Variations In Input Parameters Research Articles

Experimental probe on machining attributes of Al 7075-T6/SiC/crumb rubber/MoS2-based green hybrid composite using artificial neural network model

Machinability of hybrid metal matrix composite is quite difficult because of an anisotropic nature, abrasive effect, and nonhomogeneous arrangement of two (or more) hard reinforcements, which are infused in an unreinforced base alloy. Therefore, this paper proposes an experimental probe on machining attributes of the novel green hybrid metal matrix composite which is prepared by an advanced vacuum-sealed bottom pouring stir casting technique with Al7075-T6 as a base matrix and SiC/crumb rubber/MoS2 as three distinct reinforcements. The variable input parameters such as SiC content (3.5 and 4.5 wt.%), crumb rubber content (0.3, 0.6, and 0.9 wt.%), MoS2 content (3.5, 4.5, and 5.5 wt.%), stirring speed (580, 600 and 620 rpm), stirring time (2, 5 and 8 minutes), and pouring temperature (670, 700, and 730°C) are selected for the fabrication of hybrid composites via Taguchi L18 mixed-level orthogonal array. For the machinability of fabricated composites in terms of material removing rate and surface roughness under dry conditions, a conventional turning operation on a lathe machine was opted for fixed input parameters such as spindle speed (200 rpm), depth of cut (1.0 mm), feed rate (0.25 mm/rev), and machining time (30 seconds). In contrast to an unreinforced base alloy, the material removing rate of the proposed green hybrid composite is slightly increased by 1.47%, whereas surface roughness is significantly decreased by 12.14%. Further, these experimental outcomes are validated with the model created using an artificial neural network, which is based on Levenberg–Marquardt algorithm. Besides, the predicted values obtained from the artificial neural network model lie in the close vicinity of the experimental outcomes, which in turn ensure the authentication of the developed artificial neural network model for the machined aluminum-based green hybrid composites.

Application of Fractal Image Analysis by Scale-Space Filtering in Experimental Mechanics.

Increasingly complex numerical analyses require more and more precise, accurate and varied input parameters in order to achieve results that are as realistic and reliable as possible. Therefore, experimental analyses for material parameter identification are of high importance and a driving force for further developments. In this work, opportunities by applying fractal analysis to optical measurement data of a shear cutting process are investigated. The fractal analysis is based on a modification of the concept of scale-space filtering. Scale exponent fields are calculated for the image sequences of the shear cutting process that are taken by a mobile microscope. A least-square approximation is used for the automated evaluation of the local scale exponent values. In order to determine the change of the scale exponent of individual material points, a digital image correlation is applied.

Assessing key factors for sporting industry sustainable development through multilayer artificial perceptron neural network approach.

Sustainable development (SD) has recently emerged as a critical axis in industrial strategic management and debate. Apart from the scarcity of resources, the influence of economic difficulties on human life and society has increased the relevance of SD. However, estimating and analyzing the SD of any business are still in its infancy. Thus, the SD of the sports goods industry is assessed using a multilayer artificial perceptron neural network (MLP) technique employing eight distinct characteristics connected to production efficiency, international and internal environment, export, and domestic development by using Statistica software. To discover an ideal model for SD in the sports goods business, stepwise models with varied input parameters were built. The identity activation function was used to create and train an error propagation algorithm-based network. The results of various eight network models revealed that, among other models, the accuracy of our fifth model, which incorporates production efficiency, international and internal environment, strategic management, and domestic distribution organization, gives the best answer. This model has the lowest root-mean-square error (RMSE) = 0.002 and the greatest R square = 0.922, demonstrating its efficacy and effectiveness in terms of SD. Based on these findings, it is possible to conclude that using artificial neural networks is beneficial in determining an optimal solution for SD and other similar problems since this tool is important and would be utilized for forecasting and decision-making in any business.

Integrated Stochastic Life Cycle Assessment and Techno-Economic Analysis for Shrub Willow Production in the Northeastern United States

The refereed literature contains few studies that analyze life cycle assessment (LCA) and techno-economic analysis (TEA) methodologies together for lignocellulosic bioenergy systems, using a stochastic modeling approach. This study seeks to address this gap by developing an integrated framework to quantify the environmental and financial impacts of producing and delivering shrub willow in the Northeastern United States. This study analyses four different scenarios from a combination of two different initial land cover types (grassland, cropland) prior to willow establishment, and two harvesting conditions (leaf-on, leaf-off). Monte Carlo simulations were performed to quantify the uncertainty of the results based on a range of financial, logistical, and biophysical variable input parameters (e.g., land rental rates, transportation distance, biomass yield, etc.). Growing willow biomass on croplands resulted in net negative GHG emissions for both leaf on and leaf off scenarios for the baseline. The GHG emissions were lowest for the leaf-off harvest on cropland (−172.50 kg CO2eq Mg−1); this scenario also had the lowest MSP ($76.41 Mg−1). The baseline grassland scenario with leaf-on harvest, results in the highest net GHG emissions (44.83 kg CO2eq Mg−1) and greatest MSP ($92.97 Mg−1). The results of this analysis provide the bioenergy field and other interested stakeholders with both environmental and financial trade-offs of willow biomass to permit informed decisions about the future expansion of willow fields in the landscape, which have the potential to contribute to GHG reduction targets and conversion into fuels, energy, or bioproducts for carbon sequestration and financial benefits.

Evaluation of Subcritical Organic Rankine Cycle by Pure and Zeotropic of Binary and Ternary Refrigerants

The simulation configuration and process analysis of the Subcritical Organic Rankine Cycle (SORC) system are carried out for the potential comparison between pure, binary, and ternary zeotropic mixtures of R1234ze(E), R1234yf, and R134a as refrigerant working fluids based on applying the flue gas as a heat source with medium temperature. The compression pressure was selected as an optimized variable input parameter of SORC with the lower limit of boundary condition (1.4 MPa); to mitigate air ingress and sub-atmospheric pressure that led to approach optimum net power output generated. Increasing the compression pressure has a positive relationship with the superheated temperature and the mass enthalpy change in the evaporation and, therefore, in the expansion process. In parallel, the enthalpy and entropy changes in the flue gas and cold water positively correlate with exergy efficiency. So, R1234ze(E)/ R1234yf/R134a with 68.35% and R1234yf/ R134a with 69.29% as the lowest and highest exergy efficiency in the highest compression pressure; furthermore, the SIC consequences of increasing the cost of each component of the SORC system that has a direct relationship with the PPC and the required exchanger area of evaporation and condensation process and generating a net power output of the turbine. As a result, the maximum to the minimum value of specific investment cost (SIC) achieves R134a with 5807402.18-22455670.61 $.kW-1 and R1234yf with 16.82-17.38% reduction, respectively. To sum up, the lowest payback period (PBP) was R1234yf with 302 days.

Optimal Operation of an Integrated Hybrid Renewable Energy System with Demand-Side Management in a Rural Context

A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB© environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system’s performance.

Fractional order mathematical model of monkeypox transmission dynamics

In this paper, we present a deterministic mathematical model of monkeypox virus by using both classical and fractional-order differential equations. The model includes all of the possible interactions that contribute to disease spread in the population. We investigate the model's stability results in the disease-free case when R 0 < 1. When R 0 < 1, we show that the model is stable, otherwise it is unstable. To obtain the best fit that describes the dynamics of this disease in Nigeria, the model is fitted using the nonlinear least square method on cumulative reported cases of monkeypox virus from Nigeria between January to December 2019. Furthermore, adequate conditions for the existence and uniqueness of the solution of the model have been proved. We run numerous simulations of the proposed monkeypox model with varied input parameters to investigate the intricate dynamics of monkeypox infection under the effect of various system input parameters. We investigate the system's dynamical behavior to develop appropriate infection control policies. This allows the public to understand the significance of control parameters in the eradication of monkeypox in the population. Lowering the order of fractional derivatives has resulted in significant modifications. To the community's policymakers, we offered numerous parameters for the control of monkeypox.

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

The convective heat transfer in non-Newtonian fluid flow in the presence of temperature stratification, heat generation, and heat absorption effects is debated by using artificial neural networking. The heat transfer rate is examined for the four different thermal flow regimes namely (I) thermal flow field towards a flat surface along with thermal radiations, (II) thermal flow field towards a flat surface without thermal radiations, (III) thermal flow field over a cylindrical surface with thermal radiations, and (IV) thermal flow field over a cylindrical surface without thermal radiations. For each regime, a Nusselt number is carried out to construct an artificial neural networking model. The model prediction performance is reported by using varied neuron numbers and input parameters, and the results are assessed. The ANN model is designed by using the Bayesian regularization training procedure, and a high-performing MLP network model is used. The data used in the creation of the MLP network was 80 percent for model training and 20 percent for testing. The graph shows the degree of agreement between the ANN model projected values and the goal values. We discovered that an artificial neural network model can provide high-efficiency forecasts for heat transfer rates having engineering standpoints. For both flat and cylindrical surfaces, the heat transfer normal to the surface reflects inciting nature towards the Prandtl number and heat absorption parameter, while the opposite is the case for the temperature stratification parameter and heat generation parameter. It is important to note that the magnitude of heat transfer is significantly larger for Flow Regime-IV in comparison with Flow Regimes-I, -II, and -III.

Pion couplings with low-lying nucleon resonances

We have calculated coupling constants of a neutral pion with the lowest two nucleon resonances. This includes both the diagonal as well as non-diagonal coupling constants involving a nucleon resonance and a nucleon. For this, we first calculate vacuum-to-pion correlation function of the interpolating fields of two nucleons and then take its matrix elements with respect to a nucleon spinor and/or a nucleon resonance spinor(s). Using different QCD sum rules obtained from different matrix elements we eliminate unwanted coupling constants and solve for the desired ones. This is a simple extension of the projected correlation function approach used in the literature where we use multiple of states involving those of a nucleon and its resonances. We have also checked the stability of our results with respect to variation of different QCD and phenomenological input parameters.

Digitally CEC, electrolytes and others with temperature may determine every phenology in fisheries and anthropogenics

This present communication delt, Inputs variability of major input parameters with temperature may possible with given example of lower stretches of river Ganges of fisheries and other biological importances. Earlier we found that soil cation exchange capacity CEC has a significant impacts on biology. Any tallness lead to lower CEC and dwarfness lead to higher CEC applicable for both plants and animals. Both growth and reproduction negetively correlated with CEC. in lower CEC higher Isoprene is synthesiesd and resulting diseases less growth and survibal. In long run all hydrocarbon derivatives, petrolium can be prevailing in low CEC zones. We often emphasise environment may be the key in controlling growth and properity. Temperature being the controlling parametsers of many environmental inputs to the spoecies of plants and animals in aquatic environment. Authors has found some machine learning techniques to estimate or identify environmental input parameters with changing temperature perpetually.

Gaussian Processes for Transfer Path Analysis Applied on Vehicle Body Vibration Problems

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations. For this purpose, Gaussian processes are introduced to find a probabilistic function approximation of the transfer paths. Initial results reveal that a wider solution space can be explored by only observing a few transfer path samples. This entails a time-efficient and robust technique, which inherently incorporates the variability of the input parameters. As such, Gaussian processes offer a versatile solution strategy for transfer path analyses, where simulation data as well as experimental measurements can be holistically investigated.&lt;/div&gt;&lt;/div&gt;

Cost-Effectiveness Analysis of Evolocumab in Adult Patients with Atherosclerotic Cardiovascular Disease in Canada.

IntroductionTo evaluate the cost-effectiveness of evolocumab when added to standard of care lipid-lowering treatment (LLT) for patients with atherosclerotic cardiovascular disease (ASCVD) who cannot adequately control their low-density lipoprotein cholesterol (LDL-C) despite optimized LLT in Canada.MethodsAn incremental cost-utility analysis was conducted using a Markov cohort state transition model adapted to the Canadian setting. Analyses were conducted from a public health and societal perspective using a lifetime time horizon for Canada. Scenario analyses were conducted on the basis of recommendations from the 2021 Canadian Cardiovascular Society (CCS) dyslipidemia guidelines.ResultsIn ASCVD patients with prior myocardial infarction (MI) and baseline LDL-C ≥ 1.8 mmol/L, adding evolocumab to optimized statin therapy with or without ezetimibe is associated with an incremental cost per quality-adjusted life year (QALY) gained of $66,453 CAD. Furthermore, for every 100 patients treated with evolocumab for lifetime, adding evolocumab to optimized LLT will prevent approximately 52 cardiovascular (CV) events, of which seven would be fatal. The results are generally robust using univariate and simultaneous variation in model input parameters. Scenario analyses for patient populations as per the CCS guidelines suggest that evolocumab added to optimized LLT may be considered cost-effective, given an incremental cost-effectiveness ratio (ICER) threshold of CAD$100,000 per QALY gained. Limitations associated with this analysis should be interpreted in the context of data and modeling assumptions used.ConclusionOverall, this analysis supports reimbursement of evolocumab by payers in patients with ASCVD who cannot reach LDL-C thresholds despite optimized LLT to reduce unnecessary fatal and non-fatal CV events.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12325-022-02130-4.

Parameter optimization and experimental validation of A-TIG welding of super austenitic stainless steel AISI 904L using response surface methodology

Super austenitic stainless steel (SASS) is a premium stainless steel (SS) variant which finds applications in challenging environments. One of the premium grades of SS that falls under the said category is AISI 904L. Autogenous activated flux tungsten inert gas (A-TIG) welding is an established technique for fabricating sound steel joints which offers various advantages. In this context, this research aims to identify an optimum set of input process parameters to effectively employ A-TIG welding to fabricate 10 mm thick joints of SASS AISI 904L plates by employing bead on plate welding trials. The input parameters varied for the trials are welding current, torch travel speed, and percentage of Argon gas present in a mixture of Argon and Helium – which is the shielding gas used, while the responses were the depth of weld penetration (DOP) and weld bead width (BW). For finalizing the combinations of input parameters and the run order of the welding trials, the design of experiments (DOE) was performed using central composite design (CCD) of response surface methodology (RSM). After performing the trials, the DOP and BW attained were measured and the data thus generated were analyzed to study the influence of varying input parameters on the two responses. Analysis of variance (ANOVA) was then performed to check the input parameter's level of significance. RSM's desirability approach was used for numerical optimization to attain maximum DOP and a moderate BW. A linear regression equation generated can be used to precisely predict DOP and BW for any random values of input parameters. The bead on plate trial's validation study displayed root mean squared error (RMSE) values of 0.328 and 0.653 for DOP and BW respectively, The RMSE values of DOP and BW of the joint fabricated with optimized parameters was 0 and 0.311 respectively.

3-D spatial cluster analysis of seismic sequences through density-based algorithms

SUMMARY With seismic catalogues becoming progressively larger, extracting information becomes challenging and calls upon using sophisticated statistical analysis. Data are typically clustered by machine learning algorithms to find patterns or identify regions of interest that require further exploration. Here, we investigate two density-based clustering algorithms, DBSCAN and OPTICS, for their capability to analyse the spatial distribution of seismicity and their effectiveness in discovering highly active seismic volumes of arbitrary shapes in large data sets. In particular, we study the influence of varying input parameters on the cluster solutions. By exploring the parameter space, we identify a crossover region with optimal solutions in between two phases with opposite behaviours (i.e. only clustered and only unclustered data points). Using a synthetic case with various geometric structures, we find that solutions in the crossover region consistently have the largest clusters and best represent the individual structures. For identifying strong anisotropic structures, we illustrate the usefulness of data rescaling. Applying the clustering algorithms to seismic catalogues of recent earthquake sequences (2016 Central Italy and 2016 Kumamoto) confirms that cluster solutions in the crossover region are the best candidates to identify 3-D features of tectonic structures that were activated in a seismic sequence. Finally, we propose a list of recipes that generalizes our analyses to obtain such solutions for other seismic sequences.

Metamodeling of a deep drawing process using conditional Generative Adversarial Networks

Optimization tasks as well as quality predictions for process control require fast responding process metamodels. A common strategy for sheet metal forming is building fast data driven metamodels based on results of Finite Element (FE) process simulations. However, FE simulations with complex material models and large parts with many elements consume extensive computational time. Hence, one major challenge in developing metamodels is to achieve a good prediction precision with limited data, while these predictions still need to be robust against varying input parameters. Therefore, the aim of this study was to evaluate if conditional Generative Adversarial Networks (cGAN) are applicable for predicting results of FE deep drawing simulations, since cGANs could achieve high performance in similar tasks in previous work. This involves investigations of the influence of data required to achieve a defined precision and to predict e.g. wrinkling phenomena. Results show that the cGAN used in this study was able to predict forming results with an averaged absolute deviation of sheet thickness of 0.025 mm, even when using a comparable small amount of data.

Supervised learning for accurate mesoscale simulations of suspension flow in wall-bounded geometries

Herein, we have employed a supervised learning approach combined with Core-Modified Dissipative Particle Dynamics Simulations (CM-DPD) in order to develop and design a reliable physics-based computational model that will be used in studying confined flow of suspensions. CM-DPD was recently developed and has shown promising performance in capturing rheological behavior of colloidal suspensions; however, the model becomes problematic when the flow of the material is confined between two walls. Wall-penetration by the particles is an unphysical phenomenon that occurs in coarse-grained simulations such as Dissipative Particle Dynamics (DPD) that mostly rely on soft inter-particle interactions. Different solutions to this problem have been proposed in the literature; however, no reports have been given on how to deal with walls using CM-DPD. Due to complexity of interactions and system parameters, designing a realistic simulation model is not a trivial task. Therefore, in this work we have trained a Random Forest (RF) for predicting wall penetration as we vary input parameters such as interaction potentials, flow rate, volume fraction of colloidal particles, and confinement ratio. The RF predictions were compared against simulation tests, and a sufficiently high accuracy and low errors were obtained. This study shows the viability and potentiality of ML combined with DPD to perform parametric studies in complex fluids.

Study of Gas-to-Liquid Heat Pipe Heat Exchanger

This study is focused on the study and development of a gas-to-liquid heat pipe heat exchanger (HPHE) based on numerical and experimental analysis. Stainless steel heat pipes were installed inside the heat exchanger in the form of three equilateral triangles, staggered into a hexagonal configuration to simulate the waste heat recovery from hot exhaust gas to a water flow. The first main aim of this study was focused on 3D design and numerical analysis, which were used to create and calculate the effect of similar input conditions on the overall system. The system was tested for the overall heat transfer by measuring the temperature change in both fluids. The heat transfer and overall average temperature were used to calculate the effectiveness of the system. In the second part of this study, a test of the waste heat recovery was undertaken with this setup, using water as the cooling fluid. The study was conducted with different input velocities and temperatures of waste hot air, controlled simultaneously by the input fan and air heater, whereas the cooling water was kept at a steady state of 30 °C and 0.0156 kg/s at the input. The hot air velocity was controlled by fans with different inlet air velocities of 0.3 m/s, 0.5 m/s, and 0.7 m/s. Moreover, the temperature of the air was changed from 150 °C to 250 °C with a step of 25 °C. The increase in temperature and the velocity of air was directly proportional to the amount of heat transferred from the air to the cooling water, and the effectiveness was also found to be inversely proportional to both of the varying input parameters. The numerical study showed a maximum increase of 12% in the heat transfer. The output temperatures of hot and cold fluids showed maximum increases of 7 K and 3 K, respectively. The numerical system with such input parameters can be evaluated further to predict the behavior of changes in the design and parameters.

A quantitative approach to the spread of variance in translational research using Monte Carlo simulation

The translation of promising preclinical research into successful trials often fails. One contributing factor is the “Princess and the Pea” problem, which refers to how an initially significant effect size dissipates as research transitions to more complex systems. This work aimed to quantify the effects of spreading variability on sample size requirements. Sample size estimates were performed by Monte Carlo simulation. To simulate the process of progressing from preclinical to clinical studies, nested sigmoidal dose–response transformations with modifiable input parameter variability were used. The results demonstrated that adding variabilty to the dose–response parameters substantially increases sample size requirements compared to standared calculations. Increasing the number of consecutive studies further increases the sample size. These results quantitatively demonstrate how the spread of variability in translational research, which is not typically accounted for, can result in drastic increases in the sample size required to maintain a desired study power.

Experimental validation of 4D log file-based proton dose reconstruction for interplay assessment considering amplitude-sorted 4DCTs.

The unpredictable interplay between dynamic proton therapy delivery and target motion in the thorax can lead to severe dose distortions. A fraction-wise four-dimensional (4D) dose reconstruction workflow allows for the assessment of the applied dose after patient treatment while considering the actual beam delivery sequence extracted from machine log files, the recorded breathing pattern and the geometric information from a 4D computed tomography scan (4DCT). Such an algorithm capable of accounting for amplitude-sorted 4DCTs was implemented and its accuracy as well as its sensitivity to input parameter variations was experimentally evaluated. An anthropomorphic thorax phantom with a movable insert containing a target surrogate and a radiochromic film was irradiated with a monoenergetic field for various 1D target motion forms (sin, sin4 ) and peak-to-peak amplitudes (5/10/15/20/30mm). The measured characteristic film dose distributions were compared to the respective sections in the 4D reconstructed doses using a 2D γ-analysis (3mm, 3%); γ-pass rates were derived for different dose grid resolutions (1mm/3mm) and deformable image registrations (DIR, automatic/manual) applied during the 4D dose reconstruction process. In an additional analysis, the sensitivity of reconstructed dose distributions against potential asynchronous timing of the motion and machine log files was investigated for both a monoenergetic field and more realistic 4D robustly optimized fields by artificially introduced offsets of ±1/5/25/50/250ms. The resulting dose distributions with asynchronized log files were compared to those with synchronized log files by means of a 3D γ-analysis (1mm, 1%) and the evaluation of absolute dose differences. The induced characteristic interplay patterns on the films were well reproduced by the 4D dose reconstruction with 2D γ-pass rates ≥95% for almost all cases with motion magnitudes ≤15mm. In general, the 2D γ-pass rates showed a significant decrease for larger motion amplitudes and increase when using a finer dose grid resolution but were not affected by the choice of motion form (sin, sin4 ). There was also a trend, though not statistically significant, toward the manually defined DIR for better quality of the reconstructed dose distributions in the area imaged by the film. The 4D dose reconstruction results for the monoenergetic as well as the 4D robustly optimized fields were robust against small asynchronies between motion and machine log files of up to 5ms, which is in the order of potential network latencies. We have implemented a 4D log file-based proton dose reconstruction that accounts for amplitude-sorted 4DCTs. Its accuracy was proven to be clinically acceptable for target motion magnitudes of up to 15mm. Particular attention should be paid to the synchronization of the log file generating systems as the reconstructed dose distribution may vary with log file asynchronies larger than those caused by realistic network delays.

Effect of vaccination on non-integer dynamics of pneumococcal pneumonia infection

Pneumococcal pneumonia is a contagious, possibly fatal bacterial lung infection that can strike at any time and in any place. It can land you in the hospital and possibly put your life in jeopardy in severe situations. Pneumonia can range from a minor illness to a major or life-threatening infection and it can even be fatal. Therefore, it is of great importance to conceptualize the transmission phenomena of this dangerous infection and to provide control interventions. Here, an epidemic model to conceptualize the transmission phenomena of pneumococcal pneumonia with vaccination and treatment factors is structured. The equilibria of the model are inspected and the reproduction parameter (indicated by ℛ0) is determined. Stability results for the equilibria of the system have been proved. The reproduction parameters of the scheme are analyzed numerically with the variation of various input parameters. Moreover, adequate conditions for the uniqueness and existence of the solution to the hypothesized problem of pneumococcal pneumonia infection have been proved. Different simulations of the recommended pneumococcal pneumonia model are performed by changing the input factors to study the complicated dynamics of pneumococcal pneumonia infection with the influence of different input parameters of the system. This article discusses the system's dynamic behavior in order to develop effective infection control policies. Important changes have been observed when the order of the fractional derivative is decreased. This study recommended different factors for the reduction of pneumococcal pneumonia to the policymakers in the community.