Evolution of Biofilm and Its Effect on Microstructure of Mortar Surfaces in Simulated Seawater

To explore the role of biofilm formation on the corrosion of marine concrete structures, we investigated the attachment of biofilm on mortar surfaces in simulated seawater and the influence of biofilm on the microstructure of mortar surfaces. The results show that the evolution of biofilm on mortar surfaces in simulated seawater is closely related to the corrosion suffered by the mortar, and the process of biofilm attachment and shedding is continuous and cyclical. It is found that the specimens in the absence of biofilm attachment are more severely eroded internally by the corrosive medium in simulated seawater than those in the presence of biofilm attachment. For the specimens without biofilm attachment, after 60 days, gypsum forms,and after 120 days, the number of pores in the mortar is reduced. In contrast, for the specimens in the presence of biofilm attachment, gypsum could only be detected after 90 days, and fewer pores are filled. Therefore, the formation of biofilm could delay the invasion of the corrosive medium into the interior of mortar during the evolution of biofilm on mortar surfaces, mitigating the corrosion of mortars in seawater.

Müller cells are activated in response to retinal outer nuclear layer degeneration in rats subjected to simulated weightlessness conditions

A microgravity environment has been shown to cause ocular damage and affect visual acuity,but the underlying mechanisms remain unclear.Therefore,we established an animal model of weightlessness via tail suspension to examine the pathological changes and molecular mechanisms of retinal damage under microgravity.After 4 weeks of tail suspension,there were no notable alterations in retinal function and morphology,while after 8 weeks of tail suspension,significant reductions in retinal function were observed,and the outer nuclear layer was thinner,with abundant apoptotic cells.To investigate the mechanism underlying the degenerative changes that occurred in the outer nuclear layer of the retina,proteomics was used to analyze differentially expressed proteins in rat retinas after 8 weeks of tail suspension.The results showed that the expression levels of fibroblast growth factor 2(also known as basic fibroblast growth factor)and glial fibrillary acidic protein,which are closely related to Müller cell activation,were significantly upregulated.In addition,Müller cell regeneration and Müller cell gliosis were observed after 4 and 8 weeks,respectively,of simulated weightlessness.These findings indicate that Müller cells play an important regulatory role in retinal outer nuclear layer degeneration during weightlessness.

Simulated patient methodology as a“gold standard”in community pharmacy practice:Response to criticism

The simulated patient methodology(SPM)is considered the“gold standard”as covert participatory observation.SPM is attracting increasing interest for the investigation of community pharmacy practice;however,there is criticism that SPM can only show a small picture of everyday pharmacy practice and therefore has limited external validity.On the one hand,a certain design and application of the SPM goes hand in hand with an increase in external validity.Even if,on the other hand,this occurs at the expense of internal validity due to the trade-off situation,the justified criticism of the SPM for investigating community pharmacy practice can be countered.

Scheduling on tractor and trailer transportation considering the influence of disrupted events based on the contract net and simulated annealing algorithm

To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.

Shallow Convection Dataset Simulated by Three Different Large Eddy Models

Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes,yet its parameterization in numerical models remains a great challenge,partly due to the lack of high-resolution observations.This study describes a large eddy simulation(LES)dataset for four shallow convection cases that differ primarily in inversion strength,which can be used as a surrogate for real data.To reduce the uncertainty in LES modeling,three different large eddy models were used,including SAM(System for Atmospheric Modeling),WRF(Weather Research and Forecasting model),and UCLA-LES.Results show that the different models generally exhibit similar behavior for each shallow convection case,despite some differences in the details of the convective structure.In addition to grid-averaged fields,conditionally sampled variables,such as in-cloud moisture and vertical velocity,are also provided,which are indispensable for calculation of the entrainment/detrainment rate.Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection,the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.

Frontogenesis and Frontolysis of a Cold Filament Driven by the Cross-Filament Wind and Wave Fields Simulated by a Large Eddy Simulation

The variations of the frontogenetic trend of a cold filament induced by the cross-filament wind and wave fields are studied by a non-hydrostatic large eddy simulation. Five cases with different strengths of wind and wave fields are studied.The results show that the intense wind and wave fields further break the symmetries of submesoscale flow fields and suppress the levels of filament frontogenesis. The changes of secondary circulation directions—that is, the conversion between the convergence and divergence of the surface cross-filament currents with the downwelling and upwelling jets in the filament center—are associated with the inertial oscillation. The filament frontogenesis and frontolysis caused by the changes of secondary circulation directions may periodically sharpen and smooth the gradient of submesoscale flow fields.The lifecycle of the cold filament may include multiple stages of filament frontogenesis and frontolysis.

Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

A Unique Modelling Strategy to Dynamically Simulate the Performance of a Lobe Pump for Industrial Applications

The performance of a newly designed tri-lobe industrial lobe pump of high capacity is simulated by using commercial CFD solver Ansys Fluent. A combination of user-defined-functions and meshing strategies is employed to capture the rotation of the lobes. The numerical model is validated by comparing the simulated results with the literature values. The processes of suction, displacement, compression and exhaust are accurately captured in the transient simulation. The fluid pressure value remains in the range of inlet pressure value till the processes of suction and displacement are over. The instantaneous process of compression is accurately captured in the simulation. The movement of a particular working chamber is traced along the gradual degree of lobe’s rotation. At five different degrees of lobe’s rotation, pressure contour plots are reported which clearly shows the pressure values inside the working chamber. Each pressure value inside the working chamber conforms to the particular process in which the working chamber is operating. Finally, the power requirement at the shaft of rotation is estimated from the simulated values. The estimated value of power requirement is 3.61 BHP FHP whereas the same calculated theoretically is 3 BHP FHP. The discrepancy is attributed to the assumption of symmetry of blower along the thickness.

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

Process Simulate在综合实训系统设计中的应用

为解决传统自动化产线编程调试需根据现场实际规划反复调试的问题,借助数字孪生技术,将Process Simulate软件强大的虚实联调技术应用于自动化产线设计、安装、编程、调试、故障解决中。在现实的智能工厂中,往往都是多工作站之间配合进而实现自动化产线的连续运行。多工作站之间的配合就会涉及到通讯,以西门子1500 PLC为例详细讲解如何借助Process Simulate软件实现多站之间的PUT/GET通讯,进而完成各站的PLC程序信号交互,最终完成自动化产线的程序调试。采用Process Simulate软件实现多站之间的通讯,优势体现在:虚拟的PLC即可实现多站之间的通讯,解决无真实设备状态下的编程和通讯,进而验证Process Simulate在智能装备多工作站中的应用高效性。

基于Teamcenter Process Simulate印章定制的生产线仿真设计

基于Teamcenter Process Simulate进行了印章定制生产线仿真设计。本设计是完成印章的组装和包装工作,模拟实际生产过程。整个生产过程是由仓储供料站提供原料,运输站将其送至加工站加工,送至装配站进行组装,组装完后经过机器进行检测,然后送至包装台进行产品包装,最后由AGV小车运输至仓储放料台进行产品入库。设计以出料、运送物料、产品加工、组装、检测、包装、运送、入库等执行单元作为自动生产线的整体设计,组成了自动生产线的生产平台。系统的总控是由一台PLC承担其控制任务。结合工作中的经验,通过机电一体化设计将生产流水线设计方案嵌入到具体的Process Simulate虚拟仿真中,通过PLCSIM Advanced软件创建虚拟PLC,同时构建PLC与Process Simulate通信的桥梁,进而通过编程控制实现印章定制化生产实训系统自动化产线的仿真调试。通过仿真测试,能大大提高实际生产效率。

Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm

Cascade refrigeration system(CRS)can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system,making it more widely used in low-temperature industry processes.The synthesis of a CRS with simultaneous consideration of heat integration between refrigerant and process streams is challenging but promising for significant cost saving and reduction of carbon emission.This study presented a stochastic optimization method for the synthesis of CRS.An MINLP model was formulated based on the superstructure developed for the CRS,and an optimization framework was proposed,where simulated annealing algorithm was used to evolve the numbers of pressure/temperature levels for all sub-refrigeration systems,and particle swarm optimization algorithm was employed to optimize the continuous variables.The effectiveness of the proposed methodology was verified by a case study of CRS optimization in an ethylene plant with 21.89%the total annual cost saving.

Effects of simulated zero gravity on adhesion,cell structure,proliferation,and growth behavior,in glioblastoma multiforme

All life on Earth has evolved under the influence of continuous gravity,and methods have been developed to balance this influence with the biological evolution of organisms at the cellular and system levels.However,when exposed to zero gravity in space,the balance between cell structure and external forces is destroyed,resulting in changes at the cellular level(e.g.,cell morphology,adhesion,viability,apoptosis,etc.),and understanding the molecular mechanism of cell response to zero gravity will help to cope with diseases that rely on mechanical response.Therefore,biological research in space and zero gravity is a unique step in developing the best anti-cancer treatments,which is a great challenge to humanity.In this study,multicellular glioma cancer cells from a brain tumor in a 72-year-old Iraqi patient were subjected to simulated zero gravity for 24 h,and the results showed that most of the cells lost their adhesion,which is considered to be the first step toward cell apoptosis.In addition to the formation of multicellular spheroids,the results also showed that the inhibition rate for cell death was 32%in comparison to the control cells.Moreover,the cells showed a clear change in their cellular morphology and growth behavior.These results give new hope for fighting cancer distinctively,and such a treatment method has no side effects in comparison to traditional chemical and radiological ones.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

THD Reduction for Permanent Magnet Synchronous Motor Using Simulated Annealing

Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion(THD)technique.Reduced THD achieves lower peak current,higher efficiency and longer equipment life span.Simulated annealing(SA)is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine(PMSM).The parameters of direct torque controllers(DTC)for the drive are automatically adjusted by the optimization algorithm.Advantages of the PI-Fuzzy-SA algorithm are retained when used together.It also improves the rate of system convergence.Speed response improvement and har-monic reduction is achieved with SA-based DTC for PMSM.This mechanism is known to be faster than other algorithms.Also,it is observed that as compared to other algorithms,the projected algorithm yields a reduced total harmonic distor-tion.As a result of the employment of Space Vector Modulation(SVM)techni-que,the system is resistant to changes in motor specifications and load torque.Through MATLAB&Simulink simulation,the experiment is done and the per-formance is calculated for the controller.

An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.

Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine,for example,traditional Chinese medicine(TCM),Japanese traditional herbal medicine,and traditional Korean medicine(TKM).The diagnosis procedure is mainly based on the expert’s knowledge depending upon the visual inspec-tion comprising color,substance,coating,form,and motion of the tongue.But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective.Therefore,computer-aided tongue analyses have a greater potential to present objective and more consistent health assess-ments.This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis(SADTL-TIADD)model.The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality.Next,the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors.In turn,the SA with the ELM model enhances classification efficiency for disease detection and classification.The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work.A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm.The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric conditions.The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower heights.The first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno algorithm.Secondly,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m heights.The estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height.This procedure is repeated continuously until theWS is estimated at a height of 180 m.The RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS vertically.The estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.