Physical-law based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Due to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to model response errors in the predictions compared to the real vehicle. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions, with the ultimate aim of this model taking over when the physics-based model's accuracy degrades. In the current work, a machine-learning technique was employed to train a model to predict the dynamic response of a rotorcraft. Machine learning was facilitated using a Gaussian Process (GP) non-linear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large data sets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the GP model predictions.

The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization.

Network Simulators (NS) is typically used to study services and applications in complex scenarios due to the infeasibility of deploying real testbeds. This is the case of Mobile Ad hoc Networks (MANETs) which can be extensively used in emergency situations or to gather information without infraestructure. An important factor to get realiable results in the MANETs’ simulations is the wireless channel module of NSs. In this report, we explain how thw wireless channel is implemented in NS-3 and it can be configured.

In the development of automatic control, PID control is the oldest of the basic control mode, the control algorithm is the most widely used in engineering, especially the applied research of robustness has extensive engineering practical value. In this paper, based on the principle of the internal model PID parameter setting method was studied, through different approximate processing method, derived the three different formula of PID internal model setting and simulation verify the effectiveness of the algorithm. At the same time, the robustness of the controlled process under different parameter perturbation is theoretically analyzed and simulated.Finally, the paper summarizes the whole paper and looks forward to the future development trend.

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

In this study, we repositioned thiopurine drugs used for the treatment of acute leukaemia as new tyrosinase inhibitors. Tyrosinase catalyses two distinct and successive oxidations in melanin biosynthesis: the conversions of tyrosine to dihydroxyphenylalanine (DOPA) and DOPA to dopaquinone. Continuous efforts are underway to discover small molecule inhibitors of tyrosinase for therapeutic, cosmetic, and agricultural purposes. Structure-based virtual screening has predicted inhibitor candidates for mushroom tyrosinase from drugs approved by the US Food and Drug Administration (FDA). Enzyme assays have confirmed the thiopurine leukaemia drug, thioguanine, as a tyrosinase inhibitor. Two other thiopurine drugs, mercaptopurine and azathioprine, were also evaluated for their tyrosinase inhibitory activity; mercaptopurine caused stronger inhibition than thioguanine did, whereas azathioprine was a poor inhibitor. The inhibitory constants of thioguanine and mercaptopurine were calculated as 52 and 16 µM, respectively, and the value of mercaptopurine was comparable to that of the well-known inhibitor kojic acid (13 µM). The cell lysate and melanin content assay in B16F10 melanoma cells confirmed that the compounds inhibited mammalian tyrosinase. In particular, 50 µM thioguanine reduced the melanin content by 57% without cytotoxicity. Furthermore, the thiopurine drugs shared little chemical similarity with the known tyrosinase inhibitors.

The object of this work is to determine the correlation on the Nusselt number on the individual rows of a four-row tubular finned heat exchanger with continuous fins with a staggered tube arrangement using CFD modelling. Correlations for calculating Darcy-Weisbach friction factors on individual tube rows were also determined. Relationships for the Nusselt number and friction factor derived for the entire exchanger based on CFD modelling were compared with those available in the literature determined using experimental data. The maximum relative differences between the Nusselt number for a four-row exchanger determined experimentally and by CFD modelling are in the range from 22% for a Reynolds number based on a tube's outside diameter of 1,000 to 30% for a Reynolds number of 13,000. The maximum relative differences between the friction factor for a four-row exchanger determined experimentally and by CFD modelling are in the range of 50% for a Reynolds number based on a tube outer diameter of 1,000 to 10% for a Reynolds number of 13,000. The CFD modeling performed shows that in the range of Reynolds numbers based on hydraulic diameters from 150 to 1,400, the Nusselt number for the first row in a four-row finned heat exchanger is about 22% to 15% higher than the average Nusselt number for the entire exchanger. In the range of Reynolds number changes based on hydraulic diameter from 2,800 to 6,000, the Nusselt numbers on the first and second rows of tubes are close to each other. Correlations on Nusselt numbers and friction factors derived for individual tube rows can be used in the design of plate-fin and tube heat exchangers used in equipment such as air-source heat pumps, automotive radiators, air-conditioning systems and in air hot-liquid coolers. In particular, the correlations can be used to select the optimum number of tube rows in the exchanger.

In this work, the thermodynamic data basis and EOS modeling necessary to simulate the fractionation of organic liquid products (OLP), a liquid reaction product obtained by thermal catalytic cracking of palm oil at 450ºC, 1.0 atmosphere, with 10% (wt.) Na2CO3 as catalyst, in multistage countercurrent absorber/stripping columns using SC-CO2 as solvent, with Aspen-Hysys was systematically investigated. The chemical composition of OLP was used to predict the physical (), thermo-physical properties (Tb, Tc, Pc, Vc), and acentric factor () of all the compounds present in OLP by applying the group contribution methods of Marrero-Gani, Han-Peng, Marrero-Pardillo, Constantinou-Gani, Joback and Reid, and Vetere. The RK-Aspen (EOS) used as thermodynamic fluid package, applied to correlate the experimental phase equilibrium data of binary systems organic liquid products compounds (OLP)-i/CO2 available in the literature. The group contribution methods selected based on the lowest relative average deviation by computing Tb, Tc, Pc, Vc, and . For n-alkanes, the method of Marrero-Gani selected for the prediction of Tc, Pc and Vc, and that of Han-Peng for . For alkenes, the method of Marrero-Gani selected for the prediction of Tb and Tc, Marrero-Pardillo for Pc and Vc, and Han-Peng for . For unsubstituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of Tb, Marrero-Gani for Tc, Joback for Pc and Vc, and the undirected method of Vetere for . For substituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of Tb and Pc, Marrero-Gani for Tc and Vc, and the undirected method of Vetere for . For aromatic hydrocarbon, the method of Joback selected for the prediction of Tb, Constantinou-Gani for Tc and Vc, Marrero-Gani for Pc, and the undirected method of Vetere for . The regressions show that RK-Aspen EOS was able to describe the experimental phase equilibrium data for all the binary pairs undecane-CO2, tetradecane-CO2, pentadecane-CO2, hexadecane-CO2, octadecane-CO2, palmitic acid-CO2, and oleic acid-CO2, showing average absolute deviation (AADx) between 0.8% and 1.25% for the liquid phase and (AADy) between 0.01% to 0.66% for gaseous phase.

The application of numerical simulation methods for studying vertical-axis wind turbines offers significant advantages over conventional horizontal-axis wind turbines regarding wind energy utilisation, start-up wind speed, and environmental impact. This paper aims to investigate and establish the aerodynamic design performance of asymmetrical (NACA 4412) airfoils used in the application of vertical-axis wind turbines. The computational fluid dynamics (CFD) simulation uses Ansys software based on the Reynolds Averaged Navier-Stokes (RANS) equations. The aerodynamic parameters (pressure and velocity) are evaluated and compared within a range of angles of attack (AoA) 5-12 degrees at a fixed Reynolds number, as is a usual condition for wind turbine application. The lift coefficient (Cl), drag coefficient (Cd), and Cl/Cd ratio are obtained within the range AoA for the asymmetrical airfoils, and the performances have been predicted using the potential flow panel and CFD methods. This study suggests that while Xfoil may not be as precise as computational fluid dynamics (CFD), its predicted lift and drag coefficients generally align with experimental results. These help us to understand the best air-foil selection in a wind turbine application and predict its optimum AoA at which it performs at its best.

This study evaluates eight-year ownership costs for battery electric vehicles (BEV) versus non-plugin hybrid vehicles using forecasting to estimate future electricity and conventional gasoline prices and incorporating these in a multiple design of experiments simulation. Results suggest that while electric vehicles are statistically dominant in terms of variable costs over an 8-year life-span, high-performance hybrid non-plugins achieve variable fuel costs nearly as good as low-performing electric vehicles (those attaining only 3 miles per kilowatt hour) and that these hybrid acquisition costs are (on average) lower yet the vehicles retain higher residual values. In general, the six smallest ownership costs are split evenly between hybrid and electric vehicles; however, inflation for conventional regular gasoline is estimated to outstrip inflation per kilowatt hour. Thus, non-plugin hybrid cars are likely to require considerably more advanced engineering to keep pace.

The molecular dynamics simulation has been used to investigate the structural and transport properties of (Ba_0.5-xSr_x)La_0.5InO_3-δ (x=0, 0.1, 0.2) oxygen-ion conductor. The previous studies reported that the ionic conductivity of Ba-doped LaInO3 decreases because Ba dopant forms narrow oxygen path in the lattice, which could hinder the diffusion of oxygen ion. In this study, we reveal the mechanism to improve the ionic conductivity by Ba and Sr co-doping on La site in LaInO3 perovskite oxide. The results show that the ionic conductivity of (Ba_0.5-xSr_x)La_0.5InO_3-δ increases with increasing numbers of Sr ions, which oxygen diffusion paths including Sr ion have larger critical radius than Ba ions. The RDF calculations showed the heights of peak in composition including Sr ions is lower and broaden, so oxygen ions moved easily into other oxygen sites.

The energy transfer process of laser selective melting is very complex. To study the effect of la-ser selective melting on the microstructure and properties of 18Ni-300 martensitic steel, ABAQUS was used to simulate the temperature of laser cladding 18Ni-300 martensitic steel at different time points and different laser power. The results show that the cross-section shape of the molten pool changes from round to oval With the increase of laser power, the higher the peak value of temperature time curve, the greater the temperature gradient; and the laser clad-ding experiment of 18Ni-300 martensitic steel was carried out, and the microstructure and me-chanical properties of the samples under different laser power were analyzed. The results show that with the increase of laser power, the grain size of the cladding layer becomes smaller and the microstructure becomes more compact; the hardness of the side surface of the sample is higher than that of the upper surface, and the tensile strength and elongation show a trend of first increasing and then decreasing.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

In rock blasting for mining production, stress waves play a major role in rock fracturing along with explosion gases. Better energy distribution improves fragmentation and safety, lowers production costs, increases productivity, and controls ore losses and dilution. Blast outcomes vary significantly with the choice of the explosive and the properties of the rock mass encountered. This study analyzes the effects of rock mass and explosives properties on the blast outcomes through numerical simulation using data from the case study and later validates the simulation results from the field blast fragmentation. The outcomes suggest that, for a given set of rock properties, the choice of explosive has a major influence on the resulting fragmentation. Strong explosives favor large fracture extents in hard rocks, while less strong explosives offer a better distribution of explosive energy and fractures. The presence of rock structures such as rock con-tacts and joints influences the propagation of stress waves and fractures depending on the structures' material properties, intensity and orientations, and the direction and strength of the stress wave. To achieve effective fragmentation, the blast design should mitigate the effect of variability in the rock mass by ensuring adequate energy distribution within the limits of geo-metrical design.

Home care centers face both an increase in demand and many variations during the execution of routes, compromising the routes initially planned: robust solutions are not effective enough, it is necessary to move on to resilient approaches. We create a close to reality use case supported by interviews of staff at home health care centers, where caregivers are faced with unexpected events that compromise their initial route. We model, analyze and compare three resilient approaches to deal with these disruptions: a baseline approach without any collaboration, a distributed collaborative approach, and a centralized collaborative approach, where we propose a centralization and sharing of information to improve local decision-making. The latter reduces the number of late arrivals by 11% and the total time of late arrival by 21%, and also halves the number of routes exceeding the end of work time (contrary to the distributed collaborative approach, due to the time wasted reaching colleagues). The use of a device, such as a smartphone application, to centralize and share information thus allows better mutual assistance between caregivers. Moreover, we highlight several possible openings like the coupling of simulation and optimization to propose a more resilient approach.

Studies on the detection of layers with elevated black carbon aerosol (BC) concentrations and the formation conditions of these layers help understand the vertical distribution of BC concentrations, which will provide a basis for the assessment of climate effects and early BC pollution warnings. By using the Weather Research and Forecasting with Chemistry (WRF-Chem) numerical model, we performed a numerical simulation analysis on the authenticity of strong elevated BC concentration layers that were detected by an aircraft in the mixing layer over Harbin, China, which is a high-emission area, on a clear sunny afternoon in the early heating period of 2016. We then discuss possible problems and solutions when non-vertical paths are used to detect the vertical distribution of BC concentrations. Finally, we discuss the favorable conditions for the formation of elevated BC concentration layers by weak vertical flow. The results show that the horizontal variability of BC concentration in the mixing layer in the observation area in Harbin was sufficiently large during the measurement. This produced a false elevated layer, as detected by the aircraft during one round of spiral flight in the mixing layer. The root mean square of the horizontal distribution of BC concentration did not change with height in the mixing layer during the daytime, but it decreased with the thickness of the mixing layer and was higher in the mixing layer than in the free atmosphere. Therefore, the thinner the mixing layer, in which the vertical distribution of the BC concentration is detected in an inclined path, the stronger interference of the horizontal variability on the detected results. When a spiral flight detection path is used, the aircraft should fly at least two rounds in the mixing layer. In the daytime, due to strong turbulence in the mixing layer, weak vertical uplift is not favorable for the occurrence of elevated BC concentration layers in the mixing layer. In the nighttime, if weak vertical uplift is well matched with the BC concentration or its vertical gradient, elevated BC concentration layers can be formed in the atmosphere. Compared with upper layers far from the ground, nighttime elevated layers are easier to form in lower layers near the ground because high BC concentrations or large vertical gradients are more likely to occur in the lower layers. Both cases facilitate the occurrence of large vertical upward transport rates of BC.

The paper explores the features of numerical simulation to analyze the dynamic behavior of drives controlled by pulse-width modulators. Modern motor control systems widely use pulse width modulation. Effective numerical modeling of such systems has specific features because the models employed are continuous-event and have hybrid behavior due to the presence of nonlinear links with discontinuities of the first kind. Therefore, it is essential to have special integration methods with variable steps, which should be factored in when developing the model. The paper shows how these problems are solved when modeling an electric drive with a DC motor in the SimInTech software.

Bimetallic nanoparticles are of interest since they lead to many interesting electrical, chemical, catalytic, and optical properties. They are particularly important in the field of catalysis since they show superior catalytic properties than their monometallic counterparts. The structures of bimetallic nanoparticles depend mainly on the synthesis conditions and the miscibility of the two components. In this work, PdPt alloyed-bimetallic nanoparticles (NPs) were synthesized through the polyol method, and characterized using spherical aberration (Cs) corrected scanning transmission electron microscopy (STEM). High-angle annular dark-field (HAADF)-STEM images of bimetallic nanoparticles were obtained. The contrast of images shows that nanoparticles have an alloy structure with an average size of 8.2 nm. Together with the characterization of nanoparticles, a systematic molecular dynamics simulations study, focused on the structural stability and atomic surface segregation trends in 923-atom PdPt alloyed-bimetallic NPs was carried out.

Polymethyl methacrylate (PMMA) polymer is widely used in various fields today. In order to thoroughly reveal the structural impact performance of PMMA materials, this paper is based on the comprehensive Johnson-Cook constitutive model and damage failure model, and accurately confirm the J-C constitutive and damage failure model parameters of PMMA through material test data, The dynamic process of steel bullet impacting PMMA plate structure is analyzed by finite element software ABAQUS. The calculation results show that the numerical simulation results in this paper are in good agreement with the experimental test data. Therefore, the feasibility and accuracy of the impact analysis of PMMA structures based on J-C constitutive and damage failure models in this paper are studied. Finally, based on the J-C constitutive model and damage failure model, the variation of the residual velocity of the bullet with the PMMA plate thickness is analyzed in depth, that is, the results show that the residual velocity of the bullet has a certain linear relationship with the plate thickness.

Due to the accuracy of numerical calculation of fluid flow inside a hydrocyclone can be obtained using Computational Fluid Dynamics (CFD), highly modified super computers are used to simulate the fluid flow and track particle motion inside a hydrocyclone. This paper deals with the numerical study using three multiphase models viz. Volume of fluid, Mixture and Eulerian model. The dimensions of the hydrocyclone taken into consideration for numerical analysis is same as considered by Rajamani. Validation of axial and tangential velocities at different strategically decided axial stations, RMS axial and tangential velocity profiles of the hydrocyclone is done using Reynolds Stress Model (RSM). The hydrocyclone model has been designed in Creo 3.0 using the same dimensions which later was imported to CFD for meshing. Fine hexagonal mesh numbering up to 5 lacs were constructed to obtain optimum results. Fluid flow was allowed to be developed in ANSYS FLUENT 16.2. Entire simulation took 96 hours to generate results and track particle movements inside the hydrocyclone. The particle tracking has been done using three multiphase model. The first being the volume of fluid was used for validation purposes and the comparison of the Mixture and Eulerian model are the basic focus of this research work. Conclusive results indicate that usage of different multiphase model does not result in variation in particle motion. The slight variation in grade efficiency values are hardly noticeable. The Mixture model and Eulerian model predict lower separation efficiency as compared with Volume of fluid multiphase model.

Natural gas is an important energy source among various fossil fuels. However, natural gas typically contains a significant amount of water vapor, which can pose challenges in its usage. Therefore, the process of removing water vapor from natural gas, known as gas dehydration, is crucial in the gas industry. The presence of water vapor in the gas supply can lead to the formation of hydrates, which can cause issues such as blockages in pipelines. To tackle this problem effectively, gas dehydration makes use of Tri ethylene glycol (TEG) as a drying agent. TEG works by selectively absorbing water vapor from the natural gas flow, thereby significantly reducing its moisture content.In the gas dehydration process, wet gas is treated by using lean glycol in an absorber, where the water vapor is removed. The resulting rich glycol is then recovered and recycled for further use. In this study, we explore the possibility of replacing nitrogen with dry natural gas in the re-generator of the glycol dehydration system. The aim is to evaluate the feasibility and effectiveness of this alternative approach. To assess the performance of both techniques, we employed HYSYS modeling and simulation. Through this analysis, we compared the capital and utility expenses associated with each method while ensuring that the glycol purity requirements remained unchanged. By examining the results obtained from the simulations, we can gain insights into the economic viability and efficiency of utilizing dry natural gas in the re-generator stage of the glycol dehydration process.In addition to the primary purpose of removing water vapor, the wet gas obtained from the stripping mechanisms in the glycol dehydration process can serve additional functions. It can be utilized to power steam pumps and compressors, thereby maximizing energy efficiency within the system. Alternatively, the wet gas can be recycled back into the process for further treatment. To develop a comprehensive understanding of the entire mechanism, we constructed a model based on the actual flow diagram. This model takes into account the various components and processes involved in the glycol dehydration system, including the stripping mechanisms and their connection to other equipment. By analyzing the data and outcomes generated by this model, we can derive valuable insights. These findings can be utilized to optimize the heat and material balance within the plant, ensuring efficient operation and potentially leading to the design of an improved system in terms of energy consumption and overall performance

One of the non-linear phenomena that affect high bandwidth and long reach communication systems is the non-linear phenomenon called four-wave mixing (FWM). Unfortunately, the simulation of such systems aiming to obtain their design parameter limitations require more time as the number of channels increases. In this paper, we propose a new high-performance computational model to obtain optimal design parameters in a multi san Dense Wavelength Division Multiplexing (DWDM) system, limited by FWM and the intrinsic Amplified Spontaneous Emission (ASE) noise of optical amplifiers employed in each segment. The simulation in this work provides a complete optical design characterization and compares the efficiency and speed improvement of the proposed parallelization model versus a previous sequential model. Additionally, an analysis of the computational complexity of parallel model is presented, where two parallel implementations are used. First, Open Multi−Processing (OpenMP), based on the use of a central, multi-core processing unit is used and secondly the Compute Unified Device Arquitecture (CUDA), which is based on the use of graphics processing unit. Results show that parallelism improves to up to 40 times the performance of the simulation when nested parallelization with CUDA is used, over de sequential method and up to 6 times compared with the implementation with OpenMP using 12 processors. Within our parallel implementation, it is possible to simulate with an increased number of channels, that was unpractical in the sequential simulation.

Progress in laser-driven proton acceleration requires increasing the proton maximum energy and laser-to-proton conversion efficiency, while reducing the divergence of the proton beam. However, achieving all these qualities simultaneously has proven challenging experimentally, the increase in beam energy often coming at the cost of beam quality. Numerical simulations suggest that coupling multi-PW laser pulses with ultrathin foils could offer a route for such simultaneous improvement. Yet experimental investigations have been limited by the scarcity of such lasers and the need for very stringent temporal contrast conditions to prevent premature target expansion before the pulse maximum. Here, combining the newly commissioned Apollon laser facility that delivers high-power ultrashort (∼24fs) pulses, with a double plasma mirror scheme to enhance its temporal contrast, we demonstrate the generation of up to 35 MeV protons with only 5 J of laser energy. This approach also achieves improved laser-to-proton energy conversion efficiency, reduced beam divergence, and optimized spatial beam profile. Therefore, despite the laser energy losses induced by the plasma mirror, the proton beams produced by this method are enhanced on all accounts compared to those obtained under standard conditions. Particle-in-cell simulations reveal that this improvement results from the intensified electrostatic field caused by a more compact hot-electron distribution.

Heat transfer enhancement and hydrodynamic characteristics of nanofluid use in a micro heat exchanger is investigated for cooling electronic components working in hot climatic conditions. The cooling fluid employed was water and TiO2 nanoparticles at concentrations of 1% and 5%, Reynolds numbers ranged from 400 to 2000, and inlet temperatures ranged between 35ºC and 65ºC. At a nanofluid inlet temperature of 55ºC and a nanoparticle concentration of 1%, the Nusselt number increased by 23% up to 54% as the Reynolds number varied between 400 and 2000. At a nanoparticle concentration of 5%, the percentages which correspondingly enhanced the Nusselt number were 32% and 63%. The temperature of the electronic heating component decreased by 4.6-5.2ºC when the nanofluid concentration was increased from 0 to 5% at a Reynolds number of 400 and a nanofluid inlet temperature of 35ºC. Small increments in pressure drop of about 6% and 13% were observed at nanofluid concentrations of 1% and 5%, respectively. With nanoparticle concentrations of 1% and 5%, a Reynolds number of 2000 and a nanofluid inlet temperature of 35ºC, performance evaluation criterion (PEC) values of 1.36 and 1.45 were obtained. When the nanofluid inlet temperature was increased to 65ºC, the PEC parameter decreased to 1.02-1.10 for both concentrations.

It is imperative to examine the performance of wind turbines both in steady and unsteady states due to their significant role in generating renewable energy by utilizing wind power. The computational fluid dynamics method (CFD) is one of the most efficient tools in this field, and the unsteady state has a great deal of importance when predicting turbine performance. Using the CFD method, the hydrodynamic performance of two types of offshore and onshore horizontal-axis wind turbines in the unsteady state was examined. Offshore wind turbines were modeled using the SST turbulence model and onshore wind turbines were modeled using the large eddy simulation (LES). Three-dimensional results of the simulation were compared and validated with experimental results, and good agreement between the two results indicates that the assumptions and method were accurate. Vortices were also studied in relation to the blade tip and their formation and development. Increasing the number of blades results in a reduction in torque, as well as a rise in vortices caused by the tips of the rotor blades. Due to the intensity of turbulence, the distribution of pressure on the surfaces will become irregular as the wind speed increases.

In response to the new concept of the impact of total climate production factors on plant phenology, this study will verify the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plant from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors influencing plant phenological changes can be well used for phenological simulation and its triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plant, and their triggering thresholds depend on the parameters of climate resource change and plant biology, which are (0.085, –5.363) and (0.086, –27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plant, and its triggering thresholds also depends on the parameters of climate resource change and plant biology, which is (394.632，–38026.268). Furthermore, the results support the viewpoint that the climate abrupt changes determine the beginning and the ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

With the every passing day, the demand for data traffic is increasing and this demand forces the research community not only to look for alternating spectrum for communication but also urges the radio frequency planners to use the existing spectrum smartly. Cell size is shrinking with the every upcoming communication generation which makes the base station placement planning complex and cumbersome. In order to make the next-generation cost-effective, it is important to design the network in such a way which utilizes minimum number of base stations while ensure coverage and quality of service. This paper aims at develop a new approach using hybrid metaheuristic and metamodel applied in multi-transmitter placement planning (MTPP) problem. We apply radial basis function (RBF) metamodel to assist particle swarm optimizer (PSO) in a constrained simulation-optimization (SO) of MTPP to mitigate the associated computational burden of optimization procedure. We evaluate the effectiveness and applicability of proposed algorithm in a case study by simulating MTPP model with two, three, four and five transmitters.

Abstract: An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion, involving the decomposition of the organic matter in two or more substrates by bacteria and archaea, in the absence of oxygen. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab, with Monod kinetics involving two substrates and an intermediate (M2SI model), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi-Sugeno approach available in the ANFIS package of Matlab. The biomethane production results simulated by M2SI were used in neural network modeling, firstly employing a “generic” network applicable to all 8 scenarios. A very good fit was obtained (R²>0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition). The parameters of the M2SI model for the 8 different conditions were also mapped using a neural network, as a function of the organic material composition. A fit with R²>0.99 was obtained using 25 neurons. In the case of the fuzzy logic, RMSE of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks).

The stability of the filling roof as an important bearing unit in the stope of the access stope in the filling mining method is of great significance to guarantee the safe and efficient production of the mine. Arrangement of mining approach in downward cemented filling stope is the key factor af-fecting stability of filling body roof. Based on combination of laboratory test, theoretical analysis and numerical simulation, the influence of different mining approach arrangement on stability of filling body roof is analyzed. The weak filling surface is formed between adjacent mining paths. The mechanical strength of the weak filling surface is significantly reduced by laboratory experiments. The relationship between distribution of the weak filling surface, azimuth angle and stability of filling roof is further studied by numerical simulation. The results show that the stability of the filler roof is the best when the upper and lower stratified mining approach is arranged vertically.

This research paper presents a comprehensive study on the gasification of wheat straw pellets in a 10-kW fixed bed gasifier through a combination of Computational Fluid Dynamics (CFD) simulation and experimental validation. The developed 2D CFD model in ANSYS meshing simulates the gasification process in ANSYS Fluent software. The investigation evaluates key parameters such as equivalence ratio, higher heating value, and temperature distribution within the gasifier to enhance syngas production efficiency. The simulated results exhibit a noteworthy agreement with experimental data obtained from gasification. The impact of the equivalence ratio on gas production and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for syngas production. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The study further investigates the positive influence of reactor temperature on increasing syngas quantity while considering the uneven shape of the reactor and various thermal reactions affecting temperature, static pressure, velocity, and density. The findings contribute valuable insights to improve the efficiency of fixed-bed gasifiers, offering essential information on performance parameters for sustainable and optimized syngas production.

Additive manufactured interlocking structures often face challenges in achieving suitable joinability due to thermal deformation. This issue becomes particularly interesting when fabricating structures that require high density but not fully connected layers, such as soft-magnetic materials. This study focuses on systematical investigation of the deformation as a function of finger thickness and building direction in model interlocking structure. In the perpendicular direction to the build plate, the distortions of joint part were significantly reduced due to lower thermal stress. In addition, the effect of laser power and scan speed on interlocking structure are systematically discussed. Additionally, through stress and thermal simulations of additive manufacturing process, it was confirmed that the compensated model can enhance the joinability of interlocking structures. These findings contribute to the development of optimal design for additive manufactured parts of soft magnetic materials such as stators in electric motor components, which needs insulations and density.

We outline and test a new methodology for genuine simulation of stochastic processes with any dependence and any marginal distribution. We reproduce time dependence with a generalized, time symmetric or asymmetric, moving-average scheme. This implements linear filtering of non-Gaussian white noise, with the weights of the filter determined by analytical equations in terms of the autocovariance of the process. We approximate the marginal distribution of the process, irrespective of its type, using a number of its cumulants, which in turn determine the cumulants of white noise in a manner that can readily support the generation of random numbers from that approximation, so that it be applicable for stochastic simulation. The simulation method is genuine as it uses the process of interest directly without any transformation (e.g. normalization). We illustrate the method in a number of synthetic and real-world applications with either persistence or antipersistence, and with non-Gaussian marginal distributions that are bounded, thus making the problem more demanding. These include distributions bounded from both sides, such as uniform, and bounded form below, such as exponential and Pareto, possibly having a discontinuity at the origin (intermittence). All examples studied show the satisfactory performance of the method.

The planning and decision-making for a distributed energy supply concept in complex actor structures like in districts calls for the approach to be highly structured. Here, a strategy with strong use of energetic simulations is developed, the core elements are presented and research gaps are identified. The exemplary implementation is shown using the case study of a new district on the former Oldenburg airbase in northwestern Germany. The process is divided into four consecutive phases, which are carried out with different stakeholder participation and use of different simulation tools. Based on a common objective, a superstructure of the applicable technologies is developed. Detailed planning is then carried out with the help of a multi-objective optimal sizing algorithm and Monte Carlo based risk assessment. The process ends with the operating phase, which is to guarantee a further optimal and dynamic mode of operation. The main objective of this publication is present the core elements of the planning processes and decision-making framework based on the case study and to find and identify research gaps that will have to be addressed in the future.

The problem of optimal scheduling in a system with parallel queues and a single server has been extensively studied in the queueing theory. However, such systems have mostly been studied by assuming homogeneous attributes of arrival and service processes, or in heterogeneous case a Markov random process was usually assumed. In this paper, we explore the possibility of combining simulation and neural network paradigms for optimal scheduling in a heterogeneous system with an arbitrary inter-arrival and service time distributions. The service of any queue by the server is exhaustive. Further routing of the server after the queue has been emptied is as instructed by the trained neural network. Any change in the queue that currently has a server is accompanied by a switching cost. The simulated annealing optimization algorithm which is used to optimize the parameters of the neural network was adapted for numerically evaluated average cost function. A Markov decision problem was formulated for the corresponding markovian queueing system to verify the results of solving the optimization problem. In addition to comparing the actual values of the average cost, the parameter values of the neural network trained on the optimal control policy of the Markov model are also used to determine the domains of parameters required for the simulated annealing defined on a finite discrete domain. Numerical analysis shows the effectiveness of this approach. Moreover, a comparison of the results for different distributions illustrates the insensitivity of the optimal control policy to the shape of the distributions for the same two first moments. Thus, the optimal control policy for an exponential model can be used as a suboptimal one for the general system.

Keywords: Amorphous-precipitate, Nitride, Morphological stability, Diffusion, Internal stresses, Modelling, Simulation

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. A simple model in [] of Electric Submersible Pump [ESP] lifted production was previously streamlined into a dynamic model suitable for illustration of the dynamics of oil transport, as well for control studies, with some comparison of two popular modeling languages: Modelica, and ModelingToolkit for Julia, []. Here, the discussion on dimensionless equipment models goes into more detail, and the comparison between Modelica and ModelingToolkit is significantly expanded upon with code comparison, numerical performance, and more experiments. Some added possibilities with ModelingToolkit and Julia wrt. sensitivity analysis and control design is included.

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we present an improved innovative sequence–activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this improved approach, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability than that predicted using the existing data. We successfully improved the determination coefficient (R2) of the model from 0.66 to 0.92. In addition, root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were estimated and conformation analysis based on molecular dynamics simulations was performed to verify the enhanced thermal stability of the screened mutants. The improved innov'SAR algorithm enhanced the predictive accuracy, suggesting a method for modifying the stability of AT-ATA, which may help in directed evolutionary screening and open up new avenues for protein engineering.

This work focused on proposing a plan for improvement and control of the customer service process in a customer service center, based on a waiting line analysis where it describes all the elements that interact in the system and a statistical analysis of processes to define the state in which it operates. Subsequently, the simulation of processes is applied to represent the system studied, and with it, to be able to evaluate different strategies for improvement and increase in productivity. The following strategies were evaluated: correction of the instability of customer service processes, establishment of new specification limits according to the promise of value to be fulfilled to customers, human resource management, administrative technical support. Finally, with the results obtained from the simulation of the system, the improvement and productivity increase plan is planned according to the DMAMC (Define, Measure, Analyze, Improve, Control, or DMAIC) continuous improvement cycle established in six sigma

This study aimed to establish and verify the validity of an acoustic simulation method during sustained phonation of the Japanese vowels /i/ and /u/. The study participants were six healthy adults. First, vocal tract models were constructed based on computed tomography (CT) data, such as the range from the frontal sinus to the glottis, during sustained phonation of /i/ and /u/. Next, cylindrical shapes virtually extended by 12 cm were added to the vocal tract models to imitate the trachea between the tracheal bifurcation and lower part of the glottis. The Kirchhoff–Helmholtz integral equation was formulated as the wave equation for sound propagation, and the boundary element method was used for discretization. As a result, the relative discrimination thresholds of the vowel formant frequencies for /i/ and /u/ against actual voice were 1.1%–10.2% and 0.4%–9.3% for the first formant and 3.9%–7.5% and 5.0%–12.5% for second formant, respectively. In the vocal tract model with nasal coupling, a pole–zero pair was observed at around 500 Hz, and for both /i/ and /u/, a pole–zero pair was observed at around 1000 Hz regardless of the presence or absence of nasal coupling. These findings demonstrated that /i/ and /u/ could be simulated with high validity in a vocal tract model constructed from CT data obtained during sustained phonation using the boundary element method.

In the construction of shield crossing existing mined tunnel without load, it is imperative to develop corresponding design standards that reflect actual engineering force characteristics to ensure the successful completion of tunnel construction. This research uses The MIDAS-GTS finite element software to facilitate the creation of a numerical model of the shield structure for an air-push-over mine tunnel project in Changsha, China, investigating the stress field’s evolution during shield construction, and calculating the maximum positive and negative bending moments and maximum axial forces for different structures and other force states under various construction conditions. This study's findings informed the design and construction optimisation of the shield tunnelling empty-push method. The outcomes of this numerical simulation led to several key findings: (1) The soil density exerted a significantly greater impact on the internal forces of the initial support structure than both the tunnel depth and soil Poisson's ratio. Additionally, a sudden shift in internal forces occurred within the 300-350 mm range when the lining thickness was altered. (2) Factors such as the tunnel depth, soil density, soil Poisson's ratio μ, and lining thickness have a similarly influenced internal forces of the segment and the initial support. Notably, the backfill layer thickness significantly affected the segment’s maximum axial force causing an abrupt change of approximately 300 mm. (3) Under the shield machine equipment’s weight constraint, it is essential to control the guide rail’s thickness, to prevent it from becoming overly large.

Keywords: Deep mining; Drill bit structural parameters; Fidelity coring; Abaqus simulation and analysis.

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for monsoon region. We develop a deep neural network composed of convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the ECMWF-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including 1) quantile mapping, 2) support vector machine, and 3) convolutional neural network. To test the robustness of the model and its applicability in practical forecast, we apply the trained network for precipitation prediction forced by retrospective forecasts from ECMWF model. Compared to ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead time from 1 day up to 2 weeks. This superiority decreases along forecast lead time, as GCM’s skill in predicting atmospheric dynamics being diminished by the chaotic effect. At last, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is just slightly worse than the observed precipitation forced simulation (NSE=0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.

Nuclear export of viral mRNAs, is an essential step in the HIV replication cycle. This role is played by a small regulatory protein of HIV-1 called Rev.The N-terminal region of Rev contains an arginine-rich sequence. The arginine-rich motif (ARM) is located between amino acids 38-50 and forms an alpha-helical secondary structure. Expression of the structural proteins of human immunodeficiency virus type 1 requires the direct interaction of multiple copies of the viral Rev protein with its highly structured RNA target sequence, the Rev Response element (RRE). The major viral proteins are not produced if this transport of RNA is stopped. Therefore, knowledge of Rev structure is essential for understanding of its cooperative binding to the RRE, for understanding the mechanism of HIV infection and for the development of antiviral drugs that interfere with Rev’s essential functions and for acknowledgment of good candidate drugs for treatment of AIDS. To understand how REV interact with RRE element of HIV-RNA and its formation of oligomeric complex it is better to characterize the domain wise structure of REV with regard in function of each domain. Due to lack of structural data on Rev no single compound is reported as inhibitor of REV expect antiviral drugs. Identification of a high-affinity RNA-binding site for the human immunodeficiency virus type 1 Rev Protein is much more important. The ARM is a highly specific sequence which allows for the multimerization of Rev and also binding of REV with RNA. Here we are first time exploring the structural characteristics of REV protein both in free form and in complex with RNA at domain function level especially explore the role of ARM motif in REV HIV-1 protein as RNA binding sites by molecular dynamics (MD) simulation and homology modeling studies. Results indicate that the arginine-rich motif (ARM) is crucial in stability of this complex. The residues ARG38, 39, 41, 43, 44, 48, 50, and ASN40 are most interacting with nucleobases of RRE in Crystal structure of Rev and Rev-response-element RNA complex. Our study plays a major role in elaboration of binding of RNA with REV and pave the way for further investigation for therapeutically agent for HIV.

In this study, three new demountable joints consisting of reinforced concrete columns and steel beams were proposed and their seismic performance was investigated using cyclic loading tests. The test results demonstrated that the three demountable RCS joints had good seismic performance. Using the finite element software ABAQUS, the influence of parameters such as beam flange thickness, bolt strength, and connector steel strength on the seismic performance of each joint was analyzed, and the influence of different parameters on the seismic behavior of the joint was determined. The results also showed that the three demountable RCS joints were sensitive to changes in the thickness of the steel beam flange, while the connector steel strength and bolt type had little impact on the joint's capacity. Additionally, shear capacity calculation formulas for the joint core area provided by different codes and researchers were compared with test and finite element results. The results showed that the calculation results of the ASCE guidance method, the Nishiyama method, and the CECS 347-2013 method were higher than the test values, while the calculation results of the Para method were lower because it did not consider the contribution of the cylindrical steel plate to the shear capacity of the joint core area.

In this paper we investigate in different scenarios the feasibility of using massive multiple-input multiple-output (mMIMO) with Reconfigurable Intelligent Surfaces (RISs) to boost the throughput and coverage performances with high energy efficiency, considering sub-6 GHz, mmWave and THz bands. With that objective, a centralized radio access network (C-RAN) suitable for beyond fifth generation (B5G) systems is considered, where we integrate the base stations (BSs) with multiple RISs and devices or user equipment. RISs with a large number of quasi-passive reflecting elements constitute a low-cost approach capable of shaping the radio wave propagation and improve wireless connectivity. We consider a scenario where multiple RISs are combined with mMIMO in the uplink in order to provide connectivity to Smart City (thousands of active low power devices) wirelessly, in the 3.6GHz and 28GHz bands. We also address a scenario where RISs are adopted with mMIMO in the downlink so as to offer connectivity to a Stadium with Pitch, (thousands of active user equipment) in the 28GHz band. Finally, we have also studied the connectivity at 100GHz of a Factory where several RIS panels replacing most of the BSs equipped with mMIMO assure improved throughput and coverage. We concluded that RISs are capable of improving the performance in any of these analyzed scenarios at the different frequency bands, justifying that they are a key enabling technology for 6G.

Abstract: The purpose of this study is to explore, from the perspective of government interven-tion, behavioral logic and game relationship among farmers, service organizations and the gov-ernment in the pest and disease control outsourcing system, as well as the endogenous motiva-tion of each subject. The results indicate that the stronger the willingness of each subject, the faster the stable state of joint pest and disease control among the three parties can be formed; In the case of implementing a single policy tool, the convergence rate of each party that imple-ments the regulatory policy alone is fast but may be unstable, while the rate is slow but more stable when a guidance- or incentive-based policy is solely applied; The effect of policy tool combination is much better than that of a single policy tool applied. The simultaneous imple-mentation of the three types of policy tools can form a policy system with both positive and negative mechanisms, which can maximize complementary and superposition effect.

This research investigated the antihypertensive effects of tamarind products and compared their potentials based on their animal model’s data verified by molecular docking, multitarget interactions in network pharmacology, and dynamic simulation assays. The GC-MS characterized tamarind products were administered to the cholesterol-induced hypertensive Wistar albino rat models. Two-week-intervened animals were dissected to collect serum and organs which were respectively subjected to the analyses of hypertension-linked markers and tissue architectures. Lead biometabolites of tamarinds interacted with eight target receptors in molecular docking and dynamic simulation studies and they were tested for multitarget interactions using network pharmacological analyses. Results showed that serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), C-reactive protein (CRP), Troponin I, and lipid profiles were maximally reinstated by the phenolic-enriched ripened sour over the sweet tamarind flesh extracts, but seed extracts had a smaller influence. Among the tamarind’s biometabolites, Gamma-sitosterol (ϒ-sitosterol) was found to be the best ligand in managing hypertension via the possible ligand-receptor interactions with the guanylate cyclase which displayed the best drug-likeliness through the highest binding energy -9.3 Kcal. Multitargeted interactions-based degree algorithm and phylogenetic tree of pathways showed 14 targeted genes, of which NR3C1, REN, PPARG, and CYP11B1 hub genes, were consistently modulated by the ϒ-sitosterol to reduce hypertension and related risk factors. The dynamic simulation study showed that the P-RMSD values of ϒ-sitosterol-guanylate cyclase were stable between 75.00 to 100.00 ns at the binding pocket. The findings demonstrate that ripened sour tamarind extract may be supplemented to be a more prospective antihypertensive target or nutraceuticals affirming through advanced preclinical and clinical studies.

Ocean surface waves have been utilized as fundamental information in various fields of oceanic research. In this paper, we suggest a simulation and modelling technique for generating an ocean surface wave using an inverse Fast Fourier Transform (iFFT), and we subsequently verify the wave’s accuracy. The conventional method, linear superposition, requires recursive calculation because of the double summation and the time variable; to circumvent this issue, the new algorithm is presented. The Joint North Sea Wave Project (JONSWAP) spectrum is utilized for the ocean surface wave simulation example, and the parameters are the significant wave height (HS) and the zero-crossing wave period (TZ). A coordinate transform for the wavenumber domain was used to apply the inverse FFT algorithm. To verify the accuracy of the simulation result, the relative error between the input condition and the analysis result was calculated. The result for TZ is below 4% relative error, and the maximum relative error for HS is 7%. To avoid the Nyquist frequency for wave-field analysis and simulation, the minimum grid size was calculated by twice applying the maximum wavenumber.

In this note, I analyze the data generated by M. Fodje's (2013) simulation programs "epr-simple" and "epr-clocked". They were written in Python and published on Github. Inspection of the program descriptions shows that they make use of the detection-loophole and the coincidence-loophole respectively. I evaluate them with appropriate modified Bell-CHSH type inequalities: the Larsson detection-loophole adjusted CHSH, and the Larsson-Gill coincidence-loophole adjusted CHSH (NB: its correctness is conjecture, we do not have proof). The experimental efficiencies turn out to be approximately eta = 81% (close to optimal) and gamma = 55% (far from optimal). The observed values of CHSH are, as they should be, within the appropriately adjusted bounds. Fodjes' detection-loophole model turns out to be very, very close to Pearle's famous 1970 model, so the efficiency is close to optimal. The model has the same defect as Pearle's: the joint detection rates exhibit signaling. Fodje's coincidence-loophole model is actually a clever modification of his detection-loophole model. Because of this, however, it cannot lead to optimal efficiency.

Certain configurations of liquefied natural gas refueling stations exhibit a deficiency in managing boil-off gas. Furthermore, the ill-conceived linkage between the submersible pump and the gas storage tank pipeline leads to impeded natural gas transmission. This study employs Computational Fluid Dynamics (CFD) methodology to scrutinize the hydrodynamic attributes of T-type tee and dovetail tee configurations implemented in the pipeline design connecting the submersible pump and storage tank in an LNG filling station across diverse operational scenarios. The T-type tee induces detachment of the primary flow from the inner wall due to inertial forces, resulting in vortex formation and heightened resistance, accompanied by increased energy dissipation. Conversely, the dovetail tee, with its circular wall transition, mitigates vortex generation, reducing the separation zone and consequently minimizing resistance and energy loss. The maximum static differential pressure between the inlet and outlet of the dovetail tee is diminished by 52.52% when compared to that of the T-type tee. In the most noteworthy scenarios, the local resistance coefficient of the dovetail tee is even less than 1/10 of that of the T-type tee. In practical engineering applications, the incorporation of dovetail tees leads to a 17.58% reduction in the required elevation difference. This results in a more uniform flow field and enhanced stability in both flow rate and pressure. Such improvements contribute to heightened engineering efficiency and environmental sustainability, particularly evident in the design of LNG filling stations.

The powder bed fusion (PBF) metal additive manufacturing (AM) method uses an energy source like a laser to melt the metal powders. The laser can locally melt the metal powders and creates a solid structure as it moves. The complexity of the heat distribution in laser PBF metal AM is one of the main features that need to be accurately addressed and understood to design and manage an optimized printing process. In this research, the dependency of local thermal rates and gradients on print after solidification (in the heat-affected zone) was numerically simulated and studied to provide information for designing the print process. The simulation results were validated by independent experimental results. The simulation shows that the local thermal rates are higher at higher laser power and scan speed. Also, the local thermal gradients increase if the laser power increases. The effect of scan speed on the thermal gradients is opposite during heating versus cooling times. Increasing the scan speed increases the local thermal gradients in the cooling times and decreases the local thermal gradients during the heating. In addition, these simulation results could be used in artificial intelligence (AI) and machine learning for developing digital additive manufacturing.

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

Subsurface temperature is the key parameter in geothermal exploration. An accurate estimation of the reservoir temperature is of high importance and usually done either by interpolation of borehole temperature measurement data or numerical modeling. However, temperature measurements at depths which are of interest for deep geothermal applications (usually deeper than 2 km) are generally sparse. A pure interpolation of such sparse data always involves large uncertainties and usually neglects knowledge of the 3D reservoir geometry or the rock and reservoir properties governing the heat transport. Classical numerical modeling approaches at regional scale usually only include conductive heat transport and do not reflect thermal anomalies along faults created by convective transport. These thermal anomalies however are usually the target of geothermal exploitation. Kriging with trend does allow including secondary data to improve the interpolation of the primary one. Using this approach temperature measurements of depths larger than 1,000 m of the federal state of Hessen/Germany have been interpolated in 3D. A 3D numerical conductive temperature model was used as secondary information. This way the interpolation result reflects thermal anomalies detected by direct temperature measurements as well as the geological structure. This results in a considerable quality increase of the subsurface temperature estimation.

In this study, we develop a stochastic partial differential equation (SPDE) model to assess the regional severity of temperature increases due to global warming. The model incorporates various stochastic processes to account for uncertainties such as solar explosions, economic upheavals, rising emissions, and irreversible changes in the climate system. Our base temperature model, influenced by latitude, is enhanced by these random factors to simulate realistic and diverse outcomes of climate change. Numerical simulations on a global grid provide a heatmap representing potential temperature increase severity across different regions. The results highlight the significant impact on tropical and polar regions, demonstrating the utility of SPDE models in understanding and predicting the complex dynamics of climate change. This approach offers valuable insights for policymakers and researchers aiming to develop adaptive strategies for mitigating the adverse effects of global warming.

The use of energy resources is essential to the survival and development of human civilization. However, inefficient energy use not only has an impact on the economy, but it also has a significant impact on climate change. Studying and analyzing the factors that increase energy consumption is crucial for improving energy management. The aim of this study is to analyze the factors influencing energy savings in a building taken as a study model under two different climatic conditions. Measurements of the building's energy performance were carried out using the EnergyPlus calculation engine, which enabled us to analyze the strengths of building parameters such as the number of lights, the equipment used, occupancy, temperature and the relationships between the factors influenced. EnergyPlus is an important software tool in the field of building energy efficiency, enabling the precise simulation of building energy consumption. Simulation scenarios based on actual consumption data carried out by researchers have made it possible to develop energy management strategies in buildings in order to optimize energy efficiency, and in particular to promote the thermal performance of these buildings. In the present work, EnergyPlus building energy simulation software is used to simulate the energy consumption of the building of the Faculty of Electrical Engineering at the "Gheorghe Asachi" Technical University in Iasi, in order to determine the factors influencing building energy consumption. It has been shown that occupancy and heating and cooling systems are the key factors influencing energy consumption in buildings. The advantage of this study is that it can be used to model the most influential factors in predicting future energy consumption in buildings.

Community centers play a crucial role in urban environments, providing physical and educational services to their surrounding communities, particularly for students. Among the many benefits for students are enhanced academic outcomes, improvement of behavioral problems, and increased school attendance. Such centers are also particularly vital for low-income and racial minority students as they are pivotal in giving them outside-of-school learning opportunities. However, determinants influencing attendance at community centers remain largely unexplored. Using census data, Boston Centers for Youth and Families (BCYF) attendance data, and specific center attributes, we develop human mobility gravitational models to predict attendance across the BCYF network. Using those models, we investigate the potential effects on general and student attendance by changing center attributes, such as facilities and operating hours. We also research the impact of changing the walking accessibility to those centers on their respective attendance patterns. After the analysis, we find that the most cost-effective policy to increase BCYF attendance is changing each center’s educational and recreational offerings far beyond any accessibility interventions. Our results provide insights into potential policy changes that could optimize the attendance and reach of BCYF Community Centers to underserved populations.

Motivated by the increasing need of optimised micro-devices for droplets production in medical and biological applications, this paper introduces an integrated approach for the study of the liquid-liquid droplet creation in flow-focusing micro cross-junctions. The micro-junction considered is characterised by a restriction of the channels cross-sections in the junction, which has the function of focusing the flow in the region of the droplet formation. The problem is studied numerically in OpenFOAM environment and validated by the comparison with experimental results obtained by high-speed camera images and micro-PIV measurements. The analysis of the forces acting on the dispersed phase during the droplet formation and the diameter of the droplets obtained numerically are considered for the development of a model of the droplet breakup under the squeezing regime. On the basis of energy balancing during the breakup, a relation between interfacial tension, the size of the cross-sections in the junction, and the time interval needed for droplet creation is obtained, which yields a novel correlation between the dimensionless length of the droplet and the dimensionless flow rate. This correlation is general and can be used to predict the droplet diameters produced by similar micro-junctions with different aspect ratios and extended to different geometries of flow-focusing micro-junctions.

Hydrogen storage in high pressure tanks can occur with different filling strategies. Many studies have been carried out on supplies with increasing pressure rates. However, hydrogen filling with constant mass inflow rate have not received the same attention. The present work aims to carry out numerical simulations to study hydrogen flow, in a type 3 tank of 70 MPa NWP and constant inflow rate condition. From the analysis of the results, it was possible to verify that there is a linear relationship between the increase in the total equilibrium temperature and the final hydrogen temperature. There is also a linear increasing relationship between Inlet and final temperatures when the equilibrium temperature is fixed. Considering fully adiabatic walls results in significant increases in temperature. The difference between total and static temperatures found in the Inlet tube decreases with the decrease in mass flow rate. The choice of a polymer for the tank lining led to higher temperatures when compared to an aluminum lining.

Swirling coal flames are encountered in swirl burners of boiler furnaces and cyclone coal combustors, and were studied using large-eddy simulation (LES) in reported references. However, the structures of swirling coal flames remain to be further studied. In the present paper LES of swirling coal flames is conducted for two combustors using two gas combustion models. The statistical simulation results are accessed by experiments and the instantaneous simulation results are used to analyze the specific features of flame structures of four cases. It is seen that different inlet flow conditions and different secondary-air flow rate lead to different flame structures

An atomic-scale approach is employed to simulate the formation of precipitates with different lattice misfits in the early stages of aging of supersaturated aluminum alloys. Simulation results reveal that the increase of lattice misfit could significantly promote the nucleation rate of precipitates, which results in a larger number and smaller size of the precipitates. The morphologies of the precipitates also vary with the degree of lattice misfit. Moreover, the higher the lattice misfit, the earlier the nucleation of the second phase occurs, which can substantially inhibit the movement of dislocations. The research on the lattice misfit of precipitation can provide theoretical guidance for the design of high-strength aluminum alloy.

Cellular automata (CA) models have emerged as a valuable tool in corrosion management. This manuscript provides an overview of the application of cellular automata models in corrosion research, highlighting their benefits and contributions to understanding the complex nature of corrosion processes. Cellular automata models offer a computational approach to simulate corrosion behavior at the microscale, capturing the intricate interactions between electrochemical reactions, material properties, and environmental factors generating a new vision of predictive maintenance. It discusses the key features of cellular automata, such as the grid-based representation of the material surface, the definition of state variables, and the rules governing cell-state transitions. The ability to model local interactions and emergent global behavior makes cellular automata particularly suitable for simulating corrosion processes. Finally, cellular automata models offer a powerful and versatile approach to studying corrosion processes, expanding models that can continue to enhance our understanding of corrosion and contribute to the development of effective corrosion prevention and control strategies.

The information society has transformed human life. Technology is almost everywhere, including health and education. For example, years ago, speech and language therapy students required a long time and high-cost equipment to develop healthcare of the auditory and vestibular systems competencies. The high cost of the equipment permitted its practical use only in classes, hindering students’ autonomy in developing those competencies. That situation was a real issue, even more in times of pandemic where online education was essential. This article describes SAEF, an open-source software simulator for autonomously developing procedural audiology therapy competencies, user acceptance, and the validity of experiments and results. SAEF delivers immediate feedback and performance results. Obtained results permit validating students’ and educators’ acceptability of SAEF in audiology therapy education. Obtained results invite authors to continue developing simulator software solutions in other health education contexts. SAEF was developed using open-source technology to facilitate its accessibility, classification, and sustainability.

A large amount of steel slag (SS) and CO2 are generated in the steelmaking process. The indirect CO2 capture using SS is a promising way for co-treatment of the wastes. Ammonium salt solution is widely used to extract Ca2+ from the SS since it is recyclable. Several works have focus on improving the carbonation rate by adjusting various parameters (e.g., temperature and pH). However, there is little detail information about the associating behaviors and interaction strength between the various ions in the ammonia solution during the carbonation process. In this work, the Ca2+–CO32-–NH4+–Cl-–H2O system was established by using Material studio software. The effects of temperature and concentration of CO32- on CaCO3 growth were explored at the atomic scale by calculating the binding energy, mean square displacement, and diffusion coefficient between particles. Furthermore, the microstructure, bonding characteristics, and occurrence behavior of each particle were studied though molecular dynamics simulation methods. The results showed that with the increase of temperature (20–80 ℃), the binding ability and diffusion coefficients of Ca2+ and CO32- increase in the system, which is beneficial to the formation of CaCO3 clusters. With the increase of the concentration of CO32- (15–25 vol.%), the binding ability and diffusion coefficient of Ca2+ and CO32- in the system are enhanced, which is beneficial to the formation of CaCO3 clusters.

Simulation-Based Medical Education that uses Virtual Patients has become increasingly important during the COVID-19 pandemic. With the need for social distancing and minimizing contact, medical simulation technology has provided a valuable tool for healthcare professionals to practice and improve their skills without the need for face-to-face interactions. MedSIM is a medically accurate simulation platform with Virtual Patients designed for undergraduate medical education. Our study involved two groups of students. The PreCOVID group, before the pandemic, underwent conventional teaching methods. The COVID group, during the pandemic, had students exposed to conventions skills taught earlier and were taught again with MedSIM. Students indicated high satisfaction with the clinical skills demonstrated by the Virtual Patients. More than half agreed that MedSIM had enabled them to perform all kinds of procedures on patients (PreCOVID group 68.8%, COVID group 71.3%), showed cues and consequences much like those in natural clinical environments (PreCOVID 68.4%, COVID 71.3%). Also, MedSIM allowed them to have a repetitive practice that helps in critical skills transfer to actual patients (PreCOVID 72.7%, COVID 74.7%). MedSIM met the expectations of most of the students. Students from both groups rated the online performance of the MedSIM simulator as "Very good." Analysis from a customized word cloud indicated that most students found MedSIM to be good and of educational value. MedSIM platform enhances healthcare professionals' skills and knowledge, leading to better patient outcomes and increased access to healthcare, supporting SDG 3 (Good Health and Well-being). It also provides a safe and controlled environment for healthcare professionals to learn and practice essential skills, supporting SDG 4 (Quality Education).

A novel physiologically based algorithm (PBA) for fast CFD computation of Flow Fractional Reserve (FFR) in Coronary Artery Trees (CATs) is proposed and developed, which, unlike traditional methods, is based on the extension of the Murray’s law for blood vessels at the outlets and extra inlet conditions prescribed alternatively and iteratively. The PBA is then implemented in both SimVascular and Ansys CFD for testing and validation. For validation purpose, 3D models of CATs are built by using their CT images and computational meshes generated for mesh convergence study. Results obtained are then compared with Invasive Coronary Angiographic (ICA) data for validation and evaluation of its accuracy and computational efficiency. It is found that discrepancies between experimental and calculated values of pressure and flow rate at the inlet were less than 0.1% at the end of the 10^th round of iteration or less. Further validation shows that the difference between estimated and experimental FFR agree with each other with a maximum difference of 1.62% after convergence is achieved. The PBA is found to be a robust patient-specific and physiologically sound method that can be a good alternative to the existing Lumped Parameter Model (LPM) which is based on empirical scaling correlations using limited population-averaged data and requires nonlinear iterative computation for convergence.

Porcelain tile manufacturing is an energy-intensive industry that is in dire need of increasing productivity, minimizing costs, and reducing CO2 emissions, while keeping the product quality intact to remain competitive in today’s environment. In this contribution, alternative processing parameters for the porcelain tile production sequence were proposed based on simulation-based process optimization. Flowsheet simulations in the Dyssol framework were used to study the impact of milling and firing process parameters on the electrical and thermal energy consumption, final product quality, and productivity of the entire processing sequence. For this, a new model of gas flow consumption in the sintering stage was proposed and implemented. During optimization, the primary condition was to maintain the product quality by keeping the final open porosity of the tile within the specified industrial range. The proposed simulation methodology proved to be effective in predicting the influence of processing parameters on the intermediate and final products of the manufacturing sequence, as well as in estimating production costs for the Brazilian and Spanish economic conditions. This approach has shown great potential to promote digitalization and establish digital twins in ceramic tile manufacturing for further in-line process control.

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD-19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. For non-uniform distribution, we found that: (i) the subsets of the MFD by the loop detector position help estimate whether the loop detector MFD will have a bias; (ii) if the detectors in the network are positioned more downstream with a larger variation, the loop detector MFD is more likely to have a discrepancy in position subsets of the MFD; (iii) a lower ratio of link length to green signal time elevates the MFD bias as well. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.

Locomotor impairment is a high-prevalent and significant source of disability and significantly impacts a large population’s quality of life. Despite decades of research in human locomotion, the challenges of simulating human movement to study the features of musculoskeletal drivers and clinical conditions remain. Most recent efforts in utilizing reinforcement learning (RL) techniques are promising to simulate human locomotion and reveal musculoskeletal drives. However, these simulations often failed to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR), and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Intertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. Also, we adapted the reward function by leveraging previous research in walking simulation for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means the simulated human locomotion was more realistic. Also, as this bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence is faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quicker and in a broader range of environments with a better simulation performance.

ASEAN SME has a role as the regional socioeconomic stabilizer. This particular role is inseparable from endogenous multi-sector collaboration. Although, Indonesian SMEs were struggled in adopting Industry 4.0 correspond to digital infrastructure and digital literacy problems. This study evaluates Indonesian SME collaboration dynamics with government and technology startup (TS). The integration of agent-based model and causal loop simulation were employed to assess the TS collaboration impact on SME Industry 4.0 adoption and SME competition with larger competitors. The simulation results imply the SME collaboration with TS can lead to early adoption of Industry 4.0 which balances the business competition environment. The model also shows rising the government aid exponentially can help the SME to late adoption of Industry 4.0 which unable to sustain the SME in business competition. Thus, the developed integrative simulation model is a state-action planning model with each state result bounded to the previous state result that determined by initial input parameters. Conclusively, the model can be used as a resiliency planner for SME Industry 4.0 adoption.

A single fracture is the basic unit of fracture medium, and the roughness of fracture wall surface is an important factor influencing hydraulic characteristics of the flow in bedrock fracture. However, effects of the shape and density of roughness elements (various bulges/pits on rough fracture wall surfaces) on water flow in a single rough fracture have not been thoroughly discovered. Thus the water flow in single fracture with different shapes and densities of roughness elements was simulated by using Fluent software in this study. The results show that in wider fractures the flow rate mainly depends on fracture aperture, while in narrow and close fracture medium the surface roughness of fracture wall is the main factor of head loss of seepage; there is a negative power exponential relation between the hydraulic gradient index and the average fracture aperture, i.e. with the increase of fracture aperture, the relative roughness of fracture and the influence weight of hydraulic gradient both decrease; and in symmetrical-uncoupled fractures there is a super-cubic relation between the discharge per unit width and average aperture. Above results would help to deepen the understanding of rough fracture seepage.

In this paper, a method of direct spinning is proposed for direct spinning of heterogeneous spun wire-blown holes standard parts and the motherboard mounted on the spin board assemblies. The standardization of the spinneret hole was conducive to improving the machining accuracy and efficiency of the pores, and the spinneret holes could be replaced in time when a hole in the spinneret fails. Moreover; on the other hand, according to the needs of the process of spinning, the spinnerets with various cross-sections were combined and installed on the same motherboard for spinning, and the spinning obtained different bionic new functional products. Based on the results, a finite element model of the standard part of the spinneret hole was developed, and the spinnability of the combinable spinnable spinneret board was verified by simulating the melt flow in the spinneret channel through POLYFLOW software. Further, by the processing of the spinneret, the motherboard was installed into a combined spinneret, and the spinneret assembly was installed on the spinning machine for the experiments. Furthermore, the tow section was observed using a microscope, and the results showed the feasibility of the proposed method.

Fibre reinforced concrete (FRC) has become an alternative for structural applications due its outstanding mechanical properties. The appearance of new types of fibres and the fibre cocktails that can be configured mixing them has created FRC that clearly exceed the minimum mechanical properties required in the standards. Consequently, in order to take full advantage of the contribution of the fibres in construction projects, it is of great interest to have constitutive models that simulate the behaviour of the materials. This study aimed to simulate the fracture behaviour of five types of FRC, three with steel hooked fibres, one with a combination of two types of steel fibres and one with a combination of polyolefin fibres and two types of steel fibres, by means of an inverse analysis based on the cohesive crack approach. The results of the numerical simulations defined the softening functions of each FRC formulation and have pointed out the synergies that are created through use of fibre cocktails. The information obtained might suppose a remarkable advance for designers using high-performance FRC in structural elements.

In this paper, we use three different experimental methods (particle method, grid method and hybrid method) to model and simulate the smoke from the perspective of fluid dynamics. Through the comparison of different methods, we conclude: The particle method can avoid the numerical dissipation problem caused by grid calculation, but it also brings problems such as the distortion of the trajectory of the example. The grid method is accurate in calculation, but it is prone to numerical dissipation and loss of details. Finally, we choose the hybrid method to store the vorticity in the form of particles in vortex particles, avoiding the numerical dissipation problem caused by the use of grids, and including rich turbulence, which perfectly shows the simulation effect of smoke.

Quantum computers are invaluable tools to explore the properties of complex quantum systems. We show that dynamical localization of the quantum sawtooth map, a highly sensitive quantum coherent phenomenon, can be simulated on actual, small-scale quantum processors. Our results demonstrate that quantum computing of dynamical localization may become a convenient tool for evaluating advances in quantum hardware performances.

The designs of two large arenas of different epochs are discussed, which are the most famous antique amphitheater the Roman Colosseum (Italy) and the modern Gazprom Arena stadium (St. Petersburg, Russia). 3D-computer models of the arenas have been built and evacuation of people has been simulated in the Sigma FS software (Russia). The arena designs are analyzed in terms of organizing pedestrian evacuation, a comparative analysis is made, and the specific characteristics affecting the evacuation time are established.

This code provides computational facilities to simulate current versus time during the charging of Li-ion cells at desire constant voltage by considering multiscale physical phenomena. This code only considers a powder of active materials (at microscale or nanoscale) and a small part of electrolyte around it as a half cell. Then it is extended to a complete cell by applying correct boundary conditions. This code is very useful by modifying code parameters to understand the effect of the complex shape of active materials powder (surface area and powder size), kind of electrolyte, and the applied voltages on the charging response of Li-ion cell. As a summary, a microscale approach to the design of Li-ion cells has been provided via this code.

Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framework based on the OpenAI Gym framework, and demonstrate its use on a simple hospital bed capacity model. We built the Deep RL agents using PyTorch, and the Hospital Simulation using SimPy. Results: We demonstrate example models using a Double Deep Q Network or a Duelling Double Deep Q Network as the Deep RL agent. Conclusion: SimPy may be used to create Health System Simulations that are compatible with agents developed and tested on OpenAI Gym environments. GitHub repository of code: https://github.com/MichaelAllen1966/learninghospital

This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF considering all the potential changes in the boundary conditions of a water body. A two-dimensional contaminant transport simulation model (RMA4) was implemented to estimate the distribution patterns of the total dissolved solids (TDS) within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 model to determine the minimum SF of the monitoring stations with an accuracy level of detectable change in TDS concentration (ALC) of 5%, 10% and 15%. The proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. Additionally, increasing the ALC to 10% and 15% increase the minimum SF for some MSs by approximately 18% and 21%, respectively. However, the proposed methodology includes all the potential values and cases of boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO₂ emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

The study aims to analyze the performance criteria of the GR4J model to reproduce high water flows in the Ouémé watershed at Bonou's outlet which has been vulnerable to climate change in recent decades. The methodology focused on the use of daily climatological and hydrometric data extracted from files of National Directorate of Meteorology, and General Directorate of Water; they were supplemented by those of SIEREM/HSM dataset over the period 1961-2015. The rainfall was regionalized using Thiessen method. The performance of the GR4J model was assessed with NSE, RMSE and KGE criteria. The results indicate that the study area is marked by rainfall variabilities and detection of two breakpoints (1968 and 1987) which divide the series into three sub-periods; these discontinuities have repercussions on the streamflow. It's found that GR4J model overestimates the streamflow during the low water period and underestimates them in high water. However, the efficiency and performance criteria NSE, RMSE and KGE calculated on high water flow rates are better in calibration than in validation. The KGE values are range between 83-85% in calibration and 56-68% during validation, which gives to GR4J model the efficiency and performance to reproduce high flow rates in the study area

Laser shock peening is a process which can reduce stress corrosion cracking and improve fatigue life by forming compressive residual stress on the surface of the material. In a computational FE simulation of laser shock peening, during applying the pressure load generated by the laser pulse to the surface of simulation geometry, the peening is simulated by explicit analysis and then convert to implicit analysis to dissipate the dynamic energy remaining in the geometry. In this study, static damping is applied to dissipate residual dynamic energy without converting it into an implicit analysis. The compressive residual stress distribution is compared between the simulation results for the stainless steel 304 material and the same material subjected to actual laser shock peening. The laser shock peening parameters were 4.2J laser pulse energy, 50% overlap of 3mm diameter of the laser beam and water as a confinement layer. As a result, the compressive residual stress from the surface to the depth direction is similar to both the simulation and the experimental result measured by the hole drilling method.

With data for SARS-CoV-2 and with many countries entering the second wave of contagion required the improvement of the forecasting model, structuring its model to forecast the peak of the first and second contagion wave in Brazil. The Model Estimation for SARS-CoV-2 Peak Contagion (MESPC) was structured, capable of estimating the peak of contagion for SARS-CoV-2 in the first and second waves, as the main objective of this work. Using the MESPC model, it was possible to estimate, with a certain reliability degree, the peak of contagion for the first and second waves in Brazil, with one day difference from the real to the forecast. It is possible to use MESPC to forecast the peak of contagion for several regions, provided that the necessary structure and calibration are respected.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

The mechanical behavior, microstructures as well as the crystallographic textures of the Ti57-Nb43 alloy were investigated on cylindrical specimens compressed at high temperatures, in the range of 700-1000 °C, and strain rates between 0.001 and 1.0 s-1. Hardening, followed by softening behaviours were observed as a function of strain, due to the occurrence of dynamic recrystallization/recovery in hot deformation. The modified five-parameter Voce type equation described well the stress-strain curves, but for the present alloy, it was also possible with only four parameters. A new two-variables polynomial function was employed on the four parameters that described well the flow curves as a direct function of temperature and strain rate. It permitted to reduce the number of parameters and had the predictive capacity for the flow stress at any temperature, strain, and strain rate in the investigated range. The crystallographic textures were similar at all temperatures with an increase of intensity from 900 °C. The textures could be characterized by a double <100> and <111> fiber and a unique component of (001)<110>, the latter inherited from the initial hot rolling texture. Viscoplastic polycrystal self-consistent deformation modeling reproduced the measured textures showing that dynamic recrystallization did not alter the development of the deformation texture, only increased it intensity.

Among other fossil fuels, natural gas is a major energy source. It is generally created saturated with water vapor. Dehydration of natural gas is vital in the gas business to remove water vapor from the gas supply, which may cause hydrate development in pipes. Gas dehydration employs Tri ethylene glycol (TEG) to remove water vapor from natural gas flow. The wet gas is dehydrated with lean glycol in an absorber, and the rich glycol is recovered and reused. This work investigates the use of dry natural gas instead of nitrogen in the glycol dehydration re-generator, and compares the results using HYSYS modeling and simulation. The two techniques were compared for capital and utility expenses, while maintaining the identical glycol purity criteria. The wet gas from the stripping mechanisms may also be utilized to run steam pumps and compressors or recycled. The model is based on the real mechanism flow diagram. Finally, the findings of this model might be used to design a new heat and material balance for the plant.

(1) Background: The imaging energy range of a typical Compton camera is limited due to the fact that scattered gamma photons are seldom fully absorbed when the incident energies are above 3 MeV. Further improving the upper energy limit of gamma-ray imaging has important application significance in active interrogation of special nuclear materials and chemical warfare agents, as well as range verification of proton therapy; (2) Methods: To realize gamma-ray imag-ing in a wide energy range of 0.3~7 MeV, a principle prototype, named a portable three-layer Compton camera, was developed using the scintillation detector that consist of an silicon photo-multiplier array coupled with a Gd3Al2Ga3O12:Ce pixelated scintillator array. Implemented in a list-mode maximum likelihood expectation maximization algorithm, a far-field energy-domain imaging method based on two-interaction and three-interaction events was applied to estimate the initial energy and spatial distribution of gamma-ray sources. The simulation model of detec-tors was established based on Monte Carlo simulation toolkit Geant4. The reconstructed images of a 133Ba, a 137Cs and a 60Co point-like sources were successfully obtained with our prototype in laboratory tests and compared with simulation studies; (3) Results: The proportion of effective imaging events accounted for about 2%, which made our prototype realize reconstructing the distribution of a 0.05 μSv/h 137Cs source in 10 seconds. The angular resolution for resolving two 137Cs point-like sources was 15°. Additional simulated imaging of the 6.13 MeV gamma-rays from 14.1 MeV neutron scattering with water preliminarily demonstrated the imaging capability for high incident energy; (4) Conclusions: We concluded that the prototype had good imaging per-formance in a wide energy range (0.3~7 MeV), which showed potential in several MeV gam-ma-ray imaging applications.

The positional deviation of the Automatic Emergency Call System (AECS) under collision conditions brings difficulties for post-rescue operations. Currently, there is a lack of analysis on the system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on the data deviation of the simulated GNSS system. We also propose environmental limits applicable to comprehensive AECS tests, which can objec-tively evaluate the reliability of AECS and provide data support for AECS bench test. Through the satellite signal-based semi-physical simulation, AECS is subjected to bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibra-tion and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the AECS test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature dif-ference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of satellite signal simulation, nec-essary environmental conditions for AECS test are obtained, including the temperature consistent with the maximum operating temperature of the vector generator and the vibration Power Spectral Density (PSD) lower than 1.2gRMS.

It has been known that the performance of the High-Pressure Grinding Rolls (HPGR) varies as a function of method used to confine laterally the rolls, their diameter/length (aspect) ratio as well as their condition, if new or worn. However, quantifying these effects through direct experimentation in machines with reasonably large dimensions is not straightforward given the challenge, among others, of guaranteeing that the feed material remains unchanged. The present work couples the discrete element method (DEM) to multibody dynamics (MBD) and a novel particle replacement model (PRM) to simulate the performance of pilot-scale HPGRs grinding pellet feed. It shows that rotating side plates, in particular when fitted with studs, allow reaching more uniform forces along the bed, which also translates in a more constant product size along the rolls as well as higher throughput. It also shows that the edge effect is relatively constant with roll length, leading to substantially larger proportional edge regions for high-aspect ratio rolls. On the other hand, the product from the center region of such rolls was found to be finer when pressed at identical specific forces. Finally, rolls were found to have higher throughput, but generate a coarser product when worn following the commonly observed trapezoidal profile. The approach used in industry to compensate for roller wear by increasing the specific force and roll speed has then been demonstrated to be effective to maintain and potentially even increase product fineness and throughput, as long as the minimum safety gap is not reached.

This paper presents results from numerical and experimental investigation on Charpy tests in order to point out failure mechanisms and to evaluate new polymeric blends PP+PA6+EPDM. Charpy tests were done for initial velocity of the impactor of 0.96 m/s and its mass of 3.219 kg and these data were also introduced in the finite element model. The proposed model take into account the system of four balls, including support and the ring of fixing the three balls and it has a finer discretization of the impact area to highlight the mechanisms of failure and their development in time. The constitutive models for four materials (polypropylene with 1% Kritilen, two blends PP+PA6+EPDM and a blend PA6+EPDM) were derived from tensile tests. Running simulations for each constitutive model of material makes possible to differentiate the destruction mechanisms according to the material introduced in the simulation, including the initiation and the development of the crack(s), based on equivalent plastic strain at break (EPS) for each material. The validation of the model and the simulation results was done qualitatively, analysing the shape of broken surfaces and comparing them to SEM images and quantitatively by comparing the impact duration, energy absorbed by the sample, the value of maximum force during impact. The duration of the destruction of the specimen is longer than the actual one, explainable by the fact that the material model does not take into account the influence of the material deformation speed in Charpy test, the model being designed with the help of tests done at 0.016 m/s (1000 mm/min) (maximum strain rate for the tensile tests). Experimental results are encouraging for recommending the blends 20% PP+42% PA6+28% EPDM and 60% PA6+ 40%EPDM as materials for impact protection at low velocity (1m/s). Simulation results are closer to the experimental ones for the more brittle tested materials (with less content of PA6 and EPDM) and more distanced for the more ductile materials (with higher content of PA6 and EPDM).

Curcumin is one of the bioactive metabolites of turmeric (Curcuma longa), known for its pleiotropic pharmacological actions, including antioxidant and anti-inflammation, anticholinesterase, immunomodulation, and neuroprotection. Substantial evidence suggests the therapeutic benefits of curcumin against neurodegenerative disorders, including Alzheimer’s disease (AD), acting on a diverse array of brain targets that make the molecular mechanisms complicated. System biology level-investigation could potentially present a comprehensive molecular mechanism to delineate the neuropharmacological action of curcumin. In this study, we used integrated system pharmacology and molecular simulation analysis to gain insights into the underlying mechanism of curcumin action against AD. Network pharmacology study identified curcumin-targeted potential cellular pathways such as phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling, neurotrophin signaling, toll-like receptor (TLR) signaling, and autophagy, and proteins such as tropomyosin receptor kinase B (TrkB), liver X-receptor-beta (LXR-β), estrogen receptor-β (ER-β), mammalian target of rapamycin (mTOR), TLR-2, N-methyl-D-acetate receptor subunit 2B (GluN2B), β-secretase and glycogen synthase kinase-3β (GSK-3β), which are intimately associated with neuronal growth and survival, immune response, and inflammation. Moreover, the molecular modeling further verified that curcumin showed a significant binding affinity to mTOR, TrkB, LXR-β, TLR-2, ER-β, GluN2B, β-secretase, and GSK-3β, which are the crucial regulators of molecular and cellular processes associated with AD. Together, the present system pharmacology and in silico findings demonstrate that curcumin might play a significant role in modulating AD-pathobiology, supporting its therapeutic application for the prevention and treatment of AD.

Pore fluid pressure is very important to the generation, migration and accumulation of petroleum. The previous studies indicate that the Chezhen sag region is typically characterized by pore fluid overpressure (the difference between pore fluid pressure and hydrostatic pressure). To date, the formation mechanism of pore overpressure and the accumulation regularity of “upper source-lower reservoir” type in this region remains unknown. In order to investigate these problems, a 2-D finite element model is established with regards to the fluid-solid coupling in Chegu 25 block of Chezhen depression, fully considering the existing seismic, logging data and regional tectonic stress environment. Meanwhile, we calculate the abnormal overpressure generated at the source rock during hydrocarbon generation and the process of hydrocarbon migration and accumulation along the faults, analyzing the dynamic conditions of hydrocarbon downward accumulation. The results show that overpressure can accelerate migration of hydrocarbon and improve the efficiency of hydrocarbon accumulation. However, when the overpressure is too large, tensile fractures and shear fractures may occur, resulting in hydrocarbon dissipation, and changing the results of oil and gas accumulation. The overpressure at the source rock is mainly caused by hydrocarbon generation, while the overpressure at the reservoir is primarily created by unbalanced compaction. As the dominant channel of hydrocarbon migration, overpressure will change the direction and path of hydrocarbon migration in the fault. Therefore, the “upper source-lower reservoir” hydrocarbon accumulation model is strongly explained by the high permeability of faults and the presence overpressure. In summary, the simulated overpressure results are in good agreement with the mud weight equivalent overpressure and the drill stem tests (DSTs).

Eruca sativa is a commonly used edible plant in Italian cuisine. E. sativa 70% ethanol extract (ES) was fractionated with five organic solvents. Ethyl acetate fraction(EEA) had the highest antioxidant activity, which was correlated with the total polyphenol and flavonoid content. ES and EEA acted as PPAR-α ligands by PPAR-α competitive binding assay. EEA significantly increased cornified envelope formation as a keratinocyte terminal differentiation marker in HaCaT cells. Further, it significantly reduced nitric oxide and pro-inflammatory cytokines in lipopolysaccharide-stimulated RAW 264.7 cells. The main flavonol forms detected in high amounts from EEA are mono-, and di-glycoside of each aglycone. Flavonol mono-glycosides were shown to be a potent PPAR-α ligand using molecular docking simulation and showed the inhibition of nitric oxide. These results suggest that E. sativa is suitable for use in improving skin barrier function and inflammation in skin disorders, such as atopic dermatitis.

This work aims at improving surface integrity and LCF life of machined Inconel 718. The correlation between the LCF life of Inconel 718 and various states of machined surface integrity is explored. In this paper, the surface integrity of Inconel 718 specimens is enhanced by low plasticity burnishing (LPB). The LCF life with different surface integrity specimens is predicted by micro-macro finite element method (FEM). Firstly, the LCF specimens with different surface integrity are machined by turning (turned specimen), polishing (matrix specimen) and LPB process (LPBed specimen). Secondly, the LCF experiment is carried out to reveal the effect of surface integrity on LCF life. Finally, the LCF micro-macro FEM model is proposed to predict the LCF of machined Inconel 718 specimens. The representative volume element (RVE) model is established based on the measured surface integrity and microstructure of Inconel 718 specimens. The effect of surface integrity on LCF life is transformed to equivalent load. The micro-macro FEM model combined with Tanaka-Mura dislocation crack initiation theory and extended finite element method (XFEM) is applied to predict the LCF life the machined specimens. The study results show that the LCF life of LPBed specimens can be improved by 90.5% and 36.1% compared with that of turned specimens and polished matrix specimens, respectively. The errors between FEM prediction results and experimental results are 13.1%, 9.2% and 12.2%, respectively. The proposed micro-macro FEM model could be utilized to predict the LCF life of Inconel 718 with different surface integrity, and further to apply the LCF life prediction in industry.

The concerning rise in emergence and prevalence of resistant strains to drugs of M. tuberculosis has ,the prompted researchers to look for new and effective treatments. With this motive, biscoumarins were identified as the lead molecules on a whole-cell-based screening of several less explored low molecular weight bioactive compounds against M. tuberculosis strains. Among the screened biscoumarins, the highest dock score derivatives were synthesized (a-h) using a programmable microwave synthesizer for better yields and reaction control. The synthesized derivatives were evaluated against H37Rv, H37Ra, M. smegmatis, an MDR surrogate model, and other bacterial strains for the structure-activity response. Assessment of the synthesized library against mycobacterial strains led to the identification of compounds (f and d) as lead anti-tuberculosis agents. Compounds (f and d) exhibited less toxicity against human cell lines. At the same time, it displayed enjoyable activity wherein MIC concentrations were observed to be 16- and 32 µg/mL against the susceptible H37Rv, and H37Ra strains of M. tuberculosis and MIC value of 128 µg/mL for M. smegmatis, respectively. For mechanistic insights and identification of drug binding targets, molecular docking and dynamic simulations were employed for a panel of 16 mycobacterial enzymes essential for mycobacterial growth and survival. These in silico studies revealed the DprE1 enzyme as a druggable target for the anti-tuberculosis activity of the selected biscoumarins derivatives. Further investigation is underway in our laboratory, leading to its development as an anti-tuberculosis drug (animal model studies).

Volcanism powered by tidal forces inside celestial bodies can provide enough energy to keep important solvents for living systems in the liquid phase. Moreover, tidal forces and their environmental consequences may strongly influence habitability of planets and other celestial bodies and may result in special forms of live and living conditions. A prerequisite to calculate such tidal interactions and consequences is depending on simulations for tidal accelerations in a multi-body system. Unfortunately, from measurements in many extrasolar planetary systems only few physical and orbital parameters are well enough known for investigated celestial bodies. For calculating tidal acceleration vectors under missing most orbital parameter exactly, a simulation method is developed that is only based on a few basic parameters, easily measurable even in extrasolar planetary systems. Such a method as being presented here, allows finding a relation between the tidal acceleration vectors and potential heating inside celestial objects. Using values and results of our model approach to our solar system as a “gold standard” for feasibility allowed us to classify this heating in relation to different forms of volcanism. This “gold standard” approach gave us a classification measure for the relevance of tidal heating in other extrasolar systems with a reduced availability of exact physical parameters. We would help to estimate conditions for the identification of potential candidates for further sophisticated investigations by more complex established methods like viscoelastic multi-body theories. As a first example, we applied the procedures developed here to the extrasolar planetary system TRAPPIST-1 as an example to check our working hypothesis.

The decreasing width, increasing aspect ratio RDL presents significant challenges to design for reliability (DFR) of advanced package. Therefore, this paper proposes a ML based RDL modeling and simulation method. In the method, RDL is divided into blocks and subdivided into pixels of metal percentage, and the RDL is digitalized as tensors. Then, an ANN-based surrogate model is built and trained by a subset of tensors to predict the equivalent material properties of each block. Lastly, all blocks are transformed into elements for simulations. For validation, line bending simulations were conducted on an RDL, with the reaction force as accuracy indicator. The results show that neglecting layout impact causes critical errors as substrate thins. By the method, the reaction force error is 2.81% and the layout impact can accurately be considered with 200×200 elements. For application, the TCT maximum temperature state simulation was conducted on a CPU chip. The simulation indicates that for advanced package the maximum stress more likely occurs in RDL rather than bumps, both RDL and bumps are critical impacted by layouts, and RDL stress is also impacted by vias/bumps. The proposed method precisely concerns layout impacts with little resources, presents an opportunity of efficiency improvement.

During the recent decades, global warming by greenhouse gas evolution has attracted worldwide attention and evermore strict regulations thereon have become institutionalized as international policies. In the process, more environment-friendly power generation technologies have been developed utilizing fossil fuels with a view to timely commercialization. As one of such “clean coal” technology, Integrated Gasification Fuel Cell system is a promising power generation means and COS hydrolysis reactor is installed downstream of coal syngas to remove acidic gas constituents such as H2S and COS. The most significant design parameters affecting performance of the COS hydrolysis reactor were selected to be GHSV, (catalytic) reaction temperature, and length ratio and numerical modeling was performed considering heat and fluid flow transfer as well as chemical reaction kinetics. Effect of the selected design parameters on the variation of conversion rate and reactant gas mixture concentration were comprehensively investigated to predict performance of COS hydrolysis reactor. Stochastic modeling of reactor performance was finally performed using Monte Carlo simulation and linear regression fitting.

This study investigated riverbank protection strategies along the dynamic Jamuna River in Bangladesh, a braided river prone to erosion and sedimentation. It reviews past countermeasures and emphasizes the effectiveness of groyne-type structures in redirecting flow and preventing erosion. Notably, the left bank exhibited greater stability than the right bank, emphasizing the need for effective groyne installations. A systematic methodology utilizing numerical modeling using International River Interface Cooperation (iRIC Nays2DH) ensured accuracy in assessing morphological impacts. This study presents novel countermeasures incorporating groyne installations in Zone 1 (from Singrabari to Meghai) and Zone 2 (from Pachthakuri to Subhagacga), accompanied by simulations for various flood conditions. Zone 2, identified as highly erosion-vulnerable, showed the greatest susceptibility. Simulations (Cases_0 1, 2, and 3) revealed groynes in Zone 1 alone ineffective, while Zone 2 installations successfully diverted oblique flow, reducing erosion risk. Although Case_3's combined approach was effective, it was uneconomical for Zone 1. Case_2, focusing on Zone 2 groynes, emerged as the optimal solution, redirecting flow and shaping sandbars to mitigate Jamuna River's riverbank erosion. It provides a practical and economical solution. Further research is recommended for groyne length and spacing variations. This study enhances Jamuna River protection, emphasizing groyne-type structures' importance and promoting a holistic understanding of morphological dynamics for future river management and effective countermeasures.

Our aim is to provide an insight of the procedures and the dynamics that lead the spread of contagious diseases through populations. Our simulation tool can increase our understanding on the spatial parameters which affect the diffusion of a virus. SIR models are based on the hypothesis that populations are “well mixed”. Our model constitutes an attempt to focus on the effects of the specific distribution of the initially infected individuals through the population and provide insights, considering the stochasticity of the transmission process. For this purpose, we represent the population using a square lattice of nodes. Each node represents an individual that may or may not carry the virus. Nodes that carry the virus can only transfer it to susceptible neighboring nodes. This important revision of the common SIR model provides a very realistic property: the same number of initially infected individuals can lead to multiple paths, depending on their initial distribution in the lattice. This property creates better predictions and probable scenarios to construct a probability function and appropriate confidence intervals. Finally, this structure permits realistic visualizations of the results to understand the procedure of contagion and spread of a disease and the effects of any measures applied.

Flood events are becoming more severe, causing significant problems to human communities, including physical, psychological, and material damage. For both flood forecasting in emergency response situations and flood mapping, georeferencing and data curation are paramount in the context of prevention or preparedness. Hence, data display, data management, and articulation with numerical simulation results must occur on GIS platforms. Our research is motivated by recent advances in Web and GIS technologies, social sensing and high-performance computing, and an envisaged wider availability of remote sensing data. This paper presents and discusses an innovative Web GIS platform named "RiverCure Portal" or "RCP" for short. This platform combines observations and hydrodynamic modelling tools to support various stages of the flood risk management cycle, including operational response, emergency preparedness, and risk assessment. RCP is a multi-organisation, multi-context digital platform with flexible configuration features to define and support multiple sensor types and modelling options, satisfying the various needs of different organisations and stakeholders. In addition, this paper discusses the RiverCure Approach, which encompasses the following tasks directly supported by the RCP platform: defining the context and involved geometries, associating sensors to the context, pre-processing and generating the context mesh, defining the simulation event, running the simulation event, and analysing the results from the simulation event. Thus, the RCP streamlines and simplifies data analysis and simulation procedures to meet decision-makers' needs. The novelties discussed in this paper include the design and discussion of a Web GIS platform that allows (i) to manage flood data and results of simulations at several contextual levels by different stakeholders such as domain experts, decision-makers, researchers, or students; (ii) to process and curate sensed data obtained from physical and social sensors; and (iii) update the state and values of the parameters of simulation tools through continuous data assimilation techniques for forecasting purposes. Finally, this paper supports the explanation and discussion with a running example, "Águeda 2016 flood" event, which dataset is publicly available for further study and experimentation.