Current numerical methods for simulating biophysical processes in aquatic environments are typically constructed in a grid-based Eulerian framework or as an individual-based model in a particle-based Lagrangian framework. Often, the biogeochemical processes and physical (hydrodynamic) processes occur at different time and space scales, and changes in biological processes do not affect the hydrodynamic conditions. Therefore, it is possible to develop an alternative strategy to grid-based approaches for linking hydrodynamic and biogeochemical models that can significantly improve computational efficiency for this type of linked biophysical model. In this work, we utilize a new technique which links hydrodynamic effects and biological processes through a property-carrying particle model (PCPM) in a Lagrangian/Eulerian framework. The model is tested in idealized cases and its utility is demonstrated in a practical application to Sandusky Bay. Results show the integration of Lagrangian and Eulerian approaches allows for a natural coupling of mass transport (represented by particle movements and random walk) and biological processes in water columns which is described by a nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model. This method is far more efficient than traditional tracer based Eulerian biophysical models for 3-D simulation, particularly for a large domain and/or ensemble simulations.

Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. Key terms of model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

As the problem of air pollution becomes more urgent, the precise determination of the characteristics of air pollution and the associated mechanisms of transport of pollutants becomes an increasingly urgent task. The simulation results of the Copernicus Atmospheric Monitoring Service (CAMS) [18] were used as initial data sets. The area of the Crimean Peninsula (within coordinates 44.50 latitude, 33.50 longitude) in 23 - 26 January, 2019 was chosen for the study. That time concentrations of pollutants (aerosol particles with a diameter of 2.5 (PM2.5) and 10 micrometers (PM10)) steadily increased and reached the maximum value. Four kinds of meteorological data sets were used in the work. The first and second data sets were the global NCEP/NCAR and (ERA-5) re-analyses. The third and fourth meteorological data sets were obtained using the Weather tracking and Forecasting model (WRF) as domains on high-resolution grids: 9 km and 2 km. The analysis of meteorological data sets was carried out to analyze the nature of the air masses movement in the studied region. Using the HYSPLIT model simulation was carried out to determine area of the sources of negative impact. The emissions transfer from the supposed sources of negative impact on the Crimea region is analyzed. The sensitivity of simulation the inverse and direct trajectories of air masses transfer was studied based a set of simulation results. On the basis of HYSPLIT model the simulation was carried out separately using meteorological data sets having different spatial and temporal resolutions. In general, the simulation results allowed us to conclude that the HYSPLIT model in combination with high-resolution meteorological input data of the WRF model are good to identify the sources of possible negative effects on atmospheric air located at long distances from the study site.

The Schelling model of segregation allows for a general description of residential movements in an environment modeled by a lattice. The key factor is that occupants change positions until they are surrounded by a designated minimum number of similarly labeled residents. An analogy to the Ising model has been made in previous research, primarily due the assumption of state changes being dependent upon the adjacent cell positions. This allows for concepts produced in statistical mechanics to be applied to the Schelling model. Here is presented a methodology to estimate the entropy of the model for different states of the simulation. A Monte Carlo estimate is obtained for the set of macrostates defined as the different aggregate homogeneity satisfaction values across all residents, which allows for the entropy value to be produced for each state. This produces a trace of the estimated entropy value for the states of the lattice configurations to be displayed with each iteration. The results show that the initial random placements of residents have larger entropy values than the final states of the simulation when the overall homogeneity of the residential locality is increased.

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.

We extend our high-resolution MRI-based Finite Element (FE) head model, previously presented and validated in [1–3], by considering the heterogeneities of the white matter structures captured through the use of Magnetic Resonance Elastography (MRE). This approach imparts more sophistication to our numerical model and yields results that more closely match experimental results. It is found that the peak pressure more closely matches the experiments as compared to the heterogeneous case. Qualitatively, we find differences in stress wave propagation near the corpus callosum and the corona radiata, which are stiffer on average than the global white matter. We are able to study the effects of these stiff structures on transient stress wave propagation within the cerebrum, something that cannot be done with a homogenized material model.

Tissues of the brain, especially white matter, are extremely heterogeneous - with constitutive response varying spatially. In this paper, we implement a high-resolution Finite Element (FE) head model where heterogeneities of white matter structures are introduced through Magnetic Resonance Elastography (MRE) experiments. Displacement of white matter under shear wave excitation is captured and the material properties determined though an inversion algorithm are directly used in the FE model. This approach is found to improve model predictions when compared to experimental results. In the first place, responses in the cerebrum near stiff structures such as the corpus callosum and corona radiata are markedly different compared with a homogenized material model. Additionally, the heterogeneities introduce additional attenuation of the shear wave due to wave scattering within the cerebrum.

For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

The crosslinking behavior of polyethylene (PE) determines its exceptional performance and application. In this study, we investigated the crosslinking behaviors of different PE resins through model simulation and rheological methods. Specifically, the mathematical equation of "S" model was established for PE resin. According to this equation, the optimal maximum gel content for high-density polyethylene (HDPE) was found to be around 85%. Moreover, the maximum crosslinking degrees for different PE resins depended largely on their density and molecular weight. The melt viscosities before crosslinking in PE resins were highly influenced by their melt index. The higher melt indexes resulted in the lower storage moduli, improving melt processability during processing. In addition, the crosslinking rates of PE resins were strongly influenced by peroxide concentration, independent of PE resin structures. For high molecular weight and low-density PE resins, they exhibited decreased ti values, increased A0 values, and decreased k6 values. However, there were no noticeable variations in the values of k2 and phi among different PE resins. And all simulated modeling outcomes showed the remarkable consistency with the experimental rheological data. These findings are of utmost significance in the industrial manufacture of PE resin.

There is a case about a system comprising two queues of M/M(a)/1/K with a due date with FCFS service policy in each queue. Using the methods of simulation and mathematical analysis, the solution diagrams were illustrated for the fixed due date Ɵ=1 second at the phase of due date up to the commencement of the service, limited capacity K₁=5 and K₂=4, for both scenarios of different service management rate resulted. For the analytical solution, first, a SAN model from the system was created in the Mobius tool. For simulation, a java discrete systems simulation code was used. Adjustment of the initial values, selection of the scenarios for service rate, the number of the consumers in the simulation, etc. were all carried out in the simulation code.

The paper presents the observational, remote sensing and model simulation to analyze Southern Brazil Antarctic Ozone Hole influence (SBAOHI) events occurred between 2005 and 2014. To an-alyze it we use Total Ozone Column (TOC) data provide by Brewer Spectrophotometer (BS) and OMI (Ozone Monitoring Instrument) instrument satellite, besides the AURA/MLS (Microwave Limb Sounder) instrument satellite ozone profiles was utilized with DYBAL (Dynamical Barrier Localization) code in MIMOSA (Modélisation Isentrope du transport Mésoéchelle de l’Ozone Stratosphérique par Advection) model Potential Vorticity (PV) fields. TOC has 7.0 ± 2.9 DU re-ductions average in 62 events. October have more events (30.7%). Polar Tongue events are 19.3% of total, being in October more frequently observed (50% of cases), with medium intensity (58.2%) and in stratosphere medium levels (55.0%). Already Polar Filament events (80.7%) are more frequent in September (32.0%), medium intensity (42.0%) and in stratosphere medium lev-els (40.7%). ENSO index positive phase influenced 61.3% of events, having dominance in all in-tensity categories (minor 57.9%, medium 51.6%, major 58.3%), while the equilibrium dominate the QBO influence with 50% of total cases in positive phase and 50 % in negative phase.

This article aims to serve as a guide for the construction of supply chain simulation models designed with a lean approach, using Promodel software. To achieve this, a supply chain was designed for a fictitious company located in the City of Celaya, Guanajuato and a set of suppliers located in different cities within the same State. It was used as a google tool to define the distances between each of the companies. As a final result, a representative model of a supply chain was obtained, as well as a methodology that allows the construction of lean supply chains regardless of the number of companies that comprise it. The effect of the variability in the delivery times between suppliers was incorporated into the simulation model, as well as an equation that calculates the pollution emissions of the vehicles that integrate the network that moves the products between the companies. With this work it is possible to represent networks of supply chains of real world companies, where the variability and contamination factor is included, to facilitate the decision making regarding the number of vehicles, inventory levels, quantities to be shipped, frequency in the shipments, etc. with the purpose of contaminating as little as possible and at the same time preventing interruptions in the supply chain using the least amount of resources possible.

In large eddy simulation (LES) of turbulent flows, the most critical dynamical processes to be considered by dynamic subgrid models to account for an average cascade of kinetic energy from the largest to the smallest scales of the flow is not fully clear. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study some essential statistical characteristics of vortex stretching and its role in dynamic approaches of modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. This technique is a single unified approach to studying a wide range of length scales in the turbulent flow. In addition, it also provides a rational basis for the statistical characteristics a subgrid model must serve in physical space to ensure an appropriate cascade of kinetic energy. Results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices.

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

In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adequately reflects the behaviour of financial time series data, relevant for volatility estimation. This dynamic framework is intended to help interested users on MCS experiments utilising the GAS model for reliable volatility calculations in finance and other areas.

High density of buildings and the large traffic volume of cities give rise to narrow spaces and high passenger flows in most subway stations. When evacuation is required during an emergency, these problems may trigger issues in operational safety. Therefore, it is imperative to comprehensively assess subway station emergency evacuation times and capacities during the operating process to ensure the design is safe, science-based, and rational. To further enhance safe evacuation capacity, this paper proposes an overall evacuation time model that considers the multielement charac-teristics of subway stations in multiple segments. The evacuation route is decomposed into five stages according to the critical nodes in the evacuation process. An overall emergency evacuation time model is established based on the diversity of bottlenecks in the five stages, integrating elements such as the horizontal movement velocity of passengers, subway equipment parameters, and human density. Taking Xi’an Wulukou Subway Station as an example, this paper verifies the outcomes of the theoretical model against the Pathfinder software and conducts additional anal-yses of the evacuation conditions of stairs and exits. The results show that the error between the theoretical emergency evacuation time and simulation evacuation time was 5.4%, The emergency evacuation model established in this study boasts strong robustness and stability.

Digital Elevation Model (DEM) is a representation of elevation data that represent terrain with or without overlaying objects of the Earth. It is the ideal and most widely used method for determining topography. DEMs are generated from various techniques such as traditional Surveying, Photogrammetry, InSAR, LiDAR, Clinometry and radargrammetry. It has been observed that mostly LiDAR-generated DEMs provide the best accuracy. The unavailability of LiDAR data in most of the region restricts global researchers from high-resolution and accurate DEMs. The recent launch of ICESat-2 with a 13m beam footprint and 0.7m pulse interval, promises elevations at high orbital precision. Its accuracy is of the order of few centimeters in complex topography, because of this ICESat-2 proves to be a good source to generate high-accuracy DEMs. ICESat-2 provides discrete photon data with elevations of points on the Earth’s surface. Traditional interpolation techniques tend to over-smooth the estimated space and still are unable to justify the complicated continuity in the topographical data. Machine learning algorithms are widely being used to extract patterns and spatial extent in geographic data. To estimate a DEM from ICESat-2 LiDAR point data, machine learning regression algorithms are implemented in this study. The present study has been performed for a plain region of Ghaziabad, Uttar Pradesh, India. Studies have shown that Cartosat-1 DEM V3 R1 product provides an accuracy of the order of 2m in predominantly plain regions, hence taken for this region. Current work focuses on comparing various regression-based machine-learning techniques to interpolate DEM from ICESat-2 data The RMSE of the interpolated DEM resulted from the Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, and Multi-Layer Perceptron Regressor was 7.13m, 7.01m, 7.15m, and 3.76m, respectively when evaluated against the TANDEM-X DEM of the same region. The MLP Regressor is found to perform the best among the four algorithms tested.

Heating systems such as heat pump and combined heat and power cycle systems (CHP) are representing a key component in the future smart grid. Their capability to couple the electricity and heat sector promises a massive potential to the energy transition. Hence, these systems are continuously studied numerical and experimental to quantify their potential and develop optimal control methods. Although numerical simulations provide time and cost-effective solution for system development and optimization, they are exposed to several uncertainties. Hardware in the loop (HiL) system enables system validation and evaluation under different real-life dynamic constraints and boundary conditions. In this paper, a HiL system of heat pump testbed is presented. This system is used to present two case studies. In the first case, the conventional heat pump testbed operation method is compared to the HiL operation method. Energetic and dynamic analyses are performed to quantify the added value of the HiL and its necessity for dynamics analysis. The second case, the HiL testbed is used to validate the heat pump operation in a single family house participating in a local energy market. It enables not only the dynamics of the heat pump and the space heating circuit to be validated but also the building room temperature. The energetic analysis indicated a deviation of 2% and 5% for heat generation and electricity consumption of the heat pump, respectively. The model dynamics emphasized the model capability to present the dynamics of a real system with a temporal distortion of 3%.

In this paper, the computational fluid dynamics (CFD) method has been used to simulate the gas flow and the catalytic hydrogenation process of the C-2 hydrogenation adiabatic reactor in a practical ethylene production unit of a chemical plant. The laminar finite-rate model was used for the chemical reaction model. The standard two-equation model was used for the turbulence model. The exponent-function kinetic model was used for the reaction dynamics model. The simulation results were in good agreement with the actual industrial data obtained from the C-2 hydrogenation reactor, and the distributions of reaction parameters such as temperature and component concentrations in the reactor were further analyzed. Based on the validated CFD models, the structure and operating conditions of the reactor, as well as hydrogen load distribution ratios, are simulated for comparing the comprehensive performance metrics of ethylene selectivity and acetylene conversion rate; as a result, we can obtain the optimal temperature of 312 K, the optimal hydrogen/acetylene ratio of 0.10 as well as the optimal hydrogen load distribution ratio of 4:4:2, and the diameter/height ratio exerts a marginal effect on the selectivity, conversion rate and harmonic mean of the reactor.

Emerging semiconductor devices often lack accurate analytical models. The same is usually true of any devices working under extreme conditions like cryogenic temperatures. The usual workaround involves the use of approximation models, usually based on lookup tables or neural networks individually fitted to measurement data. In the case of experimental devices or ones working under extreme conditions, the number of units available for measurement is limited. As a result, the number of approximation-model instances is too small to enable a statistical simulation of even middle-sized circuits, which is a necessary step in integrated-circuit design since it provides the realistic picture of the circuit’s behavior in the presence of manufacturing process variations. Approximation models using structure parameters as inputs do exist in the literature, but are only useful if the end user knows the statistical distributions of those parameters, which is not usually the case. We propose a technique based on generative machine learning, namely the variational autoencoder, that uses only a small sample of devices to capture the essential features of their I–V curves under process variations and subsequently generates an arbitrary number of similarly disturbed curves. The model trained on as few as 20 instances per device type is shown to precisely reproduce the distributions of period and power consumption of a ring oscillator.

A galvalume corrugated sheet was utilized as formwork for a reinforced concrete beam in flexure. A numerical model was validated to the experimentally obtained data, and further adopted to simulate the behavior of this composite structure under elevated temperatures. The properties and constitutive stress-strain data of the basic materials were obtained from experiments, and superimposed into the finite element model. The study concluded that the load carrying capacity of the member decreased as a direct function on temperature increase, and the cracking moment was very sensitive to the temperature fluctuation. The elevated temperatures also altered the failure mode.

To achieve the near zero emission of wastewater in the flue gas desulfurization (FGD) system in coal-fired power plant and better utilize the exhaust heat from flue gas, a feasible technology of spraying FGD wastewater in the flue duct for evaporation is discussed in the present study. A full-scale influencing factor investigation on the wastewater droplet evaporation performance is established under the Eulerian-Lagrangian model numerically. The dominant factors, including the characters of wastewater droplets, flue gas and the spray nozzles were analyzed under different conditions, respectively. Considering the multiple factors and conditions in the process, a Least-Square support vector machine (LSSVM) model is introduced to predict the evaporation rate based on the numerical results. Conclusions are made that the flue gas temperature and droplet diameter are of great importance in the evaporation process. The spray direction of droplet parallel with the flue gas flow direction is profitable for the dispersion of droplet, resulting the maximal evaporation rate. A double-nozzle arrangement optimized with relatively small flow rate is recommended. The LSSVM model can accurately predict the evaporation rate using the numerical results with different conditions.

This paper considers the principles of building a circuit model of film resistor cutting. The conductive resistive medium is defined with the component equations and the topology of the circuit model. A method of estimating the electric parameters of a resistor operating in the system with a measuring voltage source is shown. An equation system for the node voltages is defined, and the resistive layer parameters are analyzed as the circuit model structure changes during the cutting process.

Flour yield determined the profitability of flour mill, but the intrinsic variability of the grain makes it very complex to analysis and estimate wheat grain flour yield. Simulation for flour yield attributes offer considerable advantages in flour mill, if reliable predictions of flour yield can be provided, because the wheat grain characteristics can be tested before milling. If this is possible, the characteristics thus observed could be quantified more reliably and objectively by Structural Equation Modelling (SEM). SEM was used to look for the most important wheat grain characteristics on flour yield, and then these wheat grain characteristics were used to simulate flour yield. Furthermore, the regressive equation was verified by the field experiment. The coefficient of variation of grain characteristics was low and distributed rather closely. The results of SEM showed that test weight had the most significantly effects on flour yield, followed by the hardness index. Test weight and hardness index could excellently estimate flour yield by multiplicative effect of test weight and hardness index, and which could determine 68% of the variation in flour yield. The simulation result can not only predict flour yield, but also look for the important grain characteristics for the flour yield.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the heath care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization.Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

The beautiful and stable posture is essential for ballet. Especially the pirouette which dancers balance themselves on their one leg is one of the most impressive postures. During the pirouette motion, loads from the rotation and the body weight are concentrated to the one leg. Proper ballet shoes can reduce the muscle burden and improve the stability. Four types of shoes; bare feet, running shoes, pointe shoes, and dance sneakers are analyzed. The motion capture system and SIMM are used for the analysis of muscle burden and stability during pirouette. The major 6 muscles in the leg (beceps femoris long head, beceps femoris short head, soleus, rectus femoris, gastrocnemius medialis, and gastrocnemius lateralis) are analyzed, and the effect of rotating velocity is considered. Dance sneakers are outstanding for improving the stability and lessening the muscle burdens in all conditions for beginners. That comes from the design feature of the divided shoe soles and protecting the ankle of the axis leg. With the results and analysis, the direction for design optimization of ballet shoes is suggested. Consequently, this research is about the verification of sports equipment using computer simulation with the musculoskeletal model from a scientific viewpoint of biomechanics.

Australia is a unique continent surrounded by the ocean and the majority of catchments flow to the coast. Some of these catchments are gauged and others are ungauged. There are 405 gauged catchments covering 2,549,000 km2 across the coastal regions of 12 drainage divisions in Australia. Whereas there are 771 catchments conceptualised as ungauged covering additional 835,000 km2. The mean annual rainfall and PET and its spatial and temporal distribution vary significantly from one drainage division to another. We developed a continuous daily streamflow time series of all gauged and ungauged catchments from 1993 onwards. We applied the GR4J lumped conceptual model to these catchments. The performance of gauged catchments was analysed through (i) visual inspection of daily hydrographs, flow duration curves and daily scatter plots, and (ii) performance metrics including NSE and PBias. Based on the NSE and PBias, performance ratings of 80% and 96% of the models respectively were found 'good'. There was no relationship found between the catchment area and model performance. The ungauged catchments were divided into four categories based on distance from potential donor catchments where observed data are available for GR4J model calibration, and Köppen climate zone. The total ungauged catchments represent 24.7% of the total drainage division areas. The streamflow from ungauged catchments was estimated using GR4J model based on the parameters of their donor catchments. Overall, runoff ratios from ungauged catchments were found to be higher compared to their donor gauged catchments, likely driven by their higher rainfall and less PET. This tendency was particularly evident in two drainage divisions – the Carpentaria Coast (CC) and Tanami-Timor Sea Coast (TTS) where ungauged areas comprised 51% and 43% respectively. The mean gauged annual streamflow varied significantly across drainage divisions – 230 GL from South Australian Gulf (SAG) to 146,150 GL in TTS. The streamflow from all ungauged catchments was estimated at 232,200 GL per year. Overall, the average streamflow from all drainage divisions, including gauged and ungauged areas, across the coastal regions of Australia was estimated at 419,950 GL per year. This nationwide estimate of streamflow dataset could potentially enhance our understanding of coastal processes and improvements of marine modelling systems and tools.

The aim of this work is to introduce a mathematical model representing the evolution of the temperature in a vegetation cover and the ground underneath it. Vegetation, and its interaction with soil, plays a very important role in the protection of soil surface from the action of sun and precipitations. A reduction in the vegetated mass increase the risk of desertification, soil erosion or surface runoffs which which can give rise to soil loss and sediment retention. These processes can favour climate change and global warming, which are major concerns nowadays. The mathematical model presented takes into account the main processes involved in vegetation cover and the interaction with the soil, among which, we can mention the Leaf Area Index, which is a dimensionless quantity defined as the one-sided green leaf area per unit ground surface area, or albedo and co-albedo which are clearly influenced by the vegetation. It is also considered a nonlinear heat capacity in the soil which incorporates the latent heat of fusion, when the phase change takes place. The numerical technique used to solve the mathematical model is based on a finite volume scheme with Weighted Essentially Non Oscillatory technique for spatial reconstruction and the third order Runge-Kutta Total Variation Diminishing numerical scheme is used for time integration. Some numerical examples are solved to obtain the distribution of temperature both in the vegetation cover and the soil.

As a radiant light source within the dynamic range of most spacecraft payloads, the moon pro-vides an excellent reference for on-orbit radiometric calibration. This research hinges on the pre-cise simulation of lunar spectral irradiances and the Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradi-ances, utilizing datasets obtained from Lunar Reconnaissance Orbiter Camera (LROC). The re-search also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit space-craft calibration procedures, thereby boosting the fidelity of data gathered from satellite obser-vations.

Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. On the contrary to ongoing outbreak in China, however, there are limited secondary outbreaks caused by exported case outside the country. We here conducted simulations to estimate the impact of potential secondary outbreaks at a community outside China. Simulations using stochastic SEIR model was conducted, assuming one patient was imported to a community. Among 45 possible scenarios we prepared, the worst scenario resulted in total number of persons recovered or removed to be 997 (95% CrI 990-1,000) at day 100 and maximum number of symptomatic infectious patients per day of 335 (95% CrI 232-478). Calculated mean basic reproductive number (R0) was 6.5 (Interquartile range, IQR 5.6-7.2). However, with good case scenarios with different parameter led to no secondary case. Altering parameters, especially time to hospital visit could change the impact of secondary outbreak. With this multiple scenarios with different parameters, healthcare professionals might be able to prepare for this viral infection better.

Cloudburst is one of the most devastating and frequently occurring natural hazardous events in Indian Himalayan region. Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Whenever, such events occur, lead to flash floods causing landslides, house collapses, dislocation of traffic, and human casualties on a large scale. Therefore, it is necessary to predict the cloudburst inundation zones accurately to avoid damage associated with them. For this, high resolution Digital Elevation Model generated from CartoSat-1 (Stereo pair) were integrated in MIKE 11 Hydrodynamic 1D model to generate longitudinal profile of the study area and to find water level, peak discharge, flow velocity, flow width at different reaches along the Asi ganga and Bhagirathi river, to know the Cloudburst flood inundation scenario. On 3^rd August 2012 one of the major Cloudburst event occurred in Asi Ganga Valley in Indian Himalayan region which was considered for simulation of hydrodynamic model. For a Cloudburst event, 100 mm/hr rainfall was considered for the simulation of the hydrodynamic model. It is observed that the discharge rise from 50 m³/s to 549.164 m³/s (an abrupt increase of about 10 times) within 1 hr at Sangamchetty in Asiganga river and at Joshiyara area rise from 600 m³/s to 3378.69 m³/s (an abrupt increase of about 5 times) within 4 hr in Bhagirathi river. Similarly the water level rises around 3 m and 6m in Asi Ganga and Bhagirathi rivers respectively. Flash Flood inundation areas due to Cloudburst on 3^rd August 2012 were demarcated from the simulation results in GIS environment.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

In Differentiated Services (DiffServ) architecture, Priority Service System (PSS) plays a vital responsibility to provide Quality-of-Service (QoS) for the applications based on networks. Priority queuing systems has always been a subject of interest for analytical modelling and evaluating the performance. However, previous works have mainly focused on performing priority queuing under range dependent traffics, namely Short Range Dependent (SRD) or Long Range Dependent (LRD). Recent studies revealed that realistic traffic demonstrates a heterogeneous nature for the modern networks that provide multiple services. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to study the heterogeneous traffic impact on designing and performance of the systems based on networks. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to investigate the impact of heterogeneous traffic on the design and performance of network-based systems. Here, the time of presence of requests in limited and unlimited buffer system is restricted. If the restriction is violated, the requests will be lost. The results of experiments conducted on both models are compared and it is identified that they differ in the allowable limit (2-9) %.

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vi-bration harvesting, a vibration differential model of tree branches was constructed using the concen-trated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the peak frequency in the tree trunk spectrum, the frequency of occurrence and mean accel-eration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the peak fre-quency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

During the last decades, the production systems have developed different strategies to increase their competitiveness in the global market. In a manufacturing and services systems, Lean Manufacturing has been consolidated through the correct implementation of its tools. The present paper presents a case study developed in a Food Packer company where a Simulation Model was considered as an alternative to reduce the waste time generated by the poor distribution of operations and transportation areas for a product within the factory. As a matter of fact, the company has detected problems on the layout distribution that prevents to fulfill the market demand. In addition, the principal aim was to create a simulation model to test different hypothetical scenarios and alternative designs for the layout distribution without modifying its facilities. Moreover, the implemented methodology was based on classical models of simulation projects and a compendium of the manufacturing systems optimization by simulation process used during the last ten years. Also, a mathematic model supported by the Promodel ® simulation software was developed considering the company characteristics; along with the model development, it was possible to compare the production system performance from the percentage of used locations, the percentage of resources utilization, the number of finished products, and the level of Work in Process (WIP). Finally, the verification and validation stages were performed before running the scenarios in the real production area. The results generated by the implementation of the project represent an increase of 68% in the production capacity and a reduction of 5% in the WIP. In addition, both outcomes are associated with the resources management, which were reassigned to other production areas.

The nature of the fracture and fragmentation process of concrete medium under blast loading is the transformation process of the medium from continuum to discontinuity, coupled with the significant rate correlation of concrete medium, its mechanical behavior presents a high degree of complexity. Problems such as grid distortion and even negative volume are often encountered in the finite element method and others when solving this problem, while the material point method can effectively avoid these problems. In addition, the widely used HJC concrete constitutive model does not take into account the segmented characteristics of the calculation function for dynamic increasing factor. Therefore, in this paper, firstly, the calculation function for dynamic increasing factor in the HJC concrete model is modified by SHPB experiment, and the improved HJC concrete model is proposed; secondly, the material point method simulation program is developed, and the improved HJC concrete model is embedded into the simulation program; finally, the simulation program is verified by numerical examples, and the results show that the developed simulation program can better simulate the fracture and fragmentation process of concrete medium under blast loading, especially the pulverization characteristics of the medium in the near zone of the load.

During deep-sea mining, the heavy metals pollutants can cause contamination in the marine environment. In this paper, the multiphasic coupling model is established to describe the heavy metal migration process during deep-sea mining, which takes the effects of convection-diffusion, adsorption-desorption and sedimentation-resuspension of heavy metals in the aquatic environment into full consideration. Due to the advantages of Lattice Boltzmann method, it is adopted to numerically solve the multiphasic coupling model and achieve the simulation for heavy metal migration process during deep-sea mining. In addition, taking Cadmium as an example, the concentration variations are discussed and analyzed in detail. Based on the established model and Lattice Boltzmann method, the concentration distribution of heavy metals can be accurately described to provide the reasonable bases for the evaluation of marine environmental protection.

A numerical mechanism connecting ice algal ecodynamics with the buildup of organic macromolecules is tested within modeled pan-Arctic brine channels. The simulations take place offline in a reduced representation of sea ice geochemistry. Physical driver quantities derive from the global sea ice code CICE, including snow cover, thickness and internal temperature. The framework is averaged over ten boreal biogeographic zones. Computed nutrient-light-salt limited algal growth supports grazing, mortality and carbon flow. Vertical transport is diffusive but responds to pore structure. Simulated bottom layer chlorophyll maxima are reasonable, though delayed by about a month relative to observations due to uncertainties in snow variability. Upper level biota arise intermittently during flooding events. Macromolecular concentrations are tracked as proxy proteins, polysaccharides, lipids and refractory humics. The fresh biopolymers undergo succession and removal by bacteria. Baseline organics enter solely through cell disruption, so that the internal carbon content is initially biased low. By including exudation, agreement with dissolved organic or individual biopolymer data is achieved given strong release coupled to light intensity. Detrital carbon then reaches hundreds of micromolar, sufficient to support structural changes to the ice matrix.

The implementation of grey and green infrastructure is an effective mean to address urban flooding and nonpoint source pollution, but due to the complexity of the process and the diver-sity of benefits, there is a lack of measurement for their comprehensive benefits. Adopting a typ-ical university in Beijing as an example, this paper simulated the multidimensional benefits of the water quantity, water quality, and ecology of grey and green facility renovation by coupling the storm water management model (SWMM) and InfoWorks Integrated Catchment Manage-ment (ICM). Monetization methods and economical means were employed to characterize the comprehensive benefits. The results showed that grey and green infrastructure retrofitting re-duces the number of severe overflow nodes in the study area by 54.35%, the total overflow vol-ume by 22.17%, and the nonpoint source pollution level by approximately 80% under the heavy rain scenario and 60% under the rainstorm scenario. The annual benefits of grey and green infra-structure renovation reached ¥765,600/year, of this amount, ¥83,100/year is from hydrological regulation, ¥454,100/year is from nonpoint source pollution reduction, and ¥228,300/year is from ecological improvement. The benefits of green facilities were higher than those of grey facilities, and the combined benefits were negatively correlated with the rainfall level, with a total bene-fit–cost ratio of 1.19. The results provide methodological and data support for grey and green in-frastructure retrofitting within the context of sponge cities.

In this study, the focus is on the drop test simulation of an MR (Magnetorheological) damper-based main landing gear (MRMLG), aiming to explore multi-degree-of-freedom (DOF) dynamic models during aircraft landing. Three different 6-DOF dynamic models are proposed in this work and their drop performances are compared with results achieved one of commercial softwares. The proposed models include a nonlinear aircraft model (NLAM), a linearized approximated aircraft model (LAAM) linearizing from the nonlinear equations of motion in NLAM, and a fully approximated aircraft model (FAAM) which linearizes the MRMLG's strut force model. In order to evaluate drop performance of the aircraft landing gear system with MR dampers, a 7-DOF aircraft model incorporating the nonlinear MRMLG is formulated using RecurDyn. The principal comparative param-eters are the coefficient of determination (R2) for system response of each model with Re-curDyn model and root mean square error (RMSE) which is the ensembles of CG dis-placement data for each model. In addition, the ensemble of time series data is created for diverse drop scenarios, providing valuable insights into the performance of the proposed drop test models of the aircraft landing gear system featuring MR dampers.

The engagement of the arresting hook with the arresting cable is a critical maneuver that is essential to the safe operation of aircraft landing on aircraft carrier. A comprehensive understanding of the engagement process dynamics is necessary to optimize landing performance and ensure the safety and efficiency of carrier operations. In this paper, an efficient and accurate simulation and analysis method is presented for studying the arresting hook engaging arresting cable process. The Finite Element Method and Multibody Dynamics (FEM-MBD) approach is employed. By establishing a rigid-flexible coupling model encompassing the aircraft frame, arresting hook, carrier deck and arresting gear system, the dynamic model for the engagement process is obtained. The model incorporates multiple coordinate systems to effectively capture the relative motion between the rigid and flexible components, enabling a thorough understanding of the dynamics characteristics. The analysis conducted in this paper takes into account various factors, including the material properties of the components, the characteristics of the arresting gear system, and the state of the aircraft during the engagement process. The analysis method is verified by comparing the simulation results with experiments of arresting hook rebound obtained from reference. Finally, simulations are performed to analyze the engagement process under different touchdown points and rolling angle of aircraft. The simulation results provide valuable insights into the distribution of stresses during the arresting hook and cable engagement, the center of gravity variations, as well as the response of the tire touch and rollover cable under specific scenarios. The proposed rigid-flexible coupling arresting dynamics model in this paper enables effective analysis of the dynamic behavior during arresting hook engaging arresting cable. The results obtained from this analysis offer valuable insights into the performance of the engagement process, which can be used to improve the design of carrier-based aircraft and techniques for carrier landing.

Fibre reinforced concrete (FRC) has been used for decades in certain applications of the construction industry, such as tunnel linings and precast elements, but has experienced an important progress in recent times, boosted by the inclusion of guidelines for their use in some national and international standards. Traditional steel fibres have been studied in depth and their performance is well known although in recent years new materials have been proposed as possible alternatives. Polyolefin macro-fibres, for instance, have proven to enhance the mechanical properties of concrete and the parameters that define their behaviour (fibre length, fibre proportion or casting method, for instance) have been identified. These fibres overcome certain traditional problems related to steel fibres, such as corrosion or their interaction with magnetic fields, which can limit the use of steel in some applications. The behaviour of polyolefin fibre reinforce concrete (PFRC) has been numerically reproduced with success through an embedded cohesive crack formulation that uses a trilinear softening diagram to describe the fracture behaviour of the material. Furthermore, concrete behaves well under high temperatures or fire events, especially when it is compared with other construction materials, but the behaviour of PFRC must be analysed if the use of these fibres is to be extended. To this end, the degradation of PFRC fracture properties has been recently experimentally analysed under a temperature range between 20ºC and 200ºC. As temperature increases, polyolefin fibres modify their mechanical properties and their shape, which reduce their performance as reinforcement of concrete. In this work, those experimental results, which include results of low (3 kg/m3) and high (10 kg/m3) proportion PFRC specimens, are used as reference to study the fracture behaviour of PFRC exposed to high temperatures from a numerical point of view. The experimental load-deflection diagrams are reproduced by modifying the trilinear diagram used in the cohesive model, which helps to understand how the trilinear diagram parameters are affected by high temperature exposure. Finally, some expressions are proposed to adapt the initial trilinear diagram (obtained with specimens not exposed to high temperature) in order to numerically reproduce the fracture behaviour of PFRC affected by high temperature exposure.

The unsteady flow around the monopile under the influence of wave and vibration is studied by numerical method. Based on VOF model and SST Turbulence model, combined with Intermittent Transition model and dynamic mesh method, wave generation and pile vibration in numerical flume are simulated. The flow field around vibrating monopile in a calm water flume and a wave flume is simulated and the simulation results are compared. The calculation results show that the monopile vibration has a great disturbance to the unsteady flow around the pile, and the disturbance degree intensifies with the increase of the vibration amplitude of the monopile: Under the action of inertia, the vibration of the monopile leads to the reattachment of the shedding vortices; Under the influence of the reattachment vortices, the asymmetry of the vorticity on the side wall of the monopile intensifies; The intensification of the disturbance causes the reduction of the phase lead angle of the bed shear stress on the side of the single pile, and an additional high bed shear stress appears on the side of the monopile. In addition, the vortex structure formed alternately around the monopile during the vibration process leads to the periodic change of the high bed shear stress.

(1)Background: To perform a simulation-based analysis of 2019 COVID-19 outbreak and how would the electronic commerce indursty help China’s economy resilient. (2)Methods: As the epidemic continues, it is possible to use Computable General Equilibrium model to simulate the economic consequences and analyse the role of electronic commerce industy in COVID-19 outbreak. (3)Results: Estimates and models produced at the time of the outbreak suggested that COVID-19 could have a catastrophic effect on China’s economy. National statistics were examined for anomalies that corresponded to the timing of COVID-19 outbreak and, where possible, the size of any gain or loss found estimated. Our analysis suggests that the electronic commerce industry could help China’s economy to recover by stimulating consumption, improving technological level and expanding investment. (4)Conclusions: This exercise holds important lessons for estimating the electronic commerce’s role of similar events – such as pandemic influenza – and measures to recover the economy. We suggest that electronic commerce industry should be paid more attention in economic development, especially in epidemic time. The implications of our findings are discussed in the light of a prospective epidemic.

The Earth's water resources, totaling 1.386 billion cubic kilometers, predominantly consist of saltwater in oceans. Groundwater, plays a pivotal role, with 99% of usable freshwater supporting 1.5–3 billion people as drinking water source and 60–70% for irrigation. Climate change, with temperature increase and altered precipitation patterns, directly impacts groundwater systems, affecting recharge, discharge, and temperature. Hydrological models are crucial for assessing climate change effects on groundwater, aiding in management decisions. Advanced hydrological models, incorporating data assimilation and improved process representation, contribute to understanding complex systems. Recent studies, employ numerical models to assess climate change impacts on groundwater recharge that could help in management of Groundwater. Groundwater vulnerability assessments vary with the spatial and temporal considerations, as well as assumptions in modelling groundwater susceptibility. The review assesses the vulnerability of groundwater to climate change and stresses the importance of accurate assessments for sustainable water resource management. It highlights challenges in assumptions related to soil and aquifer properties, multiple stressors, adaptive capacity, topography, aquifer properties, and groundwater contamination processes and gradual sea level rise scenarios and realistic representations of the region of study. The advancements in hydrological modelling, including the integration of uncertainty quantification and remote sensing data, artificial intelligence, could assist in the efforts to improve models for assessing the impacts of climate change on hydrological.

This paper presented a two-dimensional, well-balanced hydrodynamic and sediment transport model based on the solutions of variable density shallow water equations (VDSWEs) for sediment-laden flows, and the Exner equation for bed changes. Those equations are solved in a coupled way by the first-order Godunov-type finite volume method. The Harten-Lax-van Leer-Contact (HLLC) Riemann solver is extended to find the local Riemann fluxes in order to maintain the exact balance between the momentum term and the bed slope term. The advantage of a well-balanced scheme over an unbalanced scheme is demonstrated by the synthetic standing contact-discontinuity test case. Then, the model is employed to simulate two laboratory experiments. At last, a field case, the 1996 Lake Ha! Ha! flood event (Canada), is simulated. Results of cross sectional geometries and profiles of longitudinal thalweg agree well with measurements. The accuracy and simplicity of the numerical model, together with the robust implementation, make the model a good candidate for practical engineering applications.

The use of mathematical models of physiological systems in medicine has allowed the development of diagnostic, treatment, and medical educational tools, but their application for predictive, preventive, and personalized purposes is restricted by their complexity. Although there are strategies that reduce the complexity of applying models by fitting techniques, they focus on a single instant of time, neglecting the effect of the system's temporal evolution. The aim of this work is to propose a dynamic fitting strategy of physiological models with large number of parameters and a constrained amount of experimental data, focused on obtaining better predictions based on the system's temporal trend and useful to predict future states. It was applied in a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects under aerobic exercise were used for fitting and validation. The model predictions obtained at steady-state using the proposed strategy and the nominal values of the parameters were compared. The best results corresponded mostly to the proposed strategy, mainly regarding the overall prediction error. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

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.

The modeling of flood-induced bend scour near embankment toes can provide important information for river engineering, embankment safety warnings, and emergency action management. During the rainy seasons, short-term general scour and bend scour are the most common causes for the failure of reinforced concrete embankments in Taiwan. To gain a deeper understanding of the scouring process near levee foundations, this study proposed a straightforward and practical method for bend scour simulation. The proposed simulation method is subdivided into three stages: two-dimensional flow simulation, general scour estimation, and bend scour estimation. A new bend-scour computation equation is proposed and incorporated into a two-dimensional hydraulic finite-volume model for simulating the evolution of bend scour depth around embankment toes. The proposed method is applied to simulate the temporal evolution of bend scouring near the Shuideliaw Embankment on the Cho-Shui River in Taiwan, where serious failure occurred during the June 2012 monsoon. Field data were gathered using the numbered-brick technique at the Shuideliaw Embankment to demonstrate the accuracy of the proposed method. The results of the bend scour simulations compared reasonably well with field measurements, indicating close agreement in terms of water levels and bend scour depths near the Shuideliaw Embankment. The proposed method was found to quickly estimate the maximum short-term general scour and bend scour depths for further enhancement of the safety of the embankment toe.

Regular regimes of oil and oil products pipelines are studied. The problem is to develop an algorithm of effective pipeline system control that ensures the fulfillment of specified boundary conditions at the system boundaries. The product is pumped in batches, the flow is characterized by a change in temperature and pressure flow parameters over time. The developed solution method uses the principle of decomposition in the sense that each computational iteration is im-plemented in 3 stages, in three nested loops. The inner cycle is connected with the material bal-ance relations, at the next cycle the flow equations are introduced and the fulfillment of the 2nd Kirchhoff's law is monitored. If the flow process is significantly affected by temperature, then an external cycle is also implemented, correcting the solution so that the energy conservation equa-tion is satisfied. In the calculation procedure, some computational difficulties may arise, which consist in choosing an appropriate optimization method. On a specific example, the possibility of using various library programs to solve the problem was checked. The developed method is suita-ble for the analysis of systems of arbitrary topology. Since the method is not time-consuming, it can be recommended for online control of oil and oil products supply systems. The paper opens a new direction in the theory of hydraulic circuits. It uses both traditional methods of continuum mechanics and stochastic models.

Instead of simplified steady-state models, with modern computers, one can solve the complete aero-thermodynamics happening in gas turbine engines. In the present article, we describe a mathematical model and numerical procedure to represent the transient response of a PT6A gas turbine engine operating at off-design conditions. The aero-thermal model consists of a set of algebraic and ordinary differential equations that arise from the application of the mass, linear momentum, angular momentum, and energy balances in each engine's component. The solution code has been developed in Matlab-Simulink using a block-oriented approach. Transient simulations of the PT6A engine start-up have been carried out by changing the original Jet-A1 fuel with biodiesel blends. Time plots of the main thermodynamic variables are shown, especially those regarding the structural integrity of the burner. Numerical results have been validated against reported experimental measurements and GasTurb simulations. The computer model has been capable to predict acceptable fuel blends, such that the real PT6A engine can be substituted to avoid the risk of damaging it.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

Corona loss generated from conductors is one of the important design factors on ac transmission lines. Based on helical charge simulation method, the 3-D corona loss calculation model considering the conductors’ outer strands in the corona cage is established. With regard to 2-D corona-loss calculation method, the calculation model proposed in this paper can take the electric field strength’s nonuniformity along the axial direction of stranded conductor into consideration, furthermore, it can also calculate the corona loss when the conductor has sag. The calculation results of the LGJ-300/40 and LGJ-400/35 in the small corona cage well coincide with the measurement results, which indicates that the corona-loss calculation model is appropriate. In addition, the corona loss of the bundle conductors 4 × LGJ720 with sag in UHV corona cage is also analyzed.

Background: New open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model. Results: We compare the open-source Switch 2.0 to GE Energy Consulting’s Multi Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100. Conclusions: Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here. Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

A novel educational platform called Shared Modeling and Simulation Environment (SMSE) was initiated in the ERASMUS+ “CybPhys” project. It integrates the Jupyter platform, including Ju-pyter Notebooks, for e-learning in the STEM field. A novel platform enhances e-learning by combining Moodle with Jupyter's capabilities to create virtual labs. A survey was conducted in the spring of 2023 among bachelor and master students of Chernihiv National Polytechnic Uni-versity aimed to gather feedback from students using SMSE in their e-learning process. Two student groups were involved: one of them consisted of students using SMSE in both the autumn and spring semesters and another represented the students starting with SMSE in the spring se-mester. The survey based on the adjusted Technology Acceptance Model (TAM) was conducted to understand the acceptance of this e-learning approach. A comprehensive analysis of students’ answers based on the TAM approach revealed the inside of the process of transformation of stu-dents’ perceptions during the acquisition of the SMSE platform. Our research demonstrates that SMSE effectively merges Moodle's online learning capabilities with Jupyter notebooks, provid-ing a flexible and interactive learning experience for both in-class and remote students. It pro-vides a web-based, multifunctional e-learning environment that combines a variety of tools and technologies, giving students the possibility to be involved in all sorts of teaching activities, such as lectures, exercises, simulations, etc., through the use of one single online environment.

The properties of rubber are strongly influenced by the distribution of filler within the polymer matrix. Here we introduce a Monte Carlo-based morphology generator. The basic elements of our model are cubic cells, which, in the current version, can be either silica filler particles or rubber volume elements in adjustable proportion. The model allows the assignment of surface free energies to the particles according to whether a surface represents, for instance, 'naked' silica or silanised silica. The amount of silanisation is variable. We use a nearest-neighbour site-exchange Monte Carlo algorithm to generate filler morphologies, mimicking flocculation. Transmission electron micrographs (TEM) as well as small angle scattering (SAS) intensities can be calculated along the Monte Carlo trajectory. In this work we demonstrate the application of our morphology generator in terms of selected examples. We illustrate its potential as a tool for screening studies, relating interface tensions between the components to filler network structure as characterized by TEM and SAS.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

The study used ERA5 reanalysis SST dataset re-gridded to a common grid of 0.25° X 0.25° spatial resolution (latitude × longitude) for the historic (1940–2014) and projected (2015–2100). The SST simulation under the SSP5-8.5 scenarios was carried out with output from 8 General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping EQM for the historical (1940-2015) and the future (2030-2100) while the CMIP6 model simulation was evaluated against the ERA5 monthly rainfall observed data for monthly and annual sea surface temperatures (SST) over the Gulf of Guinea. ). The CMIP6 models simulation for SST projection from 2030-20100 based on SSPS 8.5 SST is projected to increase by 4.61o (31oC) in 2030 to 35oC in 20100 in the coastal GOG and 2.6oC in the Western GOG (Sahel). The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models and the analysis showed ACCESS 0.1,CAMS CSM 0.2, CAN ESM 0.3, CMCC, 0.3 and MCM, 0.4 indicating that all models performed well in capturing the climatological pattern of the SST. The.CMIP6 bias corrected model simulations showed increased SST warming over the GOG will be higher in the Far period end than the Near-term climate. The study affirmed that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate

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.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

Nitrile butadiene rubber (NBR) and its various composites are extensively studied as matrix materials for oil-free lubricated mechanical friction surfaces. While numerous macroscopic friction experiments can compare the frictional performance of different NBR composites, the complex material preparation and low experimental efficiency pose challenges. Nanoscale molecular dynamics (MD) simulations can help develop materials with improved friction properties, but they cannot directly provide the friction coefficients of actual macroscopic friction surfaces. Therefore, it is important to integrate the advantages of macroscopic and nanoscale friction studies and perform a synergistic analysis to modify the friction properties of composite materials. In this study, a multi-scale approach is proposed to simulate the frictional characteristics of carbon nanotube (CNT)-reinforced NBR by combining the MD friction simulation， micromechanical bridging model, and macroscopic finite element analysis (FEA) methods. The results of the multi-scale friction simulation of copper (Cu)-CNT/NBR composites show that the addition of CNTs significantly improves the frictional properties of NBR, and the extent of the improvement is related to the mass fraction of CNTs. As the mass fraction of CNTs increases (0%, 1.25%, 2.5%, 5%), the coefficient of friction decreases from 0.50 to 0.38. The results are validated by ring-block friction experiments at the same scale as the simulations.

To enable purposeful design and implementation of automated concrete technologies, precise assessment and prediction of the complex material flow at various stages of the process chain are necessary. This paper investigates the intermediate stage of the extrusion and deposition processes in extrusion-based 3D-concrete-printing, using a numerical model based on the Particle Finite Element Method (PFEM). In PFEM, due to the Lagrangian description of motion, remeshing algorithms and the alpha shape method are used to track the free surface during large deformation scenarios. The Bingham constitutive model was used for describing the rheological behaviour of fresh concrete. This model is validated by comparing the numerically predicted layer geometries with those obtained from laboratory 3D printing experiments. Extensive parametric studies were then conducted using the numerical simulation, delineating the influence of process and material parameters on the layer geometries, the dynamic surface forces generated under the extrusion nozzle and the inter-layer interactions.

Full-scale/model test comparison studies to validate the traditional ABL wind tunnel simulation technique are reviewed. According to the literature review, notable discrepancies between full-scale measurement results and model test results were observed by most performed comparison studies, but the causes of the observed discrepancies were not revealed in a scientific way by those studies. In this regard, a new research scheme for future full-scale/model test comparison studies is proposed in this article, which utilizes the multiple-fan actively controlled wind tunnel simulation technique. With the new research scheme, future full-scale/model test comparison studies are expected to reasonably disclose the main problems with the traditional ABL wind tunnel simulation technique, and the technique can be improved correspondingly.

A crop-weed simulation model is presented to compare and evaluate Integrated Weed Management (IWM) strategies. Specifically, the model was parameterized for soybean crops in competition with Euphorbia davidii Subils. We used both, weed and crop demographic data surveyed in agronomic fields of the central zone of the Buenos Aires province, Argentina, throughout two crop cycles (2011/2012 and 2013/2014). The proposed model underwent a calibration process and subsequent validation with 70/30 % data split respectively (N=37). Two annual-based and one multiannual-based case studies were simulated to demonstrate the performance of the model. Different IWM strategies were compared under both, operational and tactical planning horizons through the evaluation of different model outcomes (i.e. crop yield, interspecific competition, economic return, and environmental impact). Our case studies results suggest that the inclusion of cultural management practices could reduce weed interspecific competition by 46 to 97 % and weed seed production by 40 to 89%. An increment in both expected crop yield, by 6 to 20%, and annual gross margin, by 44 to 199 US$.ha-1, were obtained in silico for similar levels of environmental impact.

Most of the pore-scale imaging and simulations of non-Newtonian fluid are based on the simplifying geometry of network modeling and overlook the fluid rheology and heat transfer. In the present paper, we developed a non-isothermal and non-Newtonian numerical model of the flow properties at pore-scale by direct simulation of the 3D micro-CT images using a Finite Volume Method (FVM). The numerical model is based on the resolution of the momentum and energy conservation equations. Owing to an adaptive meshing technique and appropriate boundary conditions, rock permeability and mobility are accurately computed. A temperature and concentration-dependent power-law viscosity model in line with the experimental measurement of the fluid rheology is adopted. The model is first applied at isothermal condition to 2 benchmark samples, namely Fontainebleau sandstone and Grosmont carbonate, and is found to be in good agreement with the Lattice Boltzmann method (LBM). Finally, at non-isothermal conditions, an effective mobility is introduced that enables to perform a numerical sensitivity study to fluid rheology, heat transfer, and operating conditions. While the mobility seems to evolve linearly with polymer concentration, the effect of the temperature seems negligible by comparison. However, a sharp contrast is found between carbonate and sandstone under the effect of a constant temperature gradient. Besides concerning the flow index and consistency factor, a good master curve is derived when normalizing the mobility for both the carbonate and the sandstone.

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the SST turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

This review is devoted to present the history of CARDIOSIM© software simulator platform, which was developed in Italy to simulate the human cardiovascular and respiratory system. The first version of CARDIOSIM© was developed at the Institute of Biomedical Technologies of the National Research Council in Rome. The first platform version published in 1991 ran on PC with disk operating system (MS-DOS) and was developed using the Turbo Basic language. The last version runs on PC with Microsoft Windows 10 operating system; it is implemented in Visual Basic and C++ languages. The platform has a modular structure consisting of seven different general sections, which can be assembled to reproduce different pathophysiological conditions. The software simulator can reproduce the most important circulatory phenomena in terms of pressure and volume relationships. It represents the whole circulation using a lumped-parameter model and enables the simulation of different cardiovascular conditions according to Starling’s law of the heart and a modified time-varying elastance model. Different mechanical ventilatory and circulatory devices have been implemented in the platform including thoracic artificial lung, ECMO, IABP, pulsatile and continuous right and left ventricular assist devices, biventricular pacemaker and biventricular assist devices. CARDIOSIM© is used in clinical and educational environment.

The fundamental challenge for the structural design of complicated shapes and lightweight structures such as Tensile Membrane Structures (TMS) is a reliable estimation of lateral loading such as wind load. Wind pressure coefficients can’t be correctly calculated by standards for a large range of intricate forms of structures. Computational Fluid Dynamics (CFD), a strong computational technique for evaluating wind pressure distribution on complicated geometry, can be used to determine wind-related parameters. The average wind related-distributions for the tensile membrane surface are validated with a few contributions from prior studies, which are important to validate with benchmarks in experimental wind tunnel experiments. For the double-curved surface, the results show good agreement between experimental tests and CFD simulations. In this study, CFD simulation was used to calculate the mean surface pressure coefficient (Cp) for generic shapes and tensile membrane forms using the steady Reynolds-Averaged Navier-Stokes (RANS) technique. Wind force coefficients and the effect of wind directions are also explored across a wide range of classical and tensile forms. The purpose of the current research is to look at the effect of wind assault angle on the Cp distribution for tensile membrane structures and general shapes.

The treatment of critically ill patients remains an evolving and controversial issue. Mechanical circulatory support through a percutaneous approach for the management of cardiogenic shock has taken place in recent years. The combined use of IABP and Impella 2.5 device may have a role to play for this group of patients. A simulation approach may help with a quantitative as-sessment of the achievable degree of assistance. In this paper, we analyse the interaction be-tween Impella 2.5 pump applied as LVAD and IABP using the numerical simulator of the cardi-ovascular system developed in our laboratory. Starting with pathological conditions reproduced using literature data, the simulations were performed setting different rotational speeds for the LVAD and driving the IABP in full mode (1:1) or partial mode (1:2, 1:3 and 1:4). The effects in-duced by drug administration during the assistance were simulated too. The haemodynamic pa-rameters under investigation were: mean aortic pressure, systolic and diastolic aortic pressure, mean pulmonary arterial pressure, mean left and right atrial pressure, cardiac output, cardiac index, left and right ventricular end-systolic volume, left ventricular end-diastolic volume and mean coronary blood flow. The energetic variables considered in the study were: left and right ventricular external work and left and right atrial pressure-volume area. The outcome of our simulations shows that the combined use of IABP and Impella 2.5 achieves adequate support in the acute phase of cardiogenic shock compared to each standalone device. This would allow fur-ther stabilisation and transfer to a transplant centre should escalation of treatment be required.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The Oxford English Dictionary includes 17 definitions for the word “model” as a noun and other 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For in-stance, “model railways” refer to replicas of railways and trains at a smaller scale and a “model student” refers to an exemplar individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning of a model. Even if the context is narrowed, specifically to the research related to the tumour microenvironment, a “model” can be understood in a wide variety of ways, from an animal model, to a mathematical expression. This paper presents a review of the different “models” of the tumour microenvironment grouped by the different def-initions of the word into four categories: model organisms, in vitro models, mathematical models, and computational models. Then, the frequency of different meanings of the word “model” related to the tumour microenvironment is measured from the number of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequency of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, in particular xenografts and mouse models are the most common used “models”, the number of entries have been slowly de-creasing. Mathematical models, prognostic and risk models follow in frequency and these have been growing in use.

The eruption of COVID-19 patients in 215 countries worldwide has urged for robust predictive methods that can detect as early as possible the size and duration of the contagious disease and also providing precision predictions. In much recent literature reported on COVID-19, one or more essential parts of such investigation were missed. One of the crucial elements for any predictive method is that such methods should fit simultaneously as much data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases, and deceased cases, and so on. Other crucial elements include sensitivity and precision of such predictive methods on the amount of data as the contagious disease evolved day by day. To show the importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on the development of COVID-19 in Kuwait. It is observed that the SIRD model quickly picks up the main trends of COVID-19 development, but the Gaussian-SIRD model provides precise prediction a longer period of time.

In Model-Driven Development (MDD), the models, their generation, and imposing changes on them (model transformation) are used for the development of software. Models provide a framework to start from the imagination and abstraction to create and accomplish the final system. Models create a slow and steady transition from whatness to howness, i.e. from the natural path of the generation of software. For supporting this path, the Logic and Functionality of software must be changeable during its evolution. Here we provide a brief introduction to the concept of Model Driven Development.

Assuming a Winterberg model for space where the vacuum consists of a very stiff two component superfluid made up of positive and negative mass planckions, theory is the hypothesis, that Planck charge, , was created at the same time as Planck mass. Moreover, the repulsive force that like-mass planckions experience is, in reality, due to the electrostatic force of repulsion between like charges. These forces also give rise to what appears to be a gravitational force of attraction between two like planckions, but this is an illusion. In reality, gravity is electrostatic in origin if our model is correct. We determine the spring constant associated with planckion masses, and find that, , where equals Apery's constant, , and, , is the relaxed, i.e., , number density of the positive and negative mass planckions. In the present epoch, we estimate that, equals, , and the relaxed distance of separation between nearest neighbor positive, or negative, planckion pairs is, These values were determined using box quantization for the positive and negative mass planckions, and considering transitions between energy states, much like as in the hydrogen atom. For the cosmos as a whole, given a net smeared macroscopic gravitational field of, , due to all the ordinary, and bound, matter contained within the observable universe, an average displacement from equilibrium for the planckion masses is a mere , within the vacuum made up of these particles. On the surface of the earth, where, , the displacement amounts to, . All of these displacements are due to increased gravitational pressure within the vacuum, which in turn are caused by applied gravitational fields. The gravitational potential is also derived and directly related to gravitational pressure.

Stemming from the urgency of the actual situation, commercial banks need an effective credit risk management tool to limit risks. The authors went to survey, study and propose a set of factors affecting the ability of debt repayment of individual customers and conducting surveys. The topic uses data sets including 240 observation samples. Using the SPSS software to clean data and run the model based on Maddala's Binary logistics regression published in 1984 to find out the impact of each individual element of customers affecting their ability to repay such debts. Come on. The authors also specify the order of influence of each factor determining the ability to repay individual customers, thereby helping bank managers have a better visual view to make decisions for borrowing accurately, limiting risks.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe "what" has to be done instead of "how". Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regressive model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept “adaptive” of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In order words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

Objective: To use mathematical models to predict the epidemiological impact of lifting the lockdown in London, UK, and alternative strategies to help inform policy in the UK. Methods: A mathematical model for the transmission of SARS-CoV2 in London. The model was parametrised using data on notified cases, deaths, contacts, and mobility to analyse the epidemic in the UK capital. We investigated the impact of multiple non pharmaceutical interventions (NPIs) and combinations of these measures on future incidence of COVID-19. Results: Immediate action at the early stages of an epidemic in the affected districts would have tackled spread. While an extended lockdown is highly effective, other measures such as shielding older populations, universal testing and facemasks can all potentially contribute to a reduction of infections and deaths. However, based on current evidence it seems unlikely they will be as effective as continued lockdown. In order to achieve elimination and lift lockdown within 5 months, the best strategy seems to be a combination of weekly universal testing, contact tracing and use of facemasks, with concurrent lockdown. This approach could potentially reduce deaths by 48% compared with continued lockdown alone. Conclusions: A combination of NPIs such as universal testing, contact tracing and mask use while under lockdown would be associated with least deaths and infections. This approach would require high uptake and sustained local effort but it is potentially feasible as may lead to elimination in a relatively short time scale.

This paper describes a memory model with 2 levels of information. The lower-level stores source data, is Markov-like and unweighted. Then an upper-level ontology is created from a further 3 phases of aggregating source information, by transposing from an ensemble to a hierarchy at each level. The ontology is useful for search processes and the aggregating process transposes the information from horizontal set-based sequences to more vertical typed-based clusters. The base memory is essentially neutral, where any weighted constraints or preferences should be sent by the calling module. This therefore allows different weight sets to be imposed on the same linking structure. The success of the ontology typing is open to interpretation, but the author would suggest that when clustering text, the result was types based more on use and context, for example, 'linking' with 'structure' or 'provide' with 'web,' for a document describing distributed service-based networks. This allows the system to economise over symbol use, where links to related symbols will be clustered together. The author then conjectures that a third level would be more neural in nature and would include functions or operations to be performed on the data, along with related memory information.

Brain models present a viewpoint on the fundamental structural components of the brain and their mutual organization, generally relative to a particular concept of the brain axis. A model may be based on adult brain structure or on developmental morphogenetic aspects. Brain models usually have functional implications, depending on which functional properties derive from the postulated organization. This essay examines the present scenario about brain models, emphasizing the contrast between columnar or other longitudinal models and transverse subdivisional neuromeric models. In each case, the main functional implications and apparent problems are explored and commented. Particular attention is given to the modern molecularly based ‘prosomeric model’, which postulates a set of 20 transverse prosomeres as the developmental units that serve to construct all the cerebral parts and the particular typology of many different neuronal populations within the forebrain and the hindbrain, plus a number of additional spinal cord units. These metameric developmental units (serially repeated, but with unique molecular profiles) confer to this model remarkable functional properties based mainly on its multiplicity and modularity. Many important brain functions can be decomposed into subfunctions attended to by combined sets of neuronal elements derived from different neuromeres. Each neuromere may participate in multiple functions. Most aspects related to creation of precise order in neural connections (axonal navigation and synaptogenesis) and function is due to the influence of neuromeric anteroposterior and dorsoventral positional information. Research on neuromeric functionality aspects is increasing significantly in recent times.

Small island developing states (SIDS) traditionally face a set of challenges like the narrow economic structure, environmental issues, and dependence on a few ranges of economic activities forcing them to open the economy to the exterior. Therefore, their development model, like in Cape Verde depends on migration, remittances, dependence on aid, tourism, and state employment. The current research offers an insight about the nature of Cape Verde’s economy as a SIDS and the degree to which its economy depends on tourism and government services, both of which are supported through aid programs. Understanding Cape Verde’s development model is important to clarify the challenges the country faces, and its development needs to gather a long-term resilience and to understand if it is changing from a MIRAB (Migrations, Remittances, Aid and Bureaucracy) model into another one.

In China, the majority of mountainous regions are characterized by complex topography and a delicate, sensitive geological environment. Coupled with a generally underdeveloped infrastruc-ture and numerous unreasonable human engineering activities, these regions are often highly susceptible to geological disasters. Geological hazards can cause significant damage to human lives and property, impeding the development of mountainous areas. Consequently, researching the assessment of geological hazard vulnerability is crucial for disaster prevention, emergency management, and economic development in these regions. This study focuses on Xuanwei City and selects eight factors for evaluation, including elevation, gradient, slope aspect, normalized vegetation index, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads. These factors are chosen based on a comprehensive analysis of the spatial and tem-poral distribution of geological hazards and disaster incubation conditions. Two paired models, the deterministic coefficient model + logistic regression model (CF+LR) and the information quan-tity model + logistic regression model (I+LR), were employed to quantitatively assess the study ar-ea. The accuracy of these models was evaluated using ROC curves and AUC values. The results indicate that: (1) The AUC values for the CF+LR and I+LR coupled models are 0.799 and 0.772, respectively, demonstrating that both models can objectively and reliably assess the vulnerability to geological hazards in the study area; (2) Based on the CF+LR model calculations, the geological hazard susceptibility of Xuanwei City can be categorized into four zones: extremely high suscepti-bility (6.09%), high susceptibility (31.08%), medium susceptibility (32.26%), and low susceptibility (30.57%); (3) The CF+LR model more accurately represents the evaluation results and offers a strong reference value.

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modelling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modelling frameworks have proved useful for explaining ecosystem response to perturbations, while requiring fewer information and providing more general predictions. However, current qualitative methods have some shortcomings which may limit their utility for specific issues. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modelling framework based on "if-then" rules which generates many alternative event sequences (trajectories). Based on expert knowledge, observations and literature, we use this framework to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups had highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modelling.

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

Nowadays, network simulators are very useful to model a communications system. In this technical note we will focus in the creation of a new error model in NS-3 network simulator. This note describes the main steps to create or modify an Wi-Fi error model in this network simulator. In our case, we have created a new error model to included the approach of [1] to compute PER (Packet Error Rate) for vehicular environments.

Aiming at the defects of low precision and time cumulative error, an external wireless signal weighted centroid localization algorithm aided inertial positioning method is designed in this paper. According to the signal strength of each anchor node received at the test point, the distance between the anchor node and the anchor node is obtained by using the attenuation model of the wireless signal. Three anchor nodes are used to measure the distance between the anchor node and the measured point. We can obtain the area to be measured according to the actual situation, the position of the measured point is obtained by the weighted centroid localization algorithm and a combined model of wireless signal aided inertial navigation system is established. The simulation results show that the method can greatly improve the positioning accuracy and restrain the divergence of the longitude error and latitude error.

A new two-dimensional nanomaterial – Titanium Carbide MXene (Ti₃C₂-MXene) – was reported in 2011. In this work, the microscale models of Ti₃C₂-MXene nanomaterial are considered with polymer matrix. The nanocomposites are modeled using nacre-mimetic brick-and-mortar assembly configurations due to enhanced mechanical properties and interlocking mechanism between the Ti₃C₂-MXene (brick) and polymer matrices (mortar). The polymer matrix material (Epoxy-resin) is modeled with elastic and viscoelastic behavior (Kelvin-Voigt Model). The Finite Element Method is used for numerical analysis of the microscale models with the multi-point constraint method to include Ti₃C₂-MXene fillers in the polymer matrix. Ti₃C₂-MXenes are considered as thick plate elements with transverse shear effects. The response of elastic and viscoelastic models of polymer matrix are studied. Finally, a tensile and compressive load is applied at the microscale and the effective load transfer due to nacre-mimetic configuration is discussed. This paper provides nacre-mimetic models to pre-design the nanocomposite for optimal performance with damage resistance and enhanced strength.

The comparison of group means in latent variable models plays a vital role in empirical research in the social sciences. The present article discusses extensions of invariance alignment and Haberman linking concerning the choice of linking functions for comparisons of many groups. Robust linking functions are proposed for invariance alignment and robust Haberman linking that are particularly suited to item response data under partial invariance. In a simulation study, it is shown that both linking approaches have comparable performance, and in some conditions, the newly proposed robust Haberman linking outperforms invariance alignment.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.