Natural calamities observation, study and simulation has always been a prime concern for disaster management agencies. Billions of dollars are spent annually to explore geo-seismic movements especially earthquakes but it has always been a unique accident. The real-time study of seismic waves, ground motions, and earthquakes always needed a programmable mechanical structure capable of physically producing the identical geo-seismic motions with seismology domain definitions. A programmable multi-parametric five degrees of freedom electromechanical seismic wave events simulation platform to study and experiment seismic waves and earthquakes realization in the form of geo-mechanic ground motions is exhibited in this work. The proposed platform was programmed and interfaced through an IoT cloud-based Web application. The geo-mechanics was tested in the range of i) frequencies of extreme seismic waves from 0.1Hz to 178Hz; ii) terrestrial inclinations from -10.000° to 10.000°; iii) velocities of 1km/s to 25km/s iv) variable arrival times 1us to 3000ms; v) magnitudes M1.0 to M10.0 earthquake; vi) epi-central and hypo-central distances of 290+ and 350+ kilometers. Wadati and triangulation methods have been used for entire platform dynamics design and implementation as one of key contributions in this work. This platform is as an enabler for a variety of applications such as training self-balancing and calibrating seismic-resistant designs and structures in addition to studying and testing seismic detection devices as well as motion detection sensors. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems.

In this paper an integrated methodology for the coupling between 1D- and 3D-CFD simulation codes is presented, which has been developed to support the design and calibration of new diesel engines. The aim of the proposed methodology is to couple 1D engine models, which may be available in the early-stage engine development phases, with 3D predictive combustion simulations, in order to obtain reliable estimates of engine performance and emissions for newly designed automotive diesel engines. The coupling procedure features simulations performed in 1D-CFD by means of GT-SUITE and in 3D-CFD by means of Converge, executed within a specifically designed calculation methodology. An assessment of the coupling procedure has been performed by comparing its results with experimental data acquired on an automotive Diesel engine, considering different working points including both part load and full load conditions. Different multiple injection schedules have been evaluated for part-load operation, including pre and post injections. The proposed methodology, featuring detailed 3D chemistry modeling, was proven to be capable to properly assess pollutant formation, specifically to estimate NOx concentrations. Soot formation trend was also well-matched for most of the explored working points. The proposed procedure can therefore be considered as a suitable methodology to support the design and calibration of new Diesel engines, thanks to its ability to provide reliable engine performance and emissions estimations from the early-stage of a new engine development.

The Gurney flap (GF) is a miniature lift-enhancement device and is usually mounted at the trailing edge of an airfoil. The GF and has been successfully applied to isolated airfoils, multi-element airfoils, and aircraft wings as well as helicopter rotor due to its attractive features of simplicity, cost-effective and separation control. The GF also has aroused the attention of researchers in the turbomachinery industry. However, limited studies are currently available on the application of a GF to an axial fan.Hence, in this paper, we conduct a wind tunnel and computational fluid dynamic (CFD) investigation on an axial fan profiled with a NACA 65-(12)10 airfoil to evaluate the effect of the GF on the performance of the fan. We also present the detailed flow features of the fan with and without the GF after validating the simulation results with the experimental results. The experimental results show that as the GF is installed higher on the fan blade, it can produce a higher total pressure rise accompanied with a greater loss of efficiency. The installation of the GF also enlarges the work capacity of the fan. Detailed flow field analysis, including the surface pressure distribution, vorticity distribution at the trailing edge and streamline distribution of the fan, is carried out to understand the mechanisms of the effect of the GF on the performance of the fan.

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

Background: Simulations have historically aided training programs by providing a realistic and holistic replication of professional scenarios and procedures. Simulations have developed over the past 40 years to include varying fidelities and modalities of simulation. Learning in a simulation-centered environment has benefits, ranging from improved patient care to specific skills acquisition while catering to students’ numerous and varied learning approaches. Simulation is a multifaceted field that benefits all parties, the teachers, the learners, and the patients. The application of simulation to medical education and its amalgamation with other modes and substitutes allows for a more integrated learning and testing curriculum that advances the current trajectory of medical education. Such developments, however, are limited to resource rich areas, leaving behind low-middle income countries to use traditional, less evolved methodologies and practices. This review aimed to explore different aspects of simulation and focus specifically on low-cost task trainers and their accessibility. Method: The purpose of the study was to assess the accessibility of low-cost task trainers in terms of cost-effectiveness, distribution, validation, and frequency within specific specialties. To do so, 84 PubMed publications have been screened, and 39 filtered research studies have collected the necessary data. After analyzing the papers, we classified the following information – process, specialization, validation (y/n), costs, development location, and year of publication. Results: After carefully analyzing the accumulated data from the selected 39 publications, we found that most studies (i.e., 6 out of 39) were published in 2020. Emergency Medicine was the most common specialty for which low-cost trainers were developed (9 out of 39 procedural simulators); Otolaryngology followed this with 8 out of 39 trainers and general surgery with 7/39 of the task-trainers. The price ranges fluctuated and fell within the price bracket of USD 0 to USD 400 collectively. Our review also uncovered the concentration of development of such innovations solely in high income countries (HICs). Conclusion: Simulation is an invaluable tool applicable to a diverse range of phases of medical education. Future conjunction of simulation with low-cost substitutes along with increased encouragement and enthusiasm towards developing cost effective simulation-based learning environments (SBLEs) with the reserves and requirements of these areas in mind may prove to be a reliable option for low and middle resource settings

Carrying out technological tests and research is indispensably connected with high costs and elongation of implementation time of new casting technologies and their modifications in industry. Hence, the application of specialised computer software, enabling the simulation and analysis of metallurgical and casting processes in modern foundry industry is of special interest. The present work presents an experimental application of MAGMASoft software in casting process of EV31A magnesium alloy structural element of helicopter engine. The results of computer simulation have been compared with industrial cast of the simulated element, fabricated in the same conditions. It has been revealed that the computer simulation provides a complex information on the analysed process, leading to reduction of costs and time, connected with technological tests in implementation of new casting technologies.

High-Rise buildings with their particular features can affects on surrounding environment and makes new microclimates. In the windy conditions, the spaces that are between building blocks changes to passages and affects on the wind velocity, intensity and it’s other parameters.The importance of this effect is different in each level of building height. The Pedestrian-Level is the lowest and one of important areas. Markets, playgrounds and pedestrian access had located in this area and any unwanted microclimate changes like high velocity and turbulence in this level can makes discomfort and dangerous condition for residents. So this research tries to consider the pedestrian- level wind comfort in some High-Rise building complexes arrangement that had located in Tehran district 22 with Computational Fluid Dynamics (CFD) modeling and reaching to a suitable arrangement pattern. It had collected the required data through field study and librarian databases and then compared them with standard guidelines and analyzed them by comparative comparison method. As a result a linear arrangement that placed crossover to wind direction for providing wind comfort and preventing wind danger is been suggested in this region.

The United Arab Emirates (UAE) is significantly dependent on desalinated water and groundwater resource, which is expensive and highly energy intensive. Despite the scarce water resource, only 54% of the recycled water was reused in 2015. In this study, an “Oasis” complex comprised of Sustainable Farming Compartments (SFCs) was proposed for reusing treated wastewater to decrease the ambient temperature of the SFC via an evaporative cooling system. A prototype SFC with half the original scale (width = 1.8 m, depth = 1.5 m, front height = 1.2 m back height = 0.9 m) was designed, built, and tested in an environmentally controlled laboratory and field site to evaluate the feasibility and effectiveness of the SFC under the climatic conditions in Abu Dhabi. Based on the experimental results, the temperature drops obtained from the SFC in the laboratory and field site were 5 ̊C at initial relative humidity of 60% and 7- 15 ̊C at initial relative humidity of 50%, respectively. An energy simulation using dynamic numerical simulations was performed in comparison to the results of the experiment. The energy-based dynamic simulation shows good agreement with the experimental results. The total power consumption of the SFC system was approximately three and a half times lower than that of an electrical air conditioner.

The processing of woody biomass waste piles for use as fuel instead of burning them was investigated. At each landing slash pile location, a 132 kW grapple excavator was used to transfer the waste piles into a 522 kW horizontal grinder. Economies of scale could be expected when grinding a larger pile, although the efficiency of the loading operation might be diminished. Here, three piles were ground and the operations were time-studied: Small (20 m long × 15 m wide × 4 m high), Medium (30 × 24 × 4 m), and Large (35 × 30 × 4 m) piles. Grinding the Medium pile was found to be the most productive at 30.65 bone dry tons per productive machine hour without delay (BDT/PMH₀), thereby suggesting that there might be an optimum size of slash pile for a grinding operation. We also examined modeling of the excavator and grinder operations, and we observed that the constructed simulation model well-replicated the actual operations. Based on the modeling, we estimated that the productivity of grinding at a landing area of 710 m² of slash pile location was 31.24 BDT/PMH₀, which was the most productive rate.

The operation of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination between gas and power transmission system operators in order to improve the efficiency and reliability of the combined energy system. In this paper, we present a co-simulation platform for examining the interdependence between natural gas and electricity transmission networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We analyze the value of day-ahead coordination of power and natural gas network operations and show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and renewable energy sources. Results show that day-ahead coordination contributes to a reduction in curtailed gas during high stress periods (e.g., large gas offtake ramps) and a reduction in energy consumption of gas compressor stations.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

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

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

We have developed an unsteady and non-linear wind synopsis simulator called RIAM-COMPACT (Research Institute for Applied Mechanics, Kyushu University, COMputational Prediction of Airflow over Complex Terrain) in order to simulate the airflow on a microscale, i.e., a few tens of km or less. In RIAM-COMPACT, the large-eddy simulation (LES) has been adopted for turbulence modeling. LES is a technique in which the structures of relatively large eddies are directly simulated and smaller eddies are modeled using a sub-grid scale model. In the present study, we have conducted the numerical wind diagnoses for Taikoyama Wind Farm nacelle separation accident in Japan. The simulation results suggest that all six wind turbines at the Taikoyama Wind Farm are subject to significant influence from separated flow (terrain-induced turbulence) which is generated due to the topographic irregularities in the vicinity of the wind turbines. A proposal has been also made on reconstruction of the wind farm.

In order to be able to face competition, thriving corporations have to be compelled to maintain an awfully smart relationship with their existing customers and additionally to be able to anticipate their future wants. Thus, corporations do not target customers as teams, however they’re attempting to focus on them as individuals. However, to be able to use this information, corporations have to be compelled to use promoting automation tools. Marketing Automation was an idea first introduced in 2001 by John D.C. Little in his presentation at the 5th Invitational Choice Symposium UC Berkeley 2001. This survey paper assesses prominent research on Marketing Automation and suggests how it can be modified to adapt to the current marketing scenario.

The increasingly used approach of combining different simulation software in testing of automated driving systems (ADS) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSP) provide the possibility to cover the high demand on testing kilometers for ADS by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand of testing kilometers is not enough to choose a suitable CSP. The complexity level of the used vehicle, object, sensors and environment models is essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, the environments, objects, sensors in VSS as well as traffic parameters in TFSS can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper we present a software design for CSP based on the Model-View-Controller (MVC) design pattern and implementation of a complex CSP for virtual testing of ADS. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive called CarMaker and the TFSS from PTV Group called Vissim. The results have shown that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADS.

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

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.

new subsonic blower wind tunnel design has been studied both numerically and experimentally, it is also referred to as “blower” wind tunnel. This paper, is initially aimed to address each sequential stage of the wind tunnel design process. Rather than applying the standard method of modelling solely the flow in the test section, a large-scale CFD model of the whole wind tunnel was employed. The loss of every constituent element was calculated and then all the losses are added up to determine the power needed for the wind tunnel operation which is used as “intake fan” boundary conditions in the CFD model. Then, flow uniformity and turbulent intensity measurements in an empty test section using a Pitot-static tube and hot wire anemometer (HWA) were introduced to validate the CFD results. The results showed that flow quality was significantly affected by boundary layer controllers (honeycomb and mesh screens) in the settling chamber and wide-angle diffuser. Investigations were also conducted to evaluate the flow deficit in the wake area behind a convex hump model using HWA. This was additional experimental tests carried out to validate the suitability of the wind tunnel flow aerodynamic research.

This manuscript discusses a novel method to map pressure results from one 3D surface shell mesh onto another. This technique is especially important when transferring results from one numerical analysis to another. This method works independent of the actual pressures, and only focuses on ensuring the surface areas consistently match. By utilizing this approach, the cumulative forces consistently match for all input pressures. This method is demonstrated to work for pressure profiles with precipitous changes in pressures, and with small quadrangular source elements being applied to a mix of large quadrangular and triangular target elements, and the forces at all pressure profiles match remarkably.

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.

Among the different applicable irrigants for root canal disinfection, sodium hypochlorite 5.25% is one of the most attractive ones. The quality of root canal disinfection is dependent on some factors such as the employed approach, type of flow rate of irrigant and the size of needle. The majority of studies in the field of root canal disinfection are experimentally carried out. In the current article, Computation Fluid Dynamic (CFD) is used for modeling the antimicrobial liquid flow in the root canal and evaluate the effects of needle size and flow rate. Two needles, G28 and G30, are used for irrigation in three volumetric rates of flow including 0.10 mL⁄s , 0.20 mL⁄s and 0.30 mL⁄s. The results of numerical simulations revealed the improved quality of root canal disinfection by augmentation in the rate of flow and decrease in the inner diameter of the needle. According to the outcomes of the modeling, the highest average wall shear stress obtained in the case of using G28 needle and 30 mL⁄s flow rate, which was approximately 10.21 Pa.

The health of the landing gear retraction and extension hydraulic system may be assessed using fuzzy comprehensive evaluation (FCE), however the traditional FCE method depends solely on human assessment by specialists, which is excessively subjective. To address the issue of excessive human subjective variables in the assessment, an improved FCE model based on enhanced risk coefficient is provided, which includes four consideration indexes: failure probability, failure severity, failure detection difficulty, and failure repair difficulty. To reduce subjective human judgment errors entirely due to expert experience, the improved FCE takes into account the likelihood of failure using a statistical method, the severity of failure using a fault simulation analysis based on the LMS Imagine.Lab AMESim simulation platform, and the difficulty of fault detection and repair using the aircraft manufacturer's professional maintenance information. As part of the evaluation model, the range of health assessment values and accompanying treatment methods are included, making it easier to implement on a daily basis in aircraft maintenance. As a final step, the simulation is evaluated and the simulated faults are calculated.

Vertical-channel organic dual-gate/base transistors would be highly interesting since vertical organic transistors are the fastest organic transistors demonstrated today. However, incorporating a dual gate/base structure into an ultra-short channel vertical architecture represents a substantial challenge. Here, we successfully realize a new device concept of vertical organic permeable dual-base transistors (OPDBTs), where either of both base electrodes can be used to change the on-currents and tune the threshold voltages. The optimized devices yield a high on-current density of 1.54 A cm^-2 and a large current gain of 9.2×10⁵, corresponding to a high transmission value of 99.998%. The detailed operation mechanisms are investigated by calibrated TCAD simulations with the Poole-Frenkel mobility model, Gaussian density of states and tunneling model. Finally, high-performance logic circuits, e.g. inverter, NAND/AND computation functions are demonstrated with one single OPDBT operating at supply voltages of < 2.0 V. We believe that OPDBTs offer a compact and technologically simple hardware platform with excellent application potential for vertical-channel organic transistors in complex logic circuits.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital and probably appeared during biogenesis four billion years ago. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and protein molecules, drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: tRNAs, aaRS, mRNAs, and ribosomes. When assembled in the right order, the translation machine created biosynthetic polypeptides, a process that transferred information from mRNAs to proteins. This was the beginning of the prebiotic information age. The molecular attraction between tRNA and amino acids led to different stages of the translation machines and the genetic code. tRNA is an ancient molecule that designed and built mRNA for storing the information of its cognate amino acid. Each mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional. The origin of the genetic code is enigmatic; herein we propose an evolutionary explanation: the demand for a wide range of specific enzymes in the peptide/RNA world was the main selective pressure for the origin of information-directed protein synthesis. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and the adaptive theory. These three theories are compatible with our coevolution model of the translation machines and the genetic code. We suggest biosynthetic pathways as the origin of the specific translation machines which provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machines: GNC code developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to understand the prebiotic information system better, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the translation might have worked in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics such as barcoding, DNA mining, or DNA fingerprinting. We constructed the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. Using AnyLogic software we were able to simulate and visualize the entire evolution of the translation machines and the genetic code. The results indicate that the emergence of the information age from the peptide/RNA world was a watershed event in the origin of life about four billion years ago.

A theoretical analysis of haemoglobin (Hb) concentration detection is presented in this work with the objective of achieving more sensitive detection and monitoring low concentrations. Surface-enhanced SPR spectroscopy on silver nanoparticles was employed for recording Hb concentrations less than 10 g/L. In this paper, Fe₃O₄@Au core-shell, nanocomposite spherical nanoparticle consisting of a spherical Fe₃O₄ core covered by Au shell, was used as an active material for biomolecules detection in the Surface Plasmon Resonance (SPR)-based biosensor in the wavelength 632.8 nm. We present the simulation of detection amplification technique through Attenuated Total Reflection (ATR) spectrum in the Kretschmann configuration. The system consists of a four-layer material i.e prism/Ag/Fe₃O₄@Au+Hb/air. Dielectric function determination of the core-shell nanoparticle (Fe₃O₄@Au) and the composite (Fe₃O₄@Au+Hb) was done by applying the Effective Medium Theory approximation and the calculation of the reflectivity is carried out by varying the size of core-shell (r₀). In this simulation, the refractive index of the BK7 prism is 1.51; the refractive index of Ag thin film is 0.13455+3.98651i with the thickness of 40 nm, and the refractive index of the composite is varied depending on the size of nanoparticle core-shell. Our results show that by varying the radius of the core and the shell thickness, the dip of the reflectivity (ATR) spectrum is shifted to the larger angle of incident light and the addition of core-shell in the conventional SPR-based biosensor leads to enhancement of the SPR biosensor sensitivity, for the core-shell radius 10 nm, the sensitivity increased by 1.35% for F = 0.1, and by 4.89% for F =0.8 compared to the sensitivity of the conventional SPR-based biosensor without core-shell addition.

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.

This work deals with the numerical investigation of the delivery of potential therapeutic agents through dentinal discs (i.e. a cylindrical segment of the dentinal tissue) towards the dentin-pulp junction. The aim is to assess the main key features (i.e. molecular size, initial concentration, consumption rate, disc porosity and thickness) that affect the delivery of therapeutic substances to the dental pulp and consequently to define the necessary quantitative and qualitative issues related to a specific agent before its potential application in clinical practice. The CFD code used for the computational study is validated with relevant experimental data obtained using micro Laser Induced Fluorescence (μ-LIF) a non-intrusive optical measuring technique. As the phenomenon is diffusion dominated and strongly dependent on the molecular size, the time needed for the concentration of released molecules to attain a required value can be controlled by their initial concentration. Finally, a model is proposed which, given the maximum acceptable time for the drug concentration to attain a required value at the pulpal side of the tissue along with the aforementioned key design parameters, is able to estimate the initial concentration to be imposed and vice versa.

Nanoparticles -- regularly patterned or randomly dispersed -- are a key ingredient for emerging technologies in photonics. Of particular interest are scattering and field enhancement effects of metal nanoparticles for energy harvesting and converting systems. An often neglected aspect in the modeling of nanoparticles are light interaction effects beyond classical electrodynamics stemming from electron dynamics in confined and accelerated systems. We give a detailed account on free electron phenomena in metal nanoparticles and discuss analytic expressions, stemming from microscopic and semi-classical theories. These can improve standard computational schemes to produce more reliable results on the optical properties of metal nanoparticles. We combine these solutions into a single framework and study their joint impact on isolated Au, Ag, and Al nanoparticles as well as dimer structures.

Lightning strike can cause a considerable damage in aircraft parts made from semiconducting materials such as Carbon Fiber Reinforced Plastics (CFRPs). Therefore, in recent years, the lightning strike phenomenon has attracted the interest of the academic community and the aircraft industry. Until now, the problem has been addressed mainly experimentally, while the reported numerical works are very limited. In the present work, a coupled electro-thermal FE model has been developed using the ANSYS commercial FE code to simulate the lightning strike damage in unidirectional CFRP laminates due to the Joule heat flux phenomenon. The model is based on the SOLID69 thermoelectric element and applies a non-linear, time-transient analysis. The main input to the model is the thermal-electrical properties of the composite material which vary with temperature. Using the model, a parametric study on the effect of mesh density and peak intensity on the thermal damage has been performed. Three electrical lightning strikes of low (10 kA), medium (30 kA) and high peak intensity (40 kA) have been applied according to the SAE ARP 5412 standard. The electro-thermal model has been validated against a numerical model from the literature. The numerical results reveal that the increase of peak intensity leads to the increase of the area and penetration depth of matrix thermal damage (pyrolysis) as well as to the increase of the area of fiber damage (deterioration and ablation). Through progressive damage modeling, the residual tensile strength of the CFRP plate after being subjected to lightning strike of different peak intensity has been predicted. Lightning strike initial damage has been simulated by translating the thermal field into degradation of elastic properties of the lamina. The results show an increase in the accumulated matrix damage and a decrease of tensile strength due to the initial lightning strike damage. For the maximum peak intensity of 40 kA, a decrease in tensile strength of 4.8% has been predicted

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

Molecular dynamics simulations were carried out to investigate the atomic structure of a model Cu64Zr36 bulk metallic glass (BMG). It is found that the amount of icosahedral content of the system is significantly increased in a well relaxed structure. While we considered four connection types of vertex-, edge-, face-, and volume-sharing, the huge cluster in the relaxed samples mainly involve volume-type connection and exhibits a remarkable athermal plasticity that great stiffness and great yield strength compared to the as-quenched samples. In addition, the bond-angle distribution of annealed sample shows sharp peaks at specific bond angles which is an evidence of crystallized Laves-phase formed by icosahedral atoms, however the peaks are to be broaden after loading, which indicates decreasing amount of icosahedral content and their shape distortion. These results suggest that icosahedral content in a bulk metallic glasses plays a key role to determine the mechanical properties such as rigidity and maximum stress carrying capacity.

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

We have proposed a data-driven approach for designing mesoscale porous structures of Li-ion battery electrode with three-dimensional virtual structures and machine learning techniques. Over 2,000 artificial 3D structures assuming positive electrode composed of random packed spheres as active material particles are generated, and charge/discharge resistance has been evaluated using simplified Physico-chemical model. In this model, resistance from Li diffusion in active material particles (diffusion resistance), transfer resistance of Li+ in electrolyte (electrolyte resistance) and reaction resistance on the interface between active material and electrolyte are simulated based on mass balance of Li, Ohm’s law in and linearized Butler-Volmer equation, respectively. Using these simulation results, regression models via Artificial Neural Network (ANN) have been created in order to predict charge/discharge resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder volume fraction are adopted as features, associated with controllable process parameters for manufacturing battery electrode. As results, the predicted electrode resistance by ANN regression model is good agreement with the simulated values. Furthermore, sensitivity analysis and optimization of the process parameters have been carried out. The proposed data-driven approach could be a solution as a guiding principle for manufacturing battery electrode.

In this paper, we studied and analyzed the behavior of velocity profiles and the distribution of pressures in a pipe connected to an accessory. In this case, it was a 90º elbow fitting in which water flows as an incompressible flow. This study was developed numerically through the fluent program of ANSYS. It is of practical interest in engineering because it is based on results regarding the internal structure of a model of incompressible flows. In the same way, it provides a basis for the proper design and implementation of velocity profiles within an elbow.In the agricultural industry, suitable systems can be implemented for crop irrigation in this way with the results obtained to improve the design in the fluid distribution system.We used a feasible κ-ε turbulence model because this model causes less turbulence than the standard κ-ε model. A mesh independence study was developed, in which it was observed that combining different densities of meshes for the same geometry causes some variables, so a fine meshing was used to carry out the study so that the results are as close to reality as possible.

The link between language processing and motor systems has been the focus of increasing interest to Cognitive Neuroscience. Some classical papers studying Event Related Potentials (ERPs) induced by linguistic stimuli have found differences in electrophysiological activity when comparing action and non-action words; more specifically, a bigger p200 for action words. On the other hand, a series of studies have validated the use of a grip force sensor (GFS) to measure language-induced motor activity during both isolated words and sentence listening, finding that action words induce an augmentation in the grip force around 250-300 ms after the onset of the stimulus. The purpose of the present study is to combine both techniques to assess if the p200 is related to the augmentation of the grip force measured by a GFS. We measured ERP and GFS changes elicited by listening to action and non-action words while maintaining an active grasping task in 10 healthy subjects. Our results show that the amplitude of the p200 in central electrodes is correlated to the augmentation in the GFS around 300 ms induced by linguistic stimuli. To our knowledge, this is the first study where the electrophysiological activity and the changes in the grip force induced by auditory language processing are put together, opening new venues of interpretation for the sensorimotor interaction in language processing.

In this work the efficiency of a new μ-mixer design is investigated. As in this type of devices the Reynolds number is low, mixing is diffusion dominated and it can be enhanced by creating secondary flows. In this study we propose the introduction of helical inserts into a straight tube to create swirling flow. The influence of the insert’s geometrical parameters (pitch and length of the propeller blades) and of the Reynolds number on the mixing efficiency and on the pressure drop are numerically investigated. The mixing efficiency of the device is assessed by calculating a number, i.e. the Index of Mixing Efficiency that quantifies the uniformity of concentration at the outlet of the device. The influence of the design parameters on the mixing efficiency is assessed by performing a series of “computational” experiments, in which the values of the parameter are selected using DOE methodology. Finally using the numerical data, appropriate design equations are formulated, which, for given values ​​of the design parameters, can estimate with reasonable accuracy both the mixing efficiency and the pressure drop of the proposed mixing device.

The optimization methods are increasingly used to solve challenging problems of aeronautical engineering. Typically, the optimization methods are utilized in design of aircraft airframe or its structure. The presented study is focused on an improvement of aircraft-flight-control procedures through the numerical optimization approach. The optimization problems concern selected phases of flight of light gyroplane - a rotorcraft using an unpowered rotor in autorotation to develop lift and an engine-powered propeller to provide thrust. An original methodology of computational simulation of rotorcraft flight was developed and implemented. In this approach the aircraft-motion equations are solved step-by-step, simultaneously with the solution of the Unsteady Reynolds-Averaged Navier-Stokes equations, which is conducted to assess aerodynamic forces acting on the aircraft. As a numerical optimization method, the BFGS algorithm was adapted. The developed methodology was applied to optimize the flight-control procedures in selected stages of gyroplane flight in direct proximity of the ground, where properly conducted control of the aircraft is critical to ensure flight safety and performance. The results of conducted computational optimizations proved qualitative correctness of the developed methodology. The research results can be helpful in design of easy-to-control gyroplanes and also in the training of pilots of this type of rotorcraft.

This work studied the corrosion of welded pipes and how welding destroyed surface film of pipes. Surface reaction of a welded pipe is key to understanding phenomena and important factors during the corrosion. This paper presents experiment and CFD modeling approaches of a welded pipe corrosion under salt vapor condition. The pipes were welded at currents of 60 A,70 A and 80 A to observe the effect of welding current on corrosion. A welded pipe is a stainless-steel ASTM A312 grade 304L and period of experiment about 0-600 hours that they are tested in vertical and horizontal alignments. In CFD software, there is not direct model of corrosion but it can use surface reaction and create add-on species and chemical reaction technique for imitating the corrosion mechanism. The modeling approaches of corrosion have presented in 3-dimensional transient times in CFD simulation. Surface reactions were performed by Species Model which involve site species. Site species in Species Model took place at gas-solid interfaces and in this case are salt vapor and surface pipe. Chemical reaction rate on the surface controls lost weight of a welded pipe and the model can be validated with experiment. In conclusion, in period 0-600 hours error between CFD modeling and experiment have error trend decreased. The error at 600 hours is 6% both of vertical pipe and horizontal pipe test. The modeling approaches closely with the performed experiment and can be accepted. Moreover, the model is able to predict corrosion of a welded pipe of different sizes and their lost weight after 600 hours without experiment. Also the model can predict lifetime of pipe.

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

Whether evaluating gridded population dataset estimates (e.g. WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a close-to-reality population of individuals nested in households geolocated to realistic building locations. Using the R simPop package and ArcGIS, multiple realizations of a geolocated synthetic population are derived from the Namibia 2011 census 20% microdata sample, Namibia census enumeration area boundaries, Namibia 2013 Demographic and Health Survey (DHS), and dozens of publicly available spatial datasets. Realistic household latitude-longitude coordinates are manually generated based on public satellite imagery. Simulated households are linked to latitude-longitude coordinates by identifying distinct household types with multivariate kmeans analysis, and modelling a probability surface for each household type using Random Forest machine learning methods. We simulate five realizations of a synthetic population in Namibia's Oshikoto region, including demographic, socioeconomic and outcome characteristics at the level of household, woman, and child. Comparison of variables in the synthetic population were made with 2011 census 20% sample and 2013 DHS data by primary sampling unit/enumeration area. We found that synthetic population variable distributions matched observed observations and followed expected spatial patterns. We outline a novel process to simulate a close-to-reality microdata census geolocated to realistic building locations in a low- or middle-income country setting to support spatial demographic research and survey methodological development while avoiding disclosure risk of individuals.

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

Many scientific and technological problems are studied with the help of computer codes that simulate the phenomena of interest rather than via traditional laboratory experiments. Such models play an important role in neuroscience where they are used to mimic brain function from the sub-cellular to the macroscopic level. Exploration with computer models carries with it a number of statistical challenges: where to sample the input space for the simulator, how to make sense of the data that is generated, how to estimate unknown parameters in the model, how to validate a model. The simulator setting also has some unique problems and possibilities. This review paper describes statistical research on these issues and how that work might be applied to neural simulations.

This study aims to understand the contents of debriefing performance in simulation education and its results by comprehensively examining the learning performance of the education ac-cording to the difference in the debriefing methods employed in domestic and overseas nursing simulation training. This is a literature review conducted to identify the effect of debriefing of simulation nursing practice education. The existing literature was found in electronic databases using Pubmed, Embase, MEDLINE complete, PsycINFO, Web of Science, CINAHL, the Cochrane Library, KoreaMed, National Discovery for Science Leaders, and Research Information Sharing Service and the key words were “nurse,” “nursing student,” “simulation,” “simulator,” “stand-ardized patient,” “debriefing.” Finally, 32 studies were analyzed. All the studies were conducted from 2012 to 2021. A total of 11 RCT, 17 quasi-experimental studies, 3 mixed method studies and 1 pilot study were identified. The debriefing process used media, structured questionnaires, and a method of teaching or peer-led debriefing. The outcome variables that were statistically significant were skill, performance, knowledge, problem-solving competency, critical thinking disposition, clinical judgement, self-confidence, satisfaction, and debriefing quality evaluation. It is necessary to educate the debriefers who are responsible for strategy development and meeting effective debriefing goals.

With an explosion of data in the nearly every domain, from physical and environmental sciences to econometrics and even social science, we have entered a golden era of time-domain data science. In addition to ever increasing data volumes and varieties of time-series, there's a plethora of techniques available to analyses them. Unsurprisingly, machine learning approaches are very popular. In environmental sciences (ranging from climate studies to space weather/solar physics), machine learning (ML )and deep learning (DL) approaches complement the traditional data assimilation approaches. In solar and astrophysics, multiwavelength time-series or lightcurves along with observations of other messengers like gravitational waves provide a treasure trove of time-domain data for ML/DL approaches for modeling and forecasting. In this regard, deep learning algorithms such as recurrent neural networks (RNNs) should prove extremely powerful for forecasting as it has in several other domains. Such methods agnostic of the physics can only predict transient flares that are represented in the original distribution trained on (eg. coloured noise fluctuations) and not special, isolated episodes (eg. a one-off outburst like a major coronal mass ejection) potentially distinguishing them. Therefore it is vital to test robustness of these techniques ; here we attempt to make a small contribution to this by exploring sensitivity of RNNs with some rudimentary experiments. We test the performance of a very successful class of RNNs, the Long Short Term Memory (LSTM) algorithms with simulated lightcurves. As an example, we focus on univariate forecasting of time-series (or lightcurves) characteristic certain variable astrophysical objects (eg. active galactic nuclei or AGN). Specifically, we explore the sensitivity of training and test losses to key parameters of the LSTM network and data characteristics namely gaps and complexity measured in terms of number of Fourier components. We find that typically, the performances of LSTMs are better for pink or flicker noise type sources. The key parameters on which performance is dependent are batch size for LSTM and the gap percentage of the lightcurves. While a batch size of $10-30$ seems optimal, the most optimal test and train losses are under $10 \%$ of missing data for both periodic and random gaps in pink noise. The performance is far worse for red noise. This compromises detectability of transients. Thus, we show that generative models (time-series simulations) are excellent guides for use of RNN-LSTMs in building forecasting systems

The beneficial effects of solar radiation on human health are well documented. One necessary mechanism triggers the production of vitamin D whose insufficiency has been linked to a variety of disorders like diabetes, hypertension and more recently amyloidosis and Alzheimer disease. However, there are few architectural designs capable on ensuring adequate provision of solar radiation inside buildings. Conventional fenestration is not sufficient to provide for significant doses of sunlight even to prevent seasonal affective disorder (SAD). In this paper we discuss the effect of new design alternatives for skylights, especially in the refurbishment of obsolete facilities. After such complex retrofit is executed, we have analyzed the performance of a building in warm and sunny climates as is the case of southern Spain. It has been considered as a priority the study of the factors that relate sunlight and energy, as well as, to a certain extent, other aspects like ventilation and insulation. Many architectural designs are presented as correct if the thermal requirements alone are met, even at the risk of later energy waste in lighting devices and visual or physical discomfort. On the other hand, large glazed areas allow more daylight into a space, but they may also allow excessive heat gains or losses which increase the air-conditioning cooling or heating load. To avoid these problems, we have considered the combined effect of daylight and energy from the beginning of the skylight design-process. A daylighting software based on configuration factors that we have apply in former researches to study the problems of direct sun over architectural structures have been used. This question cannot be treated adequately with conventional programs for overcast skies. The skylights have already been constructed and on-site measurements in the offices have been taken to complement the computer simulations data. The results show that it is possible to achieve energy saving and high radiation levels in winter without increasing heat loads during the summer. All this is considered beneficial to improve the condition of users with cognitive diseases as Alzheimer’s disease by virtue of adapted spaces.

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.

Cartels cause tremendous damage to the market economy and disadvantage consumers by causing higher prices and lower quality; moreover, they are difficult to detect. We need to prevent them by scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. Particularly, the probability of cartel penalization is a useful indicator for the evaluation of the competition enforcement. This study is to estimate the probability of cartel penalization by using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by Bayesian approach from cartel data of Department of Justice in United States from 1970 to 2009. The probability of cartel penalization is seen to be sensitive to change of competition law and the results shows the usefulness of higher interpretation than other research. The result of the policy simulation shows how effective the leniency program is. From this estimation, antitrust enforcement is evaluated, and thereby, can be improved.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

One of the principal hallmarks of Alzheimer’s disease (AD) is related to the aggregation of amyloid-β fibrils in an insoluble form in the brain, also known as amyloidosis. Therefore, a prominent therapeutic strategy against AD consists either in blocking the amyloid aggregation and/or destroying the already formed aggregates. Natural products have shown significant therapeutic potential as amyloid inhibitors from in vitro studies as well as in vivo animal tests. In this study, the interaction of five natural biophenols (curcumin, dopamine, (-)-Epigallocatechin-3-gallate, Quercetin, and Rosmarinic acid) with the amyloid-β(1-40) fibrils has been studied through computational simulations. The results allowed the identification and characterization of the different binding modalities of each compounds and their consequences on fibril dynamics and aggregation. It emerges that the lateral aggregation of the fibrils is strongly influenced by the intercalation of the ligands, which modulate the double-layered structure stability.

Salmonella, especially invasive non-typhoidal Salmonella (iNTS) are responsible for developing various invasive diseases, and possess higher mortality rate, due to their higher antibiotic resistance profile than the other bacteria. Therefore, the present study was concerned to develop epitope based peptide vaccine against iNTS species as a successive and substitute protective measures. The study considered comprehensive Immunoinformatic approaches, followed by molecular docking and molecular dynamics simulation to predict the efficient vaccine candidate T cell and B cell epitopes, based on the outer membrane proteins. The study identified two best epitopes YGIFAITAL and KVLYGIFAI from total iNTS outer membrane proteins, which showed higher immunity, non-allergenicity, non-toxicity and also showed higher conservancy and population coverage values. Both epitopes showed higher binding affinity and stability towards HLA-C* 03:03. The MM-PBSA binding free energy showed the YGIFAITAL epitope binds more tightly with both MHC-I and MHC-II molecules. The total contact, H-bond analysis and RMSF results also validate the efficiency of these epitopes as vaccine candidate. The projected B cell epitopes AAPVQVGEAAGS, TGGGDGSNT and TGGGDGSNTGTTTT showed higher antigenicity. Overall, the study concluded that these epitopes can be considered as the potential vaccine candidate to make a successive vaccine against iNTS species. However, this result further needs to be validate by wet lab research to make successive vaccine with these projected epitopes.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

The present work deals with the development of finite element methodology for obtaining the stress distributions in thick cylindrical HK40 stainless steel pipe that carry high temperature fluids. The material properties and loading are assumed to be random variables. Thermal stresses that are generated along radial, axial and tangential directions are computed generally using analytical expressions which are very complex. To circumvent such an issue, the probability theory and mathematical statistics have been applied to many engineering problems which allows to determine the safety both quantitatively and objectively based on the concepts of reliability. Monte Carlo simulation methodology is used to study the probabilistic characteristics of thermal stresses which is used for estimating the probabilistic distributions of stresses against the variations arising due to material properties and load. A 2-D Probabilistic finite element code is developed in MATLAB and the deterministic solution is compared with ABAQUS solutions. The values of stresses that are obtained from the variation of elastic modulus are found to be low as compared to the case where the load alone is varying. The probability of failure of the pipe structure is predicted against the variations in internal pressure and thermal gradient. These finite element framework developments are useful for the life estimation of piping structures in high temperature applications and subsequently quantifying the uncertainties in loading and material properties.

The details of antigen-antibody interactions and the identification of epitopes are critical for the development of monoclonal antibody drugs. Ab42 is a native human-derived anti-CFH monoclonal antibody. In this study, the interaction between antigen pCFH and antibody (Ab42) was theoretically demonstrated by molecular docking and MD simulation, combined with free energy calculation and computational alanine scanning (CAS), and key amino acids and epitopes were identified. Experimental alanine scanning (EAS) was then carried out to verify the results of the calculation, and our results indicated that Ab42 antibody forms hydrogen bonds and interacts hydrophobically with pCFH through the Tyr315, Ser100, Gly33, and Tyr53 residues on its CDR, while the main pCFH epitopes are located at the six sites of Pro441, Ile442, Asp443, Asn444, Ile447, and Thr448. In conclusion, this study has explored the mechanism of antigen-antibody interaction from both theoretical and experimental aspects, and our results have important theoretical significance for the design and development of relevant antibody drugs.

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

We introduce a mission design for an interstellar expedition to nearby earth-like exoplanets, which our analysis determined to be Tau Ceti and Gliese 667C, at the time of analysis in 2013. We review the research problems in propulsion and AGI that must be addressed to launch an AI guided interstellar probe within 100 years. We propose a new semi-autonomous agent approach for intelligent control of the spacecraft. We introduce the concept of a semi-autonomous agent as having built-in safety guarantees that constrain operation. An autonomous agent case study is presented formulating the objective, constraints, AGI implementation of the agent based on Solomono's Alpha architecture, and adding specicity. We discuss the training required to reach human-level and trans-sapient levels of intelligence which corresponds to an entire crew of AI experts specialized in elds such as astrophysics, astromechanics, astrobiology, quantum physics, computer science, molecular biology, and so forth. We project the feasibility of the human-level AI technology based on empirical ndings in neuroscience, and nd that it should be feasible by 2030. We analyze Solomono's innity point hypothesis in light of Koomey's law about energy eciency of computing and nd that the trends in 2013 indicated an early singularity by 2035, which implies that we might encounter physical bottlenecks which will decelerate computing technology improvements signicantly. We recommend thus year 2040 for launching the probe by which date other required technologies will have been developed. We discuss the scenario of a virtual crew made of brain simulations, which is a bio-information based AI approach. We detail the subsystems of command and control, communication, scientic instrumentation, power, propulsion, navigation, and shielding. We propose a variation of ICAN-II/AIMStar propulsion which uses a positron source instead of anti-protons to initiate micro-fusion reactions. We combine the positron initiated fusion pulse propulsion scheme with a miniaturized version of the Daedelus fusion thruster obtaining high performance. We derive two mission proles one for fusion pulse propulsion, and the yet hypothetical Q-Thruster. Fusion thruster requires 132.2 years for Tau Ceti, and 233.2 years for Gliese 667C, while Q-Thruster takes only 42.3 years for Tau Ceti, and 62.5 years for Gliese 667C. We also discuss extended roles for intelligent interstellar probes such as self-reproduction via nanotechnology, refueling, construction, robotic bodies, and transmission of brain simulations.

First of all the paper presents a solid solution of piezoelectric ceramic that was synthesized according to the general formula Pb(1-x)Srx(Ti0.48Zr0.52)(1-y)NbyO3 with x = 0.05 and y = 0.02, using wet ceramic processing technology and using an oxide mix as prime material. The effects of dopants (Sr2+ and Nb5+) on phase constitution, on microstructure and on the dielectric and piezoelectric properties were determinate. The Zr/Ti ratio was chosen near the morphotropic phase boundary of the PZT system in studied composition. The XRD data revealed that the PZT doped composition had tetragonal perovskite structure. Secondly the paper presents the design of the novel piezo-motor based on a surface wave which translates the linear extension of different piezoelectric segments of a piezoelectric cylinder into a rotational bending movement. This rotational bending of the piezoelectric cylinder is then transformed into a continuous rotation of the rotor through a calculated contact. The design of the motor takes advantage of the high piezoelectric constants of the developed material in an optimal way in order to increase the energetic efficiency. A brief mathematical model of electromechanical answer of piezoelectric materials is presented as it was used in the modeling of the material during the numerical simulations.

Three-phase to single-phase matrix converter (TSMC) is used as the transmitter for transient electromagnetic method (TEM) instrument, instead of an AC-DC converter and an H-bridge inverter. The transmitter produces excitation current for primary field. The output current has a waveform with a bipolar square of half duty cycle, which is required by TEM. The space vector PWM based on input currents is discussed in detail. The control method of input power factor is proposed for TSMC. The simulation model of TSMC is established and the simulations of different conditions are completed. The simulation results show the controllability of the input power factor is illustrated.

In this paper, an attempt has been made to analyze the effect of spool port/ groove geometry on the pressure drop and chamber pressures which effect the performance parameters of the flow distributor valve. The work mainly involves formulation of detailed mathematical model of the valve and compare them on the same platform. For mathematical modelling, Matlab has been used. The size of the orifices is considered same throughout the model for better comparison. Initially the construction and functioning of flow distributor valve along with working principles of hydrostatic motor (Rotary Piston) is shown. Next shown the analytical analysis of area change and pressure drops due to different geometry of the spool valve ports. After that the computational fluid dynamics (CFD) analysis has been shown. A complete mathematical model to describe such flow distributor valve is developed after having a comprehensive knowledge of orifice characteristics, flow interactions based on valve geometry. Equations of flow through different orifices (fixed and variable area) of the valve have been developed based on the relationships obtained earlier.

The dilatometer curves of continuous cooling transformation of 12Mn steel were measured with Formastor-FⅡthermal mechanical simulator．The steel's undercooled austenite phase continuous cooling transformation curves ( CCT curves) were established by means of the dilatometer method and the metallographic-hardness measurement method． The effect of cooling rate on microstructure and hardness of the steel was studied． CCT curve of test steel was simulated by JMatPro. The results show that the Ac1 and Ac3 of the experimental steel are 692 ℃ and 855 ℃ ; the microstructure obtained is made of ferrite，pearlite and bainite．The ferrite transformation and pearlite transformation occur at a slower cooling rate，in which the ferrite is dominant. When the cooling rate is greater than 4.25 ℃ / s bainite transformation happens．As the cooling rate increases，microstructure or grains become finer．The hardness of the tested steel with increasing cooling rate shows a trend of first fast increase and soon decrease．The simulation results are consistent with the measured CCT law. The high temperature mechanical properties of 12mn steel round billet were tested by gleeble-1500d thermal / mechanical simulator. The tensile strength, reduction of area and stress-strain curves of the billet were obtained in the range of 600-1300 ℃.

This paper proposes a procedure for predicting the actual wind speed for flow over complex terrain with CFD. It converts a time-series of wind speed data acquired from field observations into a time-series of actual scalar wind speed by using non-dimensional wind speed parameters which are determined beforehand with the use of CFD output. The accuracy and reproducibility of the prediction procedure were examined by simulating the flow with CFD with the use of high resolution (5 m) surface elevation data for the Noma Wind Park in Kagoshima Prefecture, Japan. The errors of the predicted average monthly wind speeds relative to the observed values were less than approximately 20%.

The paper introduces a valuable new description of fatigue strength in relation to material properties and thus a new perspective on the overall understanding of the fatigue process. Namely, a relation between the endurance limits and the accompanying values of the critical resolved shear stress (CRSS) for various metallic materials has been discovered by means of a multiscale approach for fatigue simulation. Based on the uniqueness of the relation, there is a strong indication that it is feasible to relate the endurance limit to the CRSS, and not to the ultimate strength as often done in the past.

In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.

Whether the universe is a computer simulation, or whether we wish to efficiently model our universe in a computer simulation, there would be benefits to modeling it in a fashion analogous to computer spreadsheet, each lattice cell can be conceived as containing all the mathematical formula necessary to continuously compute its state relative to changes in all its neighboring cells, and by progression, in relation to all the cells of entire space-time lattice. Alternatively, the “real” universe may itself be built on a space cell lattice, an irregular foam of space cells, in which each cell may be conceived as a multidimensional cell of distortable space, the shape of which fully describes (a) the four basic forces (gravity, electromagnetic, strong, weak) observed at that cell of space, and (b) the probability (or weight distribution) of any quantum states overlapping the cell and its neighbors. At an appropriate scale, it would appear that this conceptual model would resolve apparent conflicts between general relativity and quantum physics. It would also provide a new interpretation of Planck’s constant as description of the number of space cell events associated with any set of observable events. If formulae operating at a lattice cell level can be improve our ability to understand and model larger scale phenomena, this would be strong evidence in favor of the theory that mathematics is not just a human invention but rather an inherent feature of space-time itself.

Over the last few decades, ocean research and exploration have made underwater mechanical systems a necessity. Underwater vehicles provide a new kind of marine platforms that could represent a great necessity in many areas of oceanographic research. Until now, the underwater vehicles come in a verity of shapes, sizes and means of propulsion. Depending on these characteristics, the type and mission of the vehicle are also determined. The underwater robots are used for different inspection and intervention missions in e.g. the oil and gas industry, ocean science research. Due to multiple applications to which the vehicle can participate, it can be successfully used and to determine methods of re-use of marine energy. Environmental mapping provides accurate information about the main areas of interest of the energy, as well as the exploitation possibilities of that. Most of the time, biomimetic robots were inspired their senso structure, from different kind of animals, such as insects, fish and birds. Nowadays, the concept of a underwater robotic vehicles capable to move independently, autonomously or remotely, has a great potential and a large application. This is the reason that the last studies have been directed on biomimetic robots. The fish and other underwater animals have evolved superior swimming capabilities in many ways and represent a starting point to explain the fluid-mechanical principles. Furthermore, the underwater animals develop and achieve extraordinary propulsion efficiencies, acceleration and maneuverability. They can also achieve high speed under water. Implanting and creating a vehicle through a biomimetic approach reduces the energy used to maneuver the vehicle as it can automatically correct its position and displacement. The paper presents an examination of the state of biomimetic robotic fishes, underlining the reason why bio-inspiration can help us in the underwater locomotion technology.

The multidrug resistance transporter NorM is an important drug resistance pump and plays a critical role in multidrug resistance in bacteria and mammals. In this study we carried out molecular dynamics simulation to study the mechanism of Na+ binding and dynamical structures of two long loops in the substrate-releasing process in substrate binding NorM. Our simulation study identified several key residues (D41, E261 D377) along the Na+ binding pathway and a multi-state ion-binding mechanism is proposed based on the simulation study. In this proposed model, the transport of Na+ is a multi-stage process with D41 being the first station for binding to Na+, followed by Na+ binding to the second station E262 and finally to the cation-binding site of E262 and D377. During the transport of Na+, the transmembrane components TM1, TM7 and TM2 are rearranged to facilitate the ion transport as well conformational changes of NorM to a closed state. Further, substrate-bound simulation revealed that Loop3-4 and Loop9-10 control the substrate-releasing process.

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

To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics are important to be characterized but its atomistic details are hard to obtain both experimentally and computationally. We review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: One is the Onsager-Machlup action method, and the other is the weighted ensemble method. Some applications of above methods to biomolecular systems are also discussed and illustrated.

In light of the self-simulation hypothesis, a simple form implementation of the principle of efficient language is discussed in a self-referential geometric quasicrystalline state sum model in three dimensions. Emergence is discussed in context of geometric state sum models.

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable modes of transport in urban mobility to help reduce the environmental impact. However, its traffic-related, economic and environmental effects are merely represented in academic research. We used passenger car traffic data in Berlin generated by the multi-agent transport simulation framework MATSim to develop a python-based simulation, resembling an e-moped sharing system. Based on the results, a total cost of ownership and a life cycle assessment for fleet sizes of 2,500, 10,000 and 50,000 vehicles were conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2,500 vehicles exhibits the lowest environmental emissions per kilometer driven over the expected lifespan of a shared e-moped. Based on the renewable energy potential for 2050 forecasted by the German Federal Environment Agency, a significant overall decline in environmental impacts can be achieved.

The past two decades have witnessed the introduction of and then a steady increase in the use of computational techniques in the study and development of molecularly imprinted polymers (MIPs). Molecular dynamics (MD) based studies have had a significant role in this development as they can provide insights concerning the mechanisms governing the molecular level events underlying MIP synthesis and MIP-ligand interactions and can be used for the identification of preferred monomer compositions and for the prediction of MIP properties. We here review the role that MD has played in the development of molecular imprinting and examine the different types of MD strategies that have been used, including their advantages and challenges. Recent trends in the application of MD to the study of MIPs are presented, along with a perspective on the future importance of MD-based studies for the development of molecular imprinting science and technology.

A micromechanical model is implemented to indicate the progressive fatigue problem of a laminated composite with a central circular hole under fatigue loading based on a finite element model. The mechanical properties of the composite lamina are represented based on the characteristics of the fiber and the matrix through a micromechanics model. An appropriate algorithm is then adopted to simulate fatigue damage development in the composite lamina. According to this algorithm, the stress field of the composite subjected to fatigue load is initially obtained using the finite element method. Finally, the predicted results of the stresses in the constituents i.e. fiber and matrix are determined according to the micromechanical bridging model. Finally applying proper damage driving relations leads to damage degree in each element. The proposed model is proven to be successful in the observation of the fatigue behavior with stiffness degradation in each element of the composite in each cycle. Results are reported and validated using those micromechanical models and experimental data available in the literature.

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and healthcare practitioners. Skin lesion restoration is an essential preprocessing step for lesion enhancements for accurate automated analysis and diagnosis. Digital hair removal is a non-invasive method for image enhancement by solving the hair-occlusion artefact in previously captured images. Several hair removal methods were proposed for skin delineation and removal. However, manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or using benchmarking datasets for comparison purposes. In the presented work, we propose a realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with the ground-truth mask for hair location. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximise the synthesis quality. For quantitatively evaluate the realism of image synthesis, the t-SNE feature mapping and Bland-Altman test are employed as objective metrics. Experimental results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture.

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

We proposed a so-called Beta Kumaraswamy Burr Type X distribution which gives the extension of the Kumaraswamy-G class of family distribution. Some properties of this proposed model were provided, like: the expansion of densi- ties and quantile function. We considered the Bayes and maximum likelihood methods to estimate the parameters and also simulate the model parameters to validate the methods based on dierent set of true values. Some real data sets were employed to show the usefulness and exibility of the model which serves as generalization to many sub-models in the elds of engineering, medical, survival and reliability analysis.

Ubiquitin-specific protease 14 (USP14) is a member of the Deubiquitinating enzymes (DUBs) involved in disrupting the regulation of the ubiquitin-proteasome system, responsible for the degradation of impaired and misfolded proteins which is an essential mechanism in eukaryotic cells. The involvement of USP14 in cancer progression and neurodegenerative disorders has been reported. Thereof USP14 is a prime therapeutic target; hence, designing efficacious inhibitors of USP14 is central in curbing these conditions. Herein, we relied on structural bioinformatics methods incorporating molecular docking, Molecular Mechanics Generalized Born Surface Area (MM-GBSA), Molecular dynamics simulation (MD simulation) and ADME to identify potential allosteric USP14 inhibitors. A library of over 733 compounds from the PubChem repository with >90% match to the IU1 chemical structure was screened in a multi-step framework to attain prospective drug-like inhibitors. Two potential lead compounds (CID 43013232 and CID 112370349) were shown to record better binding affinity compared to IU1, but with subtle difference to IU1-47, a 10-fold potent compound when compared to IU1. The stability of the lead molecules complexed with USP14 was studied via MD simulations. The molecules were found to be stable within the binding site throughout the 50ns simulation time. Moreover, the protein-ligand interactions across the simulation run time suggest Phe331, Tyr476, and Gln197 as crucial residues for USP14 inhibition. Furthermore, in silico pharmacological evaluation revealed the lead compounds as pharmacological sound molecules. Overall, the methods deployed in this study revealed two novel candidates that may show selective inhibitory activity against USP14, which could be exploited to produce potent and harmless USP14 inhibitors.

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool’s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

Toshiba devised the bladeless fan (or Air Multiplier) concept in 1981. Researchers like James Dyson and Jafari et al. further developed it. Bladeless fans are more energy-efficient, safer due to the hidden blades, easier to clean, and more adjustable than conventional fans. From a performance point of view, bladeless fans are better because they multiply mass flow rate, eliminate buffeting, consume less power, and are quieter. This paper investigates the influence of the airfoil’s outlet slit thickness on the discharge ratio by varying the outlet slit thickness of an Eppler 473 airfoil from 1.2 mm to 2 mm in intervals of 0.2 mm. Results indicated that smaller slits showed higher discharge ratios. The airfoil with a 1.2 mm slit thickness showed a discharge ratio of 18.78, a 24% increase from the discharge ratio of the 2 mm slit. The effect of outlet angle on the pressure drop across the airfoil was also studied. Outlet angles were varied from 16∘ to 26∘ by an interval of 2∘. The airfoil profile with a 24∘ outlet angle showed a maximum pressure difference of 965 Pa between the slit and leading edge. In contrast, the 16∘ outlet angle showed the least pressure difference of 355 Pa. Parameters such as average velocity, turbulent kinetic energy, the standard deviation of velocity and outlet velocity magnitude was used to assess the performance of airfoil profiles used in bladeless fan.

Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities like subgrade damages. The reason of the intensive void development lies in the mechanics of the sleeper and ballast bed interaction. The particularity of the interaction is a dynamic impact that occur due to void closure. Additionally, void zones cause inhomogeneous ballast pressure distribution between the void zone and fully supported neighbour zones. The present paper is devoted studying the mechanism of the sleeper-ballast dynamic impact in the void zone. The results of experimental in-situ measurements of rail deflections showed the significant impact accelerations in the zone even for light-weight slow vehicles. A simple 3-beam numerical model of track and rolling stock interaction has shown the similar to the experimental measurements dynamic interaction. Moreover, the model shows that the sleeper accelerations are more than 3 times higher than the corresponding wheel accelerations and the impact point appear before the wheel enters the impact point. The analysis of ballast loadings shows the specific impact behaviour in combination with the quasistatic part that is different for void and neighbour zones, which are characterised with high ballast pre-stressed conditions. The analysis of void sizes influence demonstrate that the impact loadings, wheel and sleeper maximal accelerations appear at certain void depth after which the values decrease. The ballast quasistatic loading analysis indicates more than twice increase of the ballast loading in neighbour zones for long voids and almost full quasistatic unloading for short length voids. However, the used imitation model cannot explain the nature of the dynamic impact. The mechanism of the void impact is clearly explained by the analytic solution using a simple clamped beam. A simplified analytical expression of the void impact velocity shows that it is linearly related to the wheel speed and loading. The comparison to the numerically simulated impact velocities shows a good agreement and the existence of the void depth with the maximal impact. An estimation of the long-term influences for the cases of normal sleeper loading, high ballast pre-stress and quasistatic loading in the neighbour zones and high impact inside the void are performed.

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.

Direct yaw moment control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer andfaster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, more advanced comparison on important characteristics of the controllers performance is missed, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified PID controller showed that its performance is comparable to other more complex control techniques such as MPC.

The residential sector in the United States is in need of comprehensive policy-making reforms that concurrently address housing affordability and environmental sustainability. This study investigates the feasibility of state-wide zero-energy affordable housing by analyzing historical data on climate, energy use, and solar system costs in the Commonwealth of Virginia. The hypothesis examined is that the net present cost of implementation of rooftop residential solar systems to achieve zero-energy Low-Income Housing Tax Credit (LIHTC) buildings is lower than the discounted present cost of energy of otherwise identical conventional buildings that run without renewable energy generation systems. The authors propose a generalizable framework for analyzing the feasibility of achieving region- or state-wide zero-energy LIHTC developments. To validate the framework, the authors employ a longitudinal sample of monthly energy use data from 2013-2016 obtained from 310 residential units of 15 LIHTC developments across the state. Based on statistical regression analysis, energy simulation, and simulation-based risk analysis, the authors find that the net present value of zero-energy LIHTC investments can be positive with a low risk. The investment value varies often depending on the zero-energy building definition, weather characteristics, retail price of electricity, and incentive rate. This study can help housing policymakers and industry professionals analyze and benchmark the feasibility of innovative zero-energy housing policies and projects.

Into the frame of the French TANDEM project (Tsunamis in the Atlantic and the English ChaNnel: Definition of the Effects through numerical Modelling) Principia has been working on the development and qualification of two in-house CFD software’s: the 2D EOLE-SV (Saint Venant) model for simulation of large scale tsunami propagation from the source up to coastal scale and the 3D EOLE-NS (Navier-Stokes) model dedicated to tsunami coastal impact modelling. This paper presents a large range of test cases carried out into the frame of the project and dedicated to the validation of numerical codes in various tsunami wave conditions. The main aspects of phenomena such as wave generation, propagation and coastal impact are investigated on academic situations. A real case simulation is concerned as well, the devastating 2011 Tohoku event which is compared with in-situ data.

The main regulatory factors during the adaptation of cancer cells to hypoxic stress are the hypoxia-inducible factors (HIFs), which are being increasingly recognized as an interesting and challenging target for the design of new chemotherapeutic molecules. HIF2A was found to have an large internal hydrophilic cavity within its PAS-B domain, unique to this sub-unit and is suggested to be a possible ligand-binding site. Regulation of HIF2A by cellular molecules is still greatly unknown. In This paper we have employed in-silico techniques, such as molecular docking and dynamic simulation, to design new direct inhibitors against HIF-2A subunit via targeting one of its critical domains and the final top screened molecules have been tested on hypoxic cancer cells for further validation of their inhibitory potential. we targeted the hydrophilic cavity inside the PAS-B domain of the HIF2A to identify novel molecules with a high binding capacity. Virtual Screening methodology was used for molecular docking of NSC library against the target domain inside the HIF2A PAS-B domain with the top 5% compounds with significant MolDock and Re-rank scores were selected for further analysis. The NSC 106416, NSC 217021, NSC 217026, and NSC 215639 compounds were selected based on their docking scores. NSC 215639 had the minimum polar solvation energy and also had a relative strong binding energy. NSC 217026 had the strongest binding energy among other compounds.

Nanoparticle (NP) transport is increasingly relevant to subsurface engineering applications such as aquifer characterization, fracture electromagnetic imaging and environmental remediation. An efficient field-scale simulation framework is critical for predicting NP performance and designing subsurface applications. In this work, for the first time, a streamline-based model is presented to simulate NP transport in field-scale subsurface systems. It considers a series of behaviors exhibited by engineered nanoparticles (NPs), including time-triggered encapsulation, retention, formation damage effects and variable nanofluid viscosity. The key methods employed by the algorithm are streamline-based simulation (SLS) and an operator-splitting (OS) technique for modeling NP transport. SLS has proven to be efficient for solving transport in large and heterogeneous systems, where the pressure and velocity fields are firstly solved on underlying grids using finite-difference (FD) methods. After tracing streamlines, one-dimensional (1D) NP transport is solved independently along each streamline. The adoption of OS enhances flexibility for the entire solution procedure by allowing different numerical schemes to solve different governing equations efficiently and accurately. For the NP transport model, an explicit FD scheme is used to solve the advection term, an implicit FD scheme is used for the diffusion term and an adaptive numerical integration is used to solve the retention terms. The model is implemented in an in-house streamline-based code, which is verified against analytical solutions, a commercial FD reservoir simulator (ECLIPSE) and an academic FD colloid transport code (MNMs). For a 1D homogeneous case, the effluent breakthrough curves (BTC) produced by the in-house simulator are in good agreement with the analytical solution and MNMs, respectively. For a two-dimensional (2D) heterogeneous case, the BTC and concentration pattern of the in-house simulator all match well with the solution produced by commercial simulator. Simulations on a synthetic three-dimensional (3D) nanocapsule application engineering design case, are performed to investigate the effect of fluid and NP properties on the displacement pattern of an existing subsurface fluid.

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

Hydrothermal /super critical processes are important process to synthesize materials which are otherwise difficult to form under normal conditions. A mathematical model is developed using standard transport equations to calculate the time for heating of hydrothermal reactor and computer simulation of the model was carried out in SOLIDWORKS® to validate it. The materials used to form reactor vessel were stainless steel (outer body) and TEFLON (PFA) (inner vessel). It was shown that composite wall, its geometry, construction & properties greatly affect the time and pattern of heat transfer. The time calculated and pattern generated were found to be in good agreement with experimental values.

The peri-urban and urban forests in Greece occupy a total area of 105.353 ha. In these vulnerable ecosystems, fire constitutes a natural disaster presenting particular challenges and specific difficulties. These include the high number of visitors as well as forests characteristics - such as the presence of particularly flammable tree species and high accumulation of combustible biomass – that make the on-start of fires more likely. The main purpose of the current research is to identify the optimum combination of silvicultural treatments to efficiently reduce potential forest fire severity and to facilitate their successful suppression by firefighting crews. In order to simulate the basic fire environment of urban forests, two main experimental plots were established and several tree and topographical characteristics were measured. Additionally, the NEXUS wildfire system was used to simulate forest fire potential behavior before and after the adoption of the silvicultural treatments that altered critical characteristics of the forest fire environment. The results clearly show that specific silvicultural prescriptions altered the type of forest fire spreading potential, revealing the overall efficiency of preventing actions during forest management.

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

In this report we have discussed the important role of molecular modeling, especially the use of the molecular dynamics method, in investigating transport processes in nanoporous materials such as membranes. With the availability of high performance computers, molecular modeling can now be used to study rather complex systems at a fraction of the cost or time requirements of experimental studies. Molecular modeling techniques have the advantage of being able to access spatial and temporal resolution which are difficult to reach in experimental studies. For example, sub-Angstrom level spatial resolution is very accessible as is sub-femtosecond temporal resolution. Due to these advantages, simulation can play two important roles: Firstly because of the increased spatial and temporal resolution, it can help understand phenomena not well understood. As an example, we discuss the study of reverse osmosis processes. Before simulations were used it was thought the separation of water from salt was purely a coulombic phenomenon. However, by applying molecular simulation techniques, it was clearly demonstrated that the solvation of ions made the separation in effect a steric separation and it was the flux which was strongly affected by the coulombic interactions between water and the membrane surface. Additionally, because of their relatively low cost and quick turnaround (by using multiple processor systems now increasingly available) simulations can be a useful screening tool to identify membranes for a potential application. To this end, we have described our studies in determining the most suitable zeolite membrane for redox flow battery applications. As computing facilities become more widely available and new computational methods are developed, we believe molecular modeling will become a key tool in the study of transport processes in nanoporous materials.

The effect of configuration of an asymmetric bulky initiator 2,2’-[1,1’-binaphtyl-2,2’-diyl- bis-(nitrylomethilidyne)]diphenoxy aluminum isopropoxide (Ini) on structure of copolymer of asymmetric monomer L,L-lactide (Lac) with symmetric comonomer trimethylene carbonate (Tmc) was studied using polarimetry, dilatometry, SEC and ¹³C NMR. When the S-enantiomer of Ini was used the distribution in copolymer chains at the beginning of polymerization is statistical, with alternacy tendency, changing next through a gradient region to homoblocks of Tmc. When, however, R-Ini was used, the product formed was a gradient oligoblock one, with Tmc blocks prevailing at the beginning, changing to Lac blocks dominating at end part of chains. Initiation of copolymerization with the mixture of both initiator enantiomers (S:R = 6:94) gave multiblock copolymer, of similar features but shorter blocks. Analysis of copolymerization progress required complex analysis of dilatometric data, assuming different molar volume contraction coefficients for units located in different triads. Comonomer reactivity ratios of studied copolymerizations were determined.

At the Atsumi Wind Farm in Aichi Prefecture, Japan, damage to wind turbines occurred frequently due to terrain-induced turbulence. In the present study, numerical analyses of terrain-induced turbulence were conducted by reproducing the topography in the vicinity of the wind turbine sites in high resolution and using RIAM-COMPACT natural terrain version, which is based on large eddy simulation (LES). The results of the diagnoses indicated that, in the case of south-easterly wind, terrain-induced turbulence is generated at a small terrain feature located upstream of Wind Turbine (WT) #2, which serves as the origin of the turbulence. At the Atsumi Wind Farm, a combination of the series of wind diagnoses and on-site operation experience led to a decision to adopt an "automatic shutdown program" for WTs #1 and #2. Here, "automatic shutdown program" refers to the automatic suspension of wind turbine operation upon the wind speed and direction meeting the conditions associated with significant effects of terrain-induced turbulence at a wind turbine site. The adoption of the "automatic shutdown program" has successfully resulted in a large reduction in the number of occurrences of wind turbine damage, thus, creating major positive economic effects. 1) a reduction in the repair costs by 9.322 million yen per year per wind turbine, 2) an increase in the availability factor by 8.05%, and 3) an increase in the capacity factor by 1.7%.

The today competitive advantage of Ready-made garment industries depends on the ability to improve the efficiency and effectiveness of resource utilization. Ready-made garment industries have long historically adopted fewer technological and process advancement as compared to automotive, electronics and semiconductor industries. Simulation modeling of garment assembly line system has attracted a number of researchers as one way for insightful analysis of system behaviour and improving its performance. However, most of simulation studies have considered ill-defined experimental design which cannot fully explore the assembly line design alternatives and does not uncover the interaction effects of the input variables. Simulation metamodeling is an approach to assembly line design which has recently been of interest to so many researchers. However, its application in garment assembly line design has never been well explored. In this paper, simulation metamodeling of trouser assembly line with 72 operations has been demonstrated. The linear regression metamodel technique with resolution-V design was used. The effects of five factors: bundle size, job release policy, task assignment pattern, machine number and helper number on the production throughput of the trouser assembly line were studied. The increase of 28.63% of the production throughput was achieved for the best factors’ setting of the metamodel.

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

The present study explores the effect of using two porous deflectors on the performance of the Savonius wind turbine compared to only one porous deflector. The numerical simulation is performed to solve the unsteady Navier-Stokes equations using the SST k-

Several factors influence traffic congestion and overall traffic dynamics. Simulation modeling has been utilized to understand the traffic performance parameters during traffic congestions. This paper focuses on driver behavior of route selection by differentiating three distinguishable decisions, which are shortest distance routing, shortest time routing and less crowded road routing. This research generated 864 different scenarios to capture various traffic dynamics under collective driving behavior of route selection. Factors such as vehicle arrival rate, behaviors at system boundary and traffic light phasing were considered. The simulation results revealed that shortest time routing scenario offered the best solution considering all forms of interactions among the factors. Overall, this routing behavior reduces traffic wait time and total time (by 69.5% and 65.72%) compared to shortest distance routing.

This paper deals with the characteristics of the cyclone separator from the Lagrangian perspective to design important dependent variables, develops a neural network model for predicting the separation performance parameter, and compares the predictive performance between the traditional surrogate model and the neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based CFD methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, Unsteady-RANS analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the ML model showed the 32.5 % of improvement rate of R2 compared to the traditional MLR considering the nonlinear relationship between the cyclone design variable and the critical diameter. The proposed techniques have proven to be fast and practical tools for cyclone design.