5 blade diameters, both upstream and downstream, showed variation up to 21% between the two cases. The additional turbulent structures generated by the additional element of the second support structure appears to cause this effect, and the upstream propagation of turbulent intensity is believed to be permitted by surface waves. This result is significant for the prediction of turbine array performance.

In this study we discuss critical issues in modelling the structure and function of lymph nodes (LNs), with emphasis on how LN physiology is related to its multi-scale structural organization. In addition to macroscopic domains such as B-cell follicles and the T cell zone, there are vascular networks which play a key role in the delivery of information to the inner parts of the LN, i.e., the conduit and blood microvascular networks. We propose object-oriented computational algorithms to model the 3D geometry of the fibroblastic reticular cell (FRC) network and the microvasculature. Assuming that a conduit cylinder is densely packed with collagen fibers, the computational flow study predicted that the diffusion should be a dominating process in mass transport than convective flow. The geometry models are used to analyze the lymph flow properties through the conduit network in unperturbed- and damaged states of the LN. The analysis predicts that elimination of up to 60–90 % of edges is required to stop the lymph flux. This result suggests a high degree of functional robustness of the network.

Traumatic Brain Injury represents a significant public health concern due to its role in traumatic death and disability, often caused by head impacts or rapid accelerations. Despite advancements in Finite Element Head Models, existing models primarily represent adult males, neglecting anatomical and physiological differences between genders and age groups. Additionally, the mechanistic roles of specific intracranial tissues, such as the dura mater, have conventionally been overlooked. To better understand the mechanical behavior of the brain and its injured regions in the context of TBI, including the dura mater, this study proposes the development of a Female Finite Element Head Model. This model, representative of a middle-aged female subject, was developed using medical image-derived geometry and finite element modeling techniques. The validation results demonstrate a similarity between the numerical displacement curves and those obtained experimentally. In the model with dura mater, the numerical results show analogous behavior to the experimental results, despite minimal variations in the amplitude of the curves. These advancements highlight the significance of including the dura mater in biomechanical brain modeling. Incorporating the dura mater in Finite Element Head Models produces more realistic results than models without it. This emphasizes the importance of enhancing biomechanical models to better represent anatomical complexity.

The second article in the series presents the application of the Smoothed Particle Hydrodynamics (SPH) method to modelling of batch crystallisation in stirred tanks. A methodology to integrate the population balance equations (PBE) in parallel and independently from the Navier-Stokes equations is demonstrated. The benefits of the proposed methodology in terms of computational requirements, accuracy and availability of the crystal size distribution are discussed. The specific formulation of the SPH equations where the resulting system of ordinary differential equations is solved using the weighted contributions rather than numerically by solving a linear system of equations allows for massive parallelisation and a very loose coupling of the population balance and the fluid dynamics. It has been demonstrated, that the population balance equations can be solved on a Shared Memory Architecture (SMA) system using the OpenMP interface while the fluid dynamics equations being computed independently on a General Purpose Graphics Processing Unit (GPGPU) using the NVidia CUDA technology. This way, a significant portion of the computational overhead due to the large number of additional transport equations resulting from the discretisation of the population balance was removed: the SPH simulation coupled with 200 population balance equations was only 40% slower compared to SPH-only simulation. Two methods for the solution of population balance equations that preserve full crystal size distribution were implemented: discretised population balance (DPB) and method of characteristics (MOCH). The DPB equations are solved using the high-resolution finite-volume method with flux limiter and the effect of a large number of different flux limiters have been investigated. Both methods were validated using the case studies from the literature where an analytical solution can be derived. The developed models were applied to a numerical solution of coupled computational fluid dynamics and population balance equations to model a batch crystallization process. The effect of the hydrodynamics on the local temperature/supersaturation and the resulting crystal size distribution was captured and compared to the ideal mixing case. The simulation results from the DPB and MOCH methods were compared in terms of computational requirements and accuracy and MOCH selected as computationally more efficient and accurate.

This study presents a detailed computational investigation aimed at identifying and evaluating potential small organic molecular scaffolds as inhibitors targeting the Pks13 protein, a critical enzyme in tuberculosis (TB) drug discovery. Our approach integrates multiple computational techniques, including quantitative structure-activity relationship (QSAR) modelling, compound library generation, molecular docking, biological activity prediction, re-evaluation of screened compounds, molecular dynamics (MD) analysis, and binding free energy estimation using the MM-GBSA approach. We developed a robust QSAR model using multiple linear regression (MLR) techniques, specifically tailored to predict the minimum inhibitory concentration (MIC) values of potential anti-TB agents. This model demonstrated excellent predictive performance, validated through rigorous internal and external validation procedures. Through molecular docking simulations, we identified five promising inhibitors Ethyl (R,Z)-2-(2-((2-chlorobenzyl) oxy)benzylidene)-5-(4-chlorophenyl)-7-methyl-3-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyrimidine-6-carboxylate (PKD1), (7R,9aR)-7-(2-hydroxyphenyl)-4-(2-methoxyphenyl)-3-methyl-7,8,9,9a-tetrahydroisoxazolo[5,4-b]quinolin-5(6H)-one (PKD2), (7-(4-methoxy phenyl)-2-methyl-3-phenylpyrazolo[1,5-a]pyrimidin-5-yl)(4-(3-(trifluoromethyl)phenyl) piperazin-1-yl)methanone (PKD3), (8S,10S)-10-(((2R,4S,5S,6S)-4-amino-5-hydroxy-6-methyl tetrahydro-2H-pyran-2-yl)oxy)-6,8,11-trihydroxy-8-(2-hydroxyacetyl)-1-methoxy-7,8,9,10-tetra-hydrotetracene-5,12-dione (PKD4), 3-(3,4-dimethylphenyl)-5-((2-(4-fluorophenyl)-2-oxoethyl) thio)-6-phenyl-2-thioxo-2,3,5,7a-tetrahydrothiazolo[4,5-d]pyrimidin-7(6H)-one (PKD5)with high binding affinities, prioritizing them based on their binding energies. These selected compounds were further assessed for their biological activity predictions, guiding the selection of candidates for experimental validation. Subsequent re-evaluation using absolute binding free energy estimation and MD simulations provided insights into the stability and dynamics of the protein-ligand complexes over time. Our findings highlighted the potential of identified compounds as stable binders within the Pks13-TE domain binding pocket, underscoring their viability as drug candidates. Additionally, binding free energy analysis using MM-GBSA reaffirmed the strong affinity of the proposed compounds towards Pks13. Our integrated computational approach offers a systematic and detailed framework for the identification and characterization of potential Pks13 inhibitors with specific relevance to TB drug discovery.

In this paper, for the Reynolds-averaged Navier-Stokes equations, a self-closed turbulence model without any adjustable parameter is formulated and a simple model is proposed. The validation rule for self-closed turbulence model is rigorously derived from the Reynolds-averaged Navier-Stokes equation. The rule is not effected by turbulence modelling on the Reynolds stresses. As an application, two dimensional turbulent boundary layers, and plate turbulent boundary layers have been formulated.

A rigid protruding surface, agitator and vortex generator often dissipate more energy than to produce a satisfied turbulence production. In contrast, a passively-oscillating flexible protruding surface has the ability in generating greater turbulence level. In the current study, a circular finite cylinder (cantilever) was used as the geometry of the rigid and protruding surface. They were varied through the aspect ratio (AR) and materials. Also, a local Reynolds number within the subcritical flow range (102<ReD<105) was considered. The results from the rigid protruding surface (finite cylinder) is served as a validation to the published results and benchmark to the improve-ment of the turbulence generated by the flexible protruding surface. It was mentioned in the pre-vious work that the increment of turbulence production by the flexible cylinder is speculated to be oscillation, this work investigates the validity of the speculation through manipulating the struc-tural stiffness of the flexible cylinder by employing different materials. The results have further demonstrated that the flexible cylinder is capable of generating a greater turbulence through the examination of turbulence intensity, turbulence production term and Reynolds stress. All the flex-ible cylinders that oscillate show increment in turbulence production but at a different percentage. The cylinders studied in this work ranged from the least structural stiffness (EVA), moderate (al-uminium) to the highest structural stiffness (carbon steel). Through studying the normalized am-plitude responses graph of the flexible cylinder, it is identified that the oscillating motion does in-deed contribute to increment. A further investigation into the results, it is found that the factor which governs the increment is the structural velocity instead of just oscillating motion alone.

Capabilities of hovering unmanned aerial vehicles (UAVs) in low-altitude sensing of atmospheric turbulence with high spatial resolution are studied experimentally. The experiment was carried out at the Basic Experimental Observatory of the V.E. Zuev Institute of Atmospheric Optics SB RAS. UAV findings are compared with objective data on the state of the atmosphere that were measured by acoustic anemometers installed at weather towers at altitudes of 4, 10, and 27 m. Profiles of atmospheric turbulence were recorded with three UAVs hovering at the same heights at a distance of 5 m from the anemometers. The behavior of the longitudinal and lateral wind velocity components is studied in the frequency band 0-10 Hz before and after one-minute smoothing, which reveals long turbulent wind gusts. The discrepancy between the UAV and anemometer data is analyzed. It is found that after smoothing, the discrepancy does not exceed 0.5 m/s in 95% cases. This value meets the requirements of the World Meteorological Organization for some applications in wind velocity measurements. The spectral and correlation analysis of the UAV and anemometer measurements is carried out. The profiles of the longitudinal and lateral scales of turbulence are examined based on the UAV data and are found to be in agreement with the anemometer data to a good accuracy.

Based on the theory of turbulence, equations are derived for estimations of turbulent fluctuations of the longitudinal and transverse components of the wind velocity during ideal hovering of a quadcopter in a turbulent atmosphere. We present the results of experiments which were carried out on the territory of the Geophysical Observatory of Institute of Monitoring of Climatic and Ecological Systems, Siberian Branch, Russian Academy of Sciences, located in Tomsk Akademgorodok on the territory with complex orography, in a parkland zone with buildings of research institutes and motorways. Time series of turbulent fluctuations of the longitudinal and transverse components of wind velocity fluctuations were received with the use of an automated weather station, and time series of estimates of these components, from data of a DJI Phantom 4 Pro quadcopter during hovering. According to the automated weather station data, anisotropy was observed in one experiment during measurements in the atmosphere, but this phenomenon was not observed in the other experiment: the fluctuation spectra of all components of wind speed fluctuations coincide. The spectra of fluctuations of the longitudinal and transverse wind velocity components based on the automated weather station data and UAV telemetry are presented. The fluctuation spectra of these components for the automated weather station data and quadcopter generally coincide. The behavior of the spectra coincides with the spectrum which corresponds to Kolmogorov–Obukhov “–5/3” law within the inertial range. The turbulent spectra of the wind velocity fluctuations obtained with the use of the automatic weather station and with the unmanned aerial vehicle differ in the high-frequency spectral region.

In the present work, a predictive modelling and optimization with the adaptive network based fuzzy inference system(ANFIS) modelling of mechanical properties of laser-coated NB/SiC/Ni welds was studied based on Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties was taken as the output of the ANFIS. The microstructure of welds were analysed using scanning electron microscopy with an energy dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi's design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified.

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time open science becomes increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a toolbox to construct comprehensive energy system models and has been published open source under a free license. With a collaborative development based on open processes the framework seeks for a maximum level of participation and transparency to facilitate open science principles in energy system modelling. Based on a generic graph based description of energy systems it is well suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems ranging from an urban to a transnational scale.

This paper presents a novel sensor for the detection and characterization of regions of air turbulence that would be of use to improve UAV stability in bad weather. It consists of a combined Schlieren imager and a Radar Acoustic Sounding System (RASS) to produce dual modality “images” of air movement within the measurement volume. The ultrasound modulated Schlieren imager consists of a strobed point light source, parabolic mirror, light-block and camera which are controlled by two laptops. It provides a fine scale projection of the acoustic pulse modulated air turbulence through the measurement volume. The narrow beam 40 kHz/ 17 GHz RASS produces spectra based on Bragg enhanced Doppler radar reflections from the acoustic pulse as it travels. Tests using artificially generated air vortices showed some disruption of the Schlieren image and of the RASS spectrogram. This should allow the higher resolution Schlieren images to identify the turbulence mechanisms that are disrupting the RASS spectra.

Optical communications are described and analyzed by shaped beams; we challenge the ‎effects of parameters that impact the profile of a Flat Top Gaussian (FTG) beam. When ‎the laser beam propagates throughout the atmosphere, it can be influenced by different ‎optical phenomena including scattering, absorption, and turbulence due to changes in the ‎scintillation index and the forms of intensity that are displayed in the source and receiver ‎planes. In this project, the FTG laser beam that propagates through a weak turbulent ‎region is numerically investigated using open-source software. This simulation will be ‎performed according to a mathematical model based on the split-step beam propagation ‎method. Intensity distributions at the source plane and the received average intensity in ‎atmospheric turbulence are calculated, and additional contour is in the transducer plane. ‎The scintillation index, structure constant, source size, and other parameters, are applied ‎in the Rytov method to quantify the weak turbulent model. Moreover, these parameters ‎are analyzed in near-field propagation. Also, the effects of the beam’s scintillation and ‎beam wander are determined. All results simulated are discussed and compared with the ‎TEM00 Gaussian beam. Finally, these results are compared to measurements in the ‎experimental part of the work.

We present a mixing length based algebraic turbulence model calibrated to pipe flow; the main purpose of the model is to capture the increasing turbulence production-to-dissipation ratio observed in connection with the high Reynolds number transition region. The model includes the mixing length description by Gersten and Herwig which takes the observed variation of the von Kármán number with Reynolds number into account. Pipe wall roughness effects are included in the model. Results are presented for area-averaged (integral) quantities which can be used both as a self-contained model and as initial inlet boundary conditions for computational fluid dynamics simulations.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Recognizing trust as a pivotal element for success within Human-Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified 'Inspector game' while we monitor their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human-robot interaction in collaborative environments.

A non-steady state mathematical model of amino acid (phenylalanine (Phe)) and mineral salt (NaCl) solution separation by neutralization dialysis (ND) carried out in a batch mode is proposed. The model takes into account the characteristics of membranes (thickness, ion-exchange capacity and conductivity) and solutions (concentration, composition), as well as the flow rate of the solution in the dialyzer compartments. As compared to previously developed models, the new one considers the local equilibrium of Phe protolysis reactions in solutions and membranes and, also, the transport of all the phenylalanine forms (zwitterionic, positively and negatively charged) through membranes. A series of experiments on ND demineralization of NaCl and Phe mixed solution was carried out. In order to minimize Phe losses, the solution pH in the desalination compartment was controlled by changing the concentrations of the solutions in the acid and alkali compartments of the ND cell. The validity of the model was verified by comparison of simulated and experimental time dependencies of solution electric conductivity and pH, as well as the concentration of Na+, Cl– ions and Phe species in the desalination compartment. Based on the simulation results, the role of Phe transport mechanisms in the losses of this amino acid during ND was discussed.

Nodes in wireless sensor networks (WSN) are characterized particularly by their limited power and memory capabilities. Limited memory is an important parameter as it defines the size of the operating system and the processing code. As established previously, energy and memory efficiency is the most important evaluation factors of WSNs as they are directly related to data loss and network lifetime. However, based on our simulation results, memory efficiency determines the selection or abandon of nodes by the botmaster for the propagation of bots in an IoT infrastructure. Consequently, the node’s memory efficiency determined the spread of bots in the network and provides defense actors with an insight of the botmaster behavior for mitigation of the attack. Conventional botnet propagation and mitigation models did not consider the impact of node’s memory efficiency in the IoT platform. To address this gap, we build IoT-SIEF, a novel propagation model with forensic capability that will analyze command and control propagation behavior based on the perspective of the node’s memory efficiency. IoT-SIEF model used to explore the dynamics of propagation using numerical simulation with more than 50% outperform other models in mitigating the number of secondary bots. Consequently, it can serve as a basis for assisting the planning, design, and defense of such networks from the investigator's point of view.

This paper focus on the modeling and time-domain analyzing method of piecewise-smooth fractional-order circuit systems. Firstly, an analytical framework is constructed based on a generalized piecewise-smooth two-port impedance network model, which takes the topological characteristics of Dickson type switched-capacitor converters (SCCs) as examples, and considers the fractional-order characteristics of class-2 X7R ceramic capacitors, which are widely used in SCCs. Secondly, the time-domain analyzing method for such a piecewise-smooth fractional-order circuit system is developed, numerical solutions are obtained, and the influence of the fractional-order capacitor on the power conversion efficiency of the system are revealed. Finally, in order to validate the analysis, both numerical simulation and experiments are provided in the work, the results of which confirm that, the proposed fractional-order analytical framework can capture the power loss mechanism of piecewise-smooth fractional-order circuit systems more effectively than the traditional integer-order based approach, thus benefiting the design of circuit systems.

Among the many Lysosomal Storage Disorders (LSDs) that would benefit from the establish-ment of novel cell models, either patient-derived or genetically engineered, is Mucopolysaccha-ridosis type II (MPS II). In fact, even though a specific therapeutic approach does exist for this disorder (Enzyme Replacement Therapy, ERT, with recombinant human IDS) and haematopoiet-ic stem cell transplantation (HSCT) may also hold promise whenever the disorder is diagnosed soon enough, both approaches have their own drawbacks. Here we present our results on the establishment and characterization of two MPS II pa-tient-derived stem cell line(s) from deciduous baby teeth. To the best of our knowledge, this is the first time a stem cell population is isolated from LSD patient samples obtained from the oral cavity. Taking into account our results on the molecular and biochemical characterization of those cells and our first insights on their subcellular MPS phenotype, we consider these cells will be a valuable tool for both pathophysiological assessments and drugs screenings. Ultimately, we believe patient-derived dental pulp stem cells (DPSC) may represent a feasible alternative to induced pluripotent stem cells (iPSC) in many labs with standard cell culture con-ditions and limited (human and economic) resources.

The year 2022 was characterized by several major events that impacted the business world in Indonesia and even globally after the COVID-19 pandemic devastated all business sectors in Indonesia and the world. The current business environment is experiencing accelerating turbulence characterized by increasing intensity of business competition, rapid changes in the market, and industrial connections climate. The industrial connection climate of an institution can be measured by the pattern of relationships that occur between workers and management. Industrial connections are fundamental to the sustainability of an organization and are understood as an important factor in influencing institutional performance. This research aims to look at the impact of the existence of industrial connections instruments, turbulence in the business environment on the industrial connections climate. Besides, how the industrial connections climate relates to firm performance. The research design is an inferential quantitative, using sampling and snowball sampling methods with total sample are 406. The data collected were analyzed using Structural Equation Model using LISREL version 8.70. This research used a survey to build an SEM model that tested the effect of organizational turbulence and industrial connections instruments on organizational climate directly, and tested mediation on organizational performance. All related indicators have outer loading more than 0.5 and T-value more than 1.96. Therefore, the conclusion is that all measurements are valid. Composite Reliability (CR) and VE for all indicators are >0.7 and >0.5 respectively. This research shows that industrial assosiation climate has a positive effect on business turbulence and industrial connections instruments. However, industrial connections instruments show a greater impact on the assosiation climate. This is because industrial connections instruments are the media used in the implementation of industrial connections in organizations. This research also confirms that industrial assosiation climate is positively related to organizational performance. The result of the indirect influence (IE) of 0.20 with t value is 8.47 means that the climate of Industrial connections is able to mediate the influence of Industrial connections Facilities on Organizational Performance.

To gain insight into the differences between onshore and offshore atmospheric turbulence, 2 pressure fluctuations were measured for offshore wind under different environmental conditions. 3 A durable piezo-electric sensor was used to sample turbulent pressure data at 50 kHz. Offshore 4 measurements were performed at 100 m height on Germany’s FINO3 offshore platform in the 5 German Bight together with additional meteorological data provided by Deutscher Wetterdienst 6 (DWD). The statistical evaluation revealed that the stability state in the atmospheric boundary has a 7 large impact on turbulent fluctuations. Therefore, we used higher statistical properties (described 8 by so-called shape factors) to the stability state. Data was classified to be either within the unstable, 9 neutral or stable stratification. We found that in case of stable stratification, the shape factor is 10 mostly close to zero, indicating that a thermally stable environment produces closer-to Gaussian 11 distributions. Non-Gaussian distributions were found in unstable and neutral boundary layer states 12 and an occurrence probability was estimated. Possible impact on laminar-turbulent transition on the 13 blade is discussed with application of so-called laminar aerofoils on wind turbine blades. Use of a 14 cut-off frequency to separate load and aerodynamic turbulence is proposed.

The process of modelling energy systems is accompanied by challenges inherently connected with mathematical modelling. However, due to modern realities in the 21st century, existing challenges are gaining in magnitude and are supplemented with new ones. Modellers are confronted with a rising complexity of energy systems and high uncertainties on different levels. In addition, interdisciplinary modelling is necessary for getting insight in mechanisms of an integrated world. At the same time models need to meet scientific standards as public acceptance becomes increasingly important. In this intricate environment model application as well as result communication and interpretation is also getting more difficult. In this paper we present the open energy modelling framework (oemof) as a novel approach for energy system modelling and derive its contribution to existing challenges. Therefore, based on literature review, we outline challenges for energy system modelling as well as existing and emerging approaches. Based on a description of the philosophy and elementary structural elements of oemof, a qualitative analysis of the framework with regard to the challenges is undertaken. Inherent features of oemof such as the open source, open data, non-proprietary and collaborative modelling approach are preconditions to meet modern realities of energy modelling. Additionally, a generic basis with an object-oriented implementation allows to tackle challenges related to complexity of highly integrated future energy systems and sets the foundation to address uncertainty in the future. Experiences from the collaborative modelling approach can enrich interdisciplinary modelling activities. Our analysis concludes that there are remaining challenges that can neither be tackled by a model nor a modelling framework. Among these are problems connected to result communication and interpretation.

Wheeled mobile robot dynamics and suitable controller design are challenging but rewarding fields of study. By understanding the dynamics of wheeled mobile robots, it could be possible to design suitable hybrid control schemes for wheeled mobile robots. Since hybrid control schemes involve combining individual control methods to create a more effective overall control strategy. This can be done in a variety of ways, such as using fuzzy linear quadratic Gaussian control. We were therefore inspired to create dynamic models and their controller designs for the wheeled mobile robot by examining the dynamics of the wheeled mobile robot. The novelty of the current paper is to hybridize the different control schemes for wheeled mobile robots in order to get better performance. Entire systems models were simulated in a MATLAB/SIMULINK environment. The results obtained for the settling time response by FLQG were 87.1% over LQG; this study compared the effectiveness of current and previous FLQG controllers in external disturbances and found peak amplitude improvements of 71.25%. Therefore, the proposed controller is suitable for use with the wheeled mobile robot.

Based on author's previous work [Sun, B. The Reynolds Navier-Stokes Turbulence Equations of Incompressible Flow Are Closed Rather Than Unclosed. Preprints 2018, 2018060461 (doi: 10.20944/preprints201806.0461.v1)], this paper proposed an explicit representation of velocity fluctuation and formulated the Reynolds stress tensor in terms of the mean velocity field. The proposed closed Reynolds Navier-Stokes turbulence formulations reveal that the mean vorticity is the key source of producing turbulence.

This paper shown that turbulence closure problem is not an issue at all. All mistakes in the literature regarding the numbers of unknown quantities in the Reynolds turbulence equations stem from the misunderstandings of physics of the Reynolds stress tensor, i.e., all literatures have stated that the symmetric Reynolds stress tensor has six unknowns; however, it actually has only three unknowns, i.e., the three components of fluctuation velocity. We shown the integral-differential equations of the Reynolds mean and fluctuation equations have exactly eight equations, which equal to the numbers of quantities in total, namely, three components of mean velocity, three components of fluctuation velocity, one mean pressure and one fluctuation pressure. That is why we claim in this paper, that the Reynolds Navier-Stokes turbulence equations of incompressible flow are closed rather than unclosed. This study may help to solve the puzzle that has eluded scientists and mathematicians for centuries.

The fuel cell particularly the Proton Exchange Membrane Fuel Cell (PEMFC) is a new energy technology that plays a critical role in achieving market viability. The PEMFC has high power density, zero CO₂ emissions, and high conversion efficiency. However, with uniform distribution of fuel in PEMFC, we can improve the performance and reduce the cost. The PEMFC will be more efficient and effective in terms of renewable energy as a byproduct only water. Metal foam is used as an alternative for the channels in PEMFC. The model of PEMFC was established by using the ANSYS Fluent 3D Model. Then we proposed four configurations of PEMFC. The outcome demonstrates that Case IV provided outstanding performance if it is compared with the other three cases. In terms of power density, Case IV provided a high power density that is double that of Case I. The porosity of metal foam was investigated. The 0.6 porosity value is found as the optimal value. The uniform distribution of the fuel can be achieved at this value which leads to the outstanding current density.

This study presents the results of a research project financed by the Lazio Regional Government. The research focused on defining an integrated model of recent alluvial deposits in the Tiber River. To achieve this objective, geological boreholes were made to monitor the aquifer and in situ and laboratory tests carried out. The data obtained was used to detail stratigraphic aspects and improve the comprehension of water circulation beneath the recent alluvial deposits of the Tiber River in the urban area of Rome, between the Ponte Milvio bridge and the Tiber Island. The stratigraphic intervals recognised in the boreholes were parameterised based on their litho-technical characteristics. The new data acquired, and integrated with existing data in the CNR IGAG database, made it possible to produce a three-dimensional model of the lithologies in the study area.The model of the subsoil, simplified for applied reasons, was described in hy-drostratigraphic terms: three different lithotypes were subjected to piezometric levels monitor-ing. Finally, the research generated a numerical hydrological level in a stationary regime. In general, this study demonstrates how a numerical hydrogeological model calibrated by piezo-metric monitoring data can support the construction of a geological model, discarding or con-firming certain hypotheses and suggesting other means of reconstructing sedimentary bodies.

The catastrophic failure of two dams in Libya in the night of 10th and 11th September 2023, resulted in a significant amount of water and debris destroying a large part of the city of Derna, which is located downstream of the two dams. The flood caused more than 6000 fatalities and the dis-placement of thousands of people from the city. The dams failed due to heavy rainfall caused by the Medicane Daniel, resulting in intense precipitation and the dams overspilling after reaching full capacity. This paper analyses the consequences of the dam breach by modelling the flow discharge and the flooding of the city of Derna. The analysis compares the computed quantities with the findings of the post-event satellite image analysis from UNOSAT and Copernicus, the field reports, and information gathered from the social media through AI detection. The results allows to better understand the dynamics of the dam breach, its initial parameters and an assessment of the accu-racy of the results.

Gene editing is now easy to make new disease models through in-vivo and in vitro tests. It can potentially be used to make animals with single-gene or multiple-gene changes. The mutant strains with changed germlines are no longer needed with in vivo gene editing, which uses the CRISPR-Cas9 system to target cells of interest in their normal tissues. Whereas, the AAVs and other viral vectors have made it possible to change cells selectively. Gene editing hade made it possible to use human induced pluripotent stem cells (iPSC) to model diseases that run in families. Researchers can compare and contrast the human genomes of many different ethnic and racial groups using this method. Scientists may be able to make a disease in a lab dish using iPSCs from a patient. Using CRISPR, iPSCs made from patient cells can be fixed if they have certain problems. This shows that gene therapy is possible and shows what happens when cells aren't working right. The fact that CRISPR-Cas9 can change the DNA by just one nucleotide has had a huge effect on biological studies. CRISPR is becoming more and more popular, which shows how useful, easy, and effective it is. With the broad use of CRISPR-based apps, the tool is now used for much more than just changing genes. This method can be used to screen the whole genome, control the translation of genes based on their sequence, and edit several genes at the same time. Scientists can now model diseases in different species and learn more about how genes work because of these advances. Genome-wide association studies and genome-editing tools like CRISPR are giving us a good look at the future of personalized medicine.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to the awareness about their biotechnological influences in a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications (both in vivo and in silico) have been developed in the context of isolated microbes. In vivo microbial consortia development, i.e. to reproduce the community life conditions in the wet-lab, is extremely difficult and expensive requiring of computational approaches to advance knowledge about microbial communities, mainly with descriptive modelling, and further with engineering modelling. In this review we provide a detailed compilation of available examples of engineered microbial communities as a launch pad for an exhaustive and historical revision of those computational methods devoted so far toward the better understanding, and rational engineering of natural and synthetic microbial communities.

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct tree branches from laser scans. We first extract an initial tree skeleton from the input tree point cloud, then simplify the skeleton through iteratively removing redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.

Selected by in vitro techniques (SELEX, cell-SELEX), aptamers are strands of DNA or RNA molecules able to bind a wide range of targets, from small molecules to live cells, and even tissues, with high affinity and specificity. Due to their efficient targeting ability, aptamers are extensively used in different fields of applications. For example, they ensure high performance as cancer-related markers or in recognizing cancer cells. Actually, they represent a promising way for early diagnosis (biosensors) and to deliver imaging agents and drugs, in both cancer imaging and therapy (therapeutic aptamers). Aptamer-based biosensors (aptasensors) have attracted particular attention over the last decades, so as the possibility of using aptamers in disease therapy in substitution of monoclonal antibodies. The paper briefly reviews the most recent literature on this topic, both concerning the advances in biomedical applications and in the development of electrical aptasensors. The investigation concerning the bioelectronics features of aptamers, to be implemented in the development of electrical nanobiosensors, is also reviewed. To this aim, some recent results of a theoretical/computational framework for modelling the electrical properties of biomolecules (Proteotronics) are reported.

Three-phase interactions (metal-slag-argon) in ladle stirring operations have strong effects on metal-slag mass transfer processes. Specifically, the thickness of the slag controls the fluid turbulence to an extent that once trespassing a critical thickness, increases of stirring strength have not further effects on the flow. To analyze these conditions, a physical model considering the three phases was built to study liquid turbulence in the proximities of the metal-slag interface. A velocity probe placed close to the interface permitted the continuous monitoring and statistical analyses of turbulence. The slag-eye opening was found to be strongly dependent on the stirring conditions, and the mixing times decreased with thin slag thicknesses. Slag entrainment was enhanced with thick slag layers, and high flow rates of the gas phase. A multiphase model was developed to simulate these results finding a good agreement between experimental and numerical results.

In the present study, field observation wind data from the time of the wind turbine blade damage accident on Shiratakiyama Wind Farm were analyzed in detail. In parallel, high-resolution LES turbulence simulations were performed in order to examine the model’s ability to numerically reproduce terrain-induced turbulence. The comparison of the observed and simulated time series (1 second average values) from a 10 minute period from the time of the accident led to the conclusion that the settings of the horizontal grid resolution and time increment are important to numerically reproduce the terrain-induced turbulence that caused the wind turbine blade damage accident on Shiratakiyama Wind Farm. A spectral analysis of the same set of observed and simulated data revealed that the simulated data reproduced the energy cascade of the actual terrain-induced turbulence well.

Animal models of diseases in particular, the mice, are considered to be the cornerstone for translational research in immunology. The aim of the present study is to model the geometry and analyze the network structure of murine lymphatic system (LS). The algorithm for building the graph model of the LS makes use of anatomical data. To identify the edge directions of the graph model, a mass balance approach to lymph dynamics based on Hagen-Poiseuille equation is applied. It is the first study in which a geometric model of the murine LS has been developed and characterized in terms of its structural organization and the lymph transfer function. Our study meets the demand for quantitative mechanistic approaches in the growing field of immunoengineering to utilize or exploit the lymphatic system for immunotherapy.

It is shown by numerical modelling that anisotropic diffusion can compensate and even overcome diffusion broadening - the main limiting factor of devices working at atmospheric pressure. The thickness of ion clouds coming out of the ionisation chamber is reduced ~ 1000 times in the ADMA inlet increasing the resolution. This finding has a great potential that enables construction of a device with resolution close to that of currently available desktop ion separation devices – high pressure mass spectrometers, but with much lower cost of manufacture and with smaller dimensions. Comprehensive numerical modelling along with mathematical analysis of physical concepts enables reduction of the footprint and manufacturing costs. Modelling determined that the resolving power (RP) potential of the ADMA is greater than the RP of currently available on the market devices for the airport security market. The ADMA technology enables very fast detection of threats; at least 100 times faster than currently used in Ion Mobility Spectrometers (IMS). This potentially allows an increase in passenger throughput in airports.

A set of programs for the numerical simulation of the diffusion decomposition processes was developed by using simulation methods, kinetic and particle method. The complex has been validated on the model system Ni-Al by the growth of -phase separations. The results on the evolution of the distribution function and other characteristics of the ensemble, which in the zero volume fraction approximation are asymptotically in good agreement with the theory and the experiment, have been obtained. The peculiarity of the created program complex is the possibility of its adaptation to the description of the decomposition of multicomponent multiphase systems. Fig. 6, list of references - 19 titles.

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often changes as a result of land usage, abstraction as well as variation in climate. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity, and minimising the effects of drought has become a major challenge for researchers as well as water managers. Furthermore, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater resource model have been revealed in the management of groundwater resources. To solve these challenges, many conventional solutions such as numerical techniques have been proffered for groundwater modelling. Also, the use of data-driven techniques such as machine learning is gaining more attraction to solve these aforementioned challenges. Thus, this has made efficient data gathering essential to maintain da-ta-driven groundwater resources management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data At the moment, some pieces of the literature revealed the groundwater managers lack an efficient, scalable and real-time groundwater management system to gather the required data. Also, the literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management methods, nonetheless, this paper seeks to highlight the current groundwater management models as well as the IoT contributions

Resilience has emerged as a buzzword among researchers and practitioners. However, despite its popularity, there has been little progress in moving it from an elaborate metaphor describing an idyllic state of the system to a tool for planning and managing adaptation. While case study research is rich with examples of systems that have proven to be resilient or are striving to develop resilience, there is no defined approach that operationalises concepts described in the literature into the planning process. This paper helps close this gap by illustrating how facilitated modelling can be used for resilience planning in socio-ecological systems. The paper summarizes our experience using facilitated system dynamics to inform a model-based discussion of food security resilience to climate change in Guatemala. We identify at least three positive outcomes from the intervention, which a) helped to build consensus about the meaning of resilience, b) improved stakeholders understanding of adaptation and c) outlined potential policies to enhance resilience.

This study evaluates the effectiveness of Darknet53 and VGG16 pretrained models to develop a hybrid ensemble model named HybridPlantNet23 deep learning model for effective plant disease classification, using the publicly available dataset ‘A Dataset of Leaf Images’ from Mendeley webpage. The preprocessing of the dataset involved resizing the images to the respective requirements of the pretrained models to develop a combined datastore which served as the input. All experiments were conducted using MATLAB R2023b deep network designer interface. Model performances were evaluated based on the conventional accuracy, precision, recall, and F1-score. VGG16 achieved the highest accuracy (97.25%), precision (96.30%), recall (96.96%), and F1-score (96.55%), outperforming both Darknet53 and the ensemble HybridPlantNet23 model. Despite the advanced architecture of the ensemble model, its performance (accuracy: 95.37%, precision: 93.56%, recall: 94.20%, F1-score: 93.74%) was not significantly superior, thus highlighting scientific reasons behind this development. Recommendations for future works were provided accordingly.

It is the aim of this article to introduce a concise notation that is be able to capture the most important dimensions of sexuality and thus facilitate discussions. Moreover, the major three dimensions - sex, gender, and sexual orientation - lead to a straightforward classification which motivates the quest for stringent explanatory models, some of which are also discussed.

. This paper features an analysis of the relative effectiveness of a variety of methods of modelling Realised Volatility (RV), namely: the use of Gegenbaur processes in Auto-Regressive Moving Average format, GARMA, as opposed to Heterogenous Auto-Regressive HAR models and simple rules of thumb. The analysis is applied to two data sets that feature the RV of the S&P500 index, as sampled at 5 minute intervals, provided by the Oxford Man RV database. The GARMA model does perform slightly better than the HAR model, but both models are matched by a simple rule of thumb regression model based on the application of lags of squared, cubed and quartic, demeaned daily returns.

pump as turbine (PAT); computational fluid dynamics; variable rotational nominal speed; OpenFoam.

This paper is focused on understanding multiscale modelling to obtain a bridge among different time and length scales of simulation techniques. These techniques are vital as it holds the potential to understand and predict the capabilities of polymer nanocomposites (PNCs). However, an appropriate approach in controlling the interfacial interaction between nanoparticle and polymer in nanocomposites structure is still needed to develop. In this review, an initial brief introduction to various trending simulation techniques has been discussed at all three levels of scale (nm, μm, mm). Later, descriptive study on fundamental issues such as thermodynamics, kinetics, mechanical properties, and morphology has been studied deeply. The multiscale modeling bridges the gaps of simulation between the different scales of models from molecular to mesoscale levels working over the broad range of length and timescale. Through the sequential, adaptive, and concurrent approaches, we can develop a system that may comprehend multiscale mode modeling adaptive resolution approach has recently added approach the molecule of the subject can shift their position freely in the domain and through this approach and studied the Brownian motion. Co-The co-current coach is also termed as handshaking path and it is linked aiming at different scale models. Covering the rigid techniques smoothly and linking them at different scales helps in normalizing the statistical behaviour.

Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Different fuel cells have been proposed, but experimental testing rigs are expensive and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of operation of fuel cells is compulsory to avoid damage, which must be based on accurate models, able to predict the cell behaviour and prevent stresses and shut-down. Some selected examples of steady state, dynamic and fluid dynamic modelling of different types of fuel cells are proposed, mainly PEMFC and SOFC type. The general ideas behind the thermodynamic, kinetic and transport description are recalled, with some examples of models derived for single cells, stacks and integrated power co-generation units.

Materials characterised by magnetorheological properties are non-classic engineering materials. A significant increase in the interest of scientific community in materials from this group can be observed over the recent several years. The results of research presented in this article are oriented on the examination of said materials’ mechanical properties. In order to do so, stress relaxation tests were conducted on cylindrical samples of magnetorheological elastomers loaded with compressive stress for various values of magnetic induction (B1 = 0 mT, B2 = 32 mT, B3 = 48 mT, and B4 = 64 mT) and temperature (T1 = 25° C, T2 = 30° C, and T3 = 40° C). The results of these tests indicate that the stiffness of examined samples increases along with the increase of magnetic field induction and decreases along with the increase of temperature. On this basis, it has been determined that: the biggest stress amplitude change caused by the influence of magnetic field was σ0ΔB = 12.7% and the biggest stress amplitude change caused by the influence of temperature was σ0ΔT = 11.3%. As a result of applying a mathematical model, it has been indicated that the stress relaxation in the examined magnetorheological elastomer for the adopted time range (t = 3600 s) has a hyperbolic decline nature. The collected test results point to examined materials being characterised by extensive rheological properties, which leads to a conclusion that it is necessary to conduct further tests in this scope.

Integrated Model of Distributed Systems is used for modeling and verification. In formalism, the distributed system is modeled as a collection of server states and agent messages. The evolution of the system takes the form of actions that transform the global system configuration (states and messages) into a new configuration. Formalism is used in the Dedan verification environment for finding different kinds of deadlocks: communication deadlocks in the server view and resource deadlocks in the agent view. For other purposes, a conversion has been developed to equivalent models: to Petri nets for structural analysis and do Distributed Autonomous and Asynchronous Automata (DA3) for easy graphical modeling in terms of system components. In addition, it is possible to simulate a verified system on distributed components in DA3. The automata have two forms: Server-DA3 (S-DA3) for the server view and Agent-DA3 (A-DA3) for the agent view. DA3 formalism is compared to other concepts of distributed automata known from the literature.

The aim of this paper is the presentation of an analytical model of insulator flashover and its application for air at atmospheric pressure and pressurized SF6. After a review of the main existing models in air and compressed gases, we develop a relationship of flashover voltage on the basis of an electrical equivalent circuit and the thermal properties of the discharge. The model includes the discharge resistance, the insulator impedance, and the gas interface impedance. The application of this model to a cylindrical resin-epoxy insulators in air medium and SF6 gas with different pressures gives results close to the experimental measurements.

In this study, an experimentally validated computational model was developed to investigate the hydrodynamics in a rotor-stator vortex RVR agglomeration reactor having a rotating disc at the centre with two shrouded outer plates. A numerical simulation was performed using a simplified form of the reactor geometry to compute the 3D flow field in batch mode operations. Thereafter, the model was validated using data from a 2D Particle Image Velocimetry (PIV) flow analysis performed during the design of the reactor. Using different operating speeds—70, 90, 110 and 130 rpm, the flow fields were computed numerically followed by a comprehensive data analysis. The simulation results showed separated boundary layers on the rotating disc and the stator. The flow field within the reactor is characterized by a rotational plane circular forced vortex flow in which the streamlines are concentric circles with a rotational vortex. Overall, the results of the numerical simulation demonstrate a fairly good agreement between the CFD model and the experimental data as well as the available theoretical predictions. The swirl ratio β was found to be approximately 0.4044, 0.4038, 0.4044 and 0.4043 for operating speeds of N=70, 90, 110 and 130 rpm respectively. In terms of the spatial distribution, the turbulence intensity and kinetic energy are concentrated on the outer region of the reactor while the axial velocity showed a decreasing intensity towards the shroud. However, a comparison of the CFD and experimental predictions of the tangential velocity and the vorticity amplitude profiles shows that these parameters were under-predicted by the experimental analysis which could be attributed to some of the experimental limitations rather than the robustness of the CFD model or numerical code.