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.

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.

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.

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.

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.

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.

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.

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.

Analysis of Growth Characteristics and Differentially Expressed Homologous Genes in Rhodobacter sphaeroides under Normal and Simulated Microgravity Conditions

The term “microgravity” is used to describe the “weightlessness” or “zero-g” circumstances that can only be found in space beyond earth’s atmosphere. Rhodobacter sphaeroides is a gram-negative purple phototroph, used as a model organism for this study due to its genomic complexity and metabolic versatility. Its genome has been completely sequenced, and profiles of the differential gene expression under aerobic, semi-aerobic, and photosynthetic conditions were examined. In this study, we hypothesized that R. sphaeroides will show altered growth characteristics, morphological properties, and gene expression patterns when grown under simulated microgravity. To test that, we measured the optical density and colony-forming units of cell cultures grown under both microgravity and normal gravity conditions. Differences in the cell morphology were observed using scanning electron microscopy (SEM) images by measuring the length and the surface area of the cells under both conditions. Furthermore, we also identified homologous genes of R. spheroides using the differential gene expression study of Acidovorax under microgravity in our laboratory. Growth kinetics results showed that R. sphaeroides cells grown under microgravity experience a shorter log phase and early stationary phase compared to the cells growing under normal gravity conditions. The length and surface area of the cells under microgravity were significantly higher confirming that bacterial cells experience altered morphological features when grown under microgravity conditions. Differentially expressed homologous gene analysis indicated that genes coding for several COG and GO functions, such as metabolism, signal-transduction, transcription, translation, chemotaxis, and cell motility are differentially expressed to adapt and survive microgravity.

Biodegradation Behavior of Starch in Simulated White Water System of Old Corrugated Cardboard Pulping Process

Considering the serious barriers/issues induced by the accumulated starch generated in white water system of old corrugated cardboard(OCC)pulping process,large amounts of accumulated starch in white water would be decomposed by microorganisms and could not be utilized,thereby resulting in severe resource wastage and environmental pollution.This study mainly explored the effects of biodegradation/hydrolysis conditions of the two types of starch substrates(native starch and enzymatically(α-amylase)hydrolyzed starch),which were treated via microorganism degradation within the simulated white water from OCC pulping system and their biodegradation products on the key properties were characterized via X-ray diffraction(XRD),Fourier-transform infrared spectroscopy(FT-IR),and gel permeation chromatography(GPC)technologies.The effects of system temperature,pH value,starch concentration,and biodegradation time on starch biodegradation ratio and the characteristics of obtained biodegradated products from the two types of starches were studied.In addition,the effect ofα-amylase dosage on the biodegradation ratio of enzymatically hydrolyzed starch and its properties was investigated.It was found that the native starch presented a maximal degradation ratio at a system temperature of 55℃and pH value range of 5-7,respectively,the corresponding starch concentration within simulated white water system was 200 mg/L.Whereas the enzymatically hydrolyzed starch exhibited a highest degradation ratio at a system temperature of 50℃and pH value of 5.5,respectively,and the corresponding starch concentration within simulated white water system was 100 mg/L.It was verified that native starch is more readily bio-hydrolyzed and biodegradation-susceptive by microorganisms in simulated white water system of OCC pulping process,while the enzymatically hydrolyzed starch exhibits better biodegradation/hydrolysis resistance to the microbial degradation than that of native starch.This study provides a practical and interesting approach to investigate the starch hydrolysis or biodegradation behaviors in white water system of OCC pulping process,which would greatly contribute to the full recycling and valorized application of starch as a versatile additive during paperboard production.

Comparison between compression tested and simulated Mg-6.3Gd bone scaffolds produced by binder based additive manufacturing technique

In this study,a binder based 3D-printing technology viz.Fused Granular Fabrication(FGF)technique was used to produce interconnected and open porous Mg-6.3Gd bone scaffolds for compression test analyses.The consolidation of the green parts(as printed scaffolds)was performed using solvent debinding in cyclohexane and subsequent conventional sintering in argon atmosphere.Compression tests were performed on as sintered parts.Additionally,a simulation strategy was developed for modeling the compression behavior of the sintered parts,utilizing the data from the experimental results.The experimental compression test results and the simulation strategy for the compression behavior of the 3D-printed scaffolds demonstrated good agreement.

RFID Network Planning Optimization Using a Genetic-Simulated Annealing Combined Algorithm

The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification(RFID)network planning in an emergency department of a hospital.Accordingly,though genetic algorithm(GA)and simulated annealing(SA)have advantages and disadvantages,but they are also complementary.Hence,the combined algorithm not only takes advantages of the two methods,but also avoids their disadvantages.The simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID readers.Besides,the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.

Web services composition with QoS bound based on simulated annealing algorithm

In order to enable quality-aware web services selection in the process of service composition,this paper first describes the non-functional requirements of service consumers and the quality of elementary service or composite service as a quality vector,and then models the QoS（quality of service）-aware composition as a multiple criteria optimization problem in extending directed graph.A novel simulated annealing algorithm for QoS-aware web services composition is presented.A normalizing for composite service QoS values is made,and a secondary iterative optimization is used in the algorithm.Experimental results show that the simulated annealing algorithm can satisfy the multiple criteria and global QoS requirements of service consumers.The algorithm produces near optimum solution with much less computation cost.