Simulated Method Research Articles

Analysis and identification of degradation products in gas, particle, and liquid phases of polypropylene and polyethyleneterephthalate microplastics aging through non-thermal plasma simulation.

Plastic aging can cause alterations in the physical and chemical characteristics of plastics, as well as their behavior in the environment. Due to the extremely slow natural aging process, laboratory simulated aging methods have to be used. In this study, non-thermal plasma (NTP) was adopted to investigate the aging process of polypropylene (PP) and polyethylene terephthalate (PET) microplastics. Various analytical instruments, including proton transfer reaction mass spectrometry and single-particle aerosol mass spectrometry, were employed to examine and identify the organic constituents of the gas, liquid, and particle phase degradation products, as well as to monitor the degradation process. The results showed that after 90min of aging, both PP and PET surfaces showed yellowing, and the carbonyl index of PP increased while that of PET decreased, with an increase in crystallinity. The organic components of reaction products, such as ketones, esters, acids, and alcohols, increased with longer aging times. Gas products mainly contain aromatic hydrocarbons, while particles from aged PET contain compounds with benzene rings and metal elements. Liquid products from aged PP show a significant presence of branched alkanes. Based on this analysis, degradation mechanisms of PP and PET by NTP were proposed. This investigation represents the initial systematically exploration of the release of organic substances during the degradation of microplastics mediated by NTP. It provides significant insights into the detrimental organic compounds emitted during this process, thereby offering valuable information for understanding the environmental and human health implications of natural microplastic degradation. Furthermore, it addressed the requirements for increased attention to the potential environmental risks associated with these harmful components.

Numerical and experimental research on the performance of an iodine-fed Hall thruster

The development of space technology brings about an increasing demand for electric thrusters. Iodine has been considered a feasible alternative to resource-critical xenon. Numerical and experimental methods are used to study the performance of the iodine-fed Hall thruster. The ionization and acceleration characteristics are investigated in the simulation with a particle-in-cell/Monte Carlo collision model. Different from previous simulations of noble gas, the dissociation equation of molecular iodine is added to the code for propulsive performance evaluation. A dissociation region in the discharge chamber is presented and described in detail. The propulsive performances of the iodine-fed Hall thruster are calculated by the plasma parameters. Meanwhile, the experimental measurements are finished for model verification and performance comparison at nominal conditions. The measured data is slightly lower than the simulated data. The error between them is about 1.65%. The reasons are analyzed and discussed. Then, the performance is predicted and measured using this simulated and experimental method within 20% mass flow rate and 10% anode voltage fluctuations relative to the nominal condition. Further optimizations of the simulation and experiment are in progress.

Simulation and Validation of an 8 × 8 Scaled Electric Combat Vehicle

In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios.

GA-BPNN Prediction Model of Broadband Ground Motion Parameters in Tianjin Area Driven by Synthetic Database Based on Hybrid Simulated Method

GA-BPNN Prediction Model of Broadband Ground Motion Parameters in Tianjin Area Driven by Synthetic Database Based on Hybrid Simulated Method

Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries

At present, the accurate establishment of the battery model and the effective state of health (SOH) estimation under actual energy storage conditions have become the main problems in new energy storage stations. Therefore, a SOH estimation method based on cooperative competitive particle swarm optimization (CCPSO) and nonlinear coefficient temperature decreasing simulated annealing-back propagation (NSA-BP) is proposed. The novelty of this research mainly includes the design of extraction methods in different health indicators (HIs) and the construction of developed NSA-BP network for SOH estimation. In this research, the contributions of SOH estimation are mainly to assist in battery replacement and provide relevant economic reference. Low-rate constant current energy storage degradation experiments and a variable-rate energy storage degradation experiment are performed for different battery packs at 25 °C. The experimental results indicate that the root mean square error (RMSE) and the mean absolute error (MAE) of the proposed method are 0.00588 and 0.00481 under the 0.5 rate condition, and the corresponding values are 0.00732 and 0.00639 under the variable-rate condition. Under the same condition, the proposed SOH estimation method is superior to the methods before improvement in RMSE and MAE, which can provide a basis for efficient monitoring of energy storage batteries.

Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing

AbstractThe construction of pavements requires the complete identification of the soils in place and of the added materials. This identification consists in determining the class of the soils and in evaluating their bearing capacity through the California bearing ratio (CBR) index. Obtaining the CBR index is very costly in terms of time and financial resources, especially when it is a large-scale project. It thus leaves prospects of obtaining it by simpler processes; hence, it arises the need to find simpler processes compared to classical processes. This study develops models for predicting the CBR index from physical properties that are less complex to obtain, based on deep neural networks. To achieve this, three databases were used. A first database consists of the proportion of fines, the Atterberg limits and the Proctor references of the soils. A second database uses the methylene blue value instead of the Atterberg limits, and a third database uses only the proportion of fines and the Proctor soil reference. On each of the databases, a deep neural network model was developed using dense layers, regularization layers, residual blocks and parallelization in TensorFlow to predict the CBR value. Each model was formed by combining several deep neural networks developed according to specific architectures. To expedite training, the simulated annealing method was employed to optimize hyperparameters and define the optimal configuration for each network. The predictions obtained are correlated with the true values from 83.6 to 96.5%. In terms of performance, the models have a mean deviation ranging from 3.74 to 5.96%, a maximum deviation ranging from 12.43 to 16.2% and a squared deviation ranging from 0.781 to 2.189. The results suggest that the variable VBS has a negative impact on the accuracy of the networks in predicting the CBR index. The developed models respect the confidence threshold (± 10%) and can be used to set up a local or regional geotechnical platform.

Optimal arrangement of heliostat field based on simulated annealing algorithm

In this study, a simulated annealing algorithm is proposed as an optimisation method for the mirror arrangement of heliostat mirrors in a tower solar thermal power plant. Firstly, the optimal number and position of heliostats in the whole mirror field in radial staggered layout (EB unobstructed layout) were solved using simulated annealing method. Then further optimisation, i.e. changing the mirror width, mirror height and mounting height of the heliostats and solving for the optimal number and position of heliostats using simulated annealing again, resulted in an increase in the number of heliostats in the optimised mirror field by 77.92% but a decrease in the total area by 18.77%, and an increase in the average annual thermal power output per unit area of the mirrors by 4.76%. Therefore, this result shows that the heliostat field optimised using the simulated annealing algorithm has a higher efficiency and a smaller footprint.

Unsupervised Wavelet-Feature Correlation Ratio Markov Clustering Algorithm for Remotely Sensed Images

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. It can quickly obtain the best class centers at each clustering temperature. The experimental results of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its acceptable clustering accuracy and high convergence velocity.

Potential Regulatory Gene Network Associated with the Ameliorative Effect of Oat Antibacterial Peptides on Rat Colitis.

Oat protein is unstable in intestinal fluid digestion, and it is easily degraded by trypsin and chymotrypsin, producing low molecular weight peptides. Endopeptidase hydrolysis can improve the bioavailability of active peptides and avoid further digestion in the gastrointestinal tract. Antimicrobial peptides (AMPs) can effectively improve host immunity, but most related studies focus on physiology and ecology, and there are few reports on their molecular level. Therefore, in this article, oat peptides were prepared via the simulated digestion method in vitro, and the main metabolites and action factors affecting colitis were screened by using the multi-omics methods in a high-throughput mode to analyze the effect and mechanism of colitis. Firstly, oat antimicrobial peptides were prepared from cationic resin combined with HPLC, and the anti-inflammatory effects of antimicrobial peptides were analyzed in vitro through the use of human colon epithelial (HCoEpiC) anti-inflammatory cells. In vivo experiments using rats have verified that AMPs can effectively prevent colitis caused by dextran sodium sulfate (DSS), reduce intestinal inflammatory cell infiltration and glandular disappearance in the colon, and reduce the apoptosis rate of colon cells. Secondly, metabolomics and transcriptomics were combined to analyze the mechanism of preventing enteritis, and it was found that oat antimicrobial peptides can promote DAG diglycerol production and inhibit the activation of T helper cells (TH), resulting in the down-regulation of key factors in the main downstream pathways of TH1, TH2 and TH17, and inhibit the production of inflammatory cells. At the same time, AMP can activate the wnt pathway, improve the expression of key genes of wnt and frizzled, promote the generation of intestinal stem cells, facilitate the differentiation and repair of intestinal epithelial cells, and prevent the generation of enteritis. Finally, the underlying genetic regulatory network of the important pathway was constructed from the effect of AMP on rat colitis.

Estimation of Poverty Measures for Small Areas Under a Two-fold Nested Error Linear Regression Model: Comparison of Two Methods

Demand for reliable statistics at a local area (small area) level has greatly increased in recent years. Traditional area-specific estimators based on probability samples are not adequate because of small sample size or even zero sample size in a local area. As a result, methods based on models linking the areas are widely used. World Bank focused on estimating poverty measures, in particular poverty incidence and poverty gap called FGT measures, using a simulated census method, called ELL, based on a one-fold nested error model for a suitable transformation of the welfare variable. Modified ELL methods leading to significant gain in efficiency over ELL also have been proposed under the one-fold model. An advantage of ELL and modified ELL methods is that distributional assumptions on the random effects in the model are not needed. In this article, we extend ELL and modified ELL to two-fold nested error models to estimate poverty indicators for areas (say a state) and subareas (say counties within a state). Our simulation results indicate that the modified ELL estimators lead to large efficiency gains over ELL at the area level and subarea level. Further, modified ELL method retaining both area and subarea estimated effects in the model (called MELL2) performs significantly better in terms of mean squared error (MSE) for sampled subareas than the modified ELL retaining only estimated area effect in the model (called MELLI). AMS Subject Classification: 62D05, 62G09

Insights into the role of RAHB and IHB interactions for the structure- Activity relationship of caged xanthone Morellic Acid: Spectroscopic and DFT exploration, molecular docking, and molecular dynamics simulation studies as an Antituberculosis agent

The current study elaborates on experimental and simulated methods using density functional theory (DFT) to analyze the structure, electronic characteristics, and vibrational features of morellic acid, an isolate from Garcinia wightii. The in-depth examination of the stabilized structure indicates that the compound achieves equilibrium through conjugative, hyperconjugative, resonance-assisted hydrogen bonding (RAHB), and intramolecular hydrogen bonding (IHB) interactions, resulting in a well-supported conformation. The vibrational spectra were recorded, and the wavenumbers were comprehensively assigned using Potential Energy Distribution with a scaled quantum mechanical force field. Natural bond orbital (NBO) studies were utilized to calculate donor and acceptor bond interaction energy and electron densities. The TD-DFT computations were performed in solvent (chloroform) and gaseous phase, which agreed with the experimental values. Molecular electrostatic potential (MEP), reduced density gradient (RDG), electron localized function (ELF), and localized orbital locator (LOL) of the molecule were also analyzed. The molecular docking studies revealed that the compound holds great promise as a drug candidate for tuberculosis treatment, with the most thermodynamically stable binding score of -11.542 kcal/mol with 4BFS (protein of Mycobacterium tuberculosis PanK). The molecular dynamics (MD) simulation studies examined the stability and hydrogen bonding system of the ligand-protein complex formed between the molecule and the essential target protein for 100 ns. The findings revealed that the complex remained stable with minimal fluctuations throughout the simulation, suggesting that the molecule may prevent tuberculosis and be used as a treatment candidate.

Minimize population exposure to vehicle-generated emissions by road pricing

Vehicular emissions, as a major source of urban air pollution, are threatening public health. With a goal to regulate population exposure to vehicular emissions, three bilevel road pricing models are established to minimize the exposure to vehicular emissions for on-road travelers, near-road residents, and all populations in the study network. A simulated annealing-based method is applied to solve these models based on a first-best pricing scheme. For ease of implementation, a second-best pricing scheme is then proposed and solved based on a meta-heuristic approach coupled with genetic algorithm with elitism and simulated annealing. The proposed method is tested using the Nguyen-Dupuis network and the Sioux Falls network. It is found that a tradeoff exists between the objectives of minimizing the emission exposure levels of travelers and near-road residents. Another tradeoff is found between the objectives of minimizing total population exposure and total travel time. Results from the numerical example indicate that the optimality results obtained in the first-best pricing scheme may also be approximated by the second-best pricing scheme with fewer tolling locations and less toll collection. The conclusions drawn from the paper could potentially assist policymakers in formulating road pricing policies that prioritize environmental concerns.

Stable and efficient seismic impedance inversion using quantum annealing with L1 norm regularization

Abstract Seismic impedance inversion makes a significant contribution to locating hydrocarbons and interpreting seismic data. However, it suffers from non-unique solutions, and a direct linear inversion produces large errors. Global optimization methods, such as simulated annealing, have been applied in seismic impedance inversion and achieved promising inversion results. Over the last decades, there has been an increasing interest in quantum computing. Owing to its natural parallelism, quantum computing has a powerful computational capability and certain advantages in solving complex inverse problems. In this article, we present a stable and efficient impedance inversion using quantum annealing with L1 norm regularization, which significantly improves the inversion accuracy compared to the traditional simulated annealing method. Tests on a one-dimensional 10-layer model with noisy data demonstrate that the new method exhibits significantly improved accuracy and stability. Additionally, we perform seismic impedance inversion for synthetic data from the overthrust model and field data using two methods. These results demonstrate that the quantum annealing impedance inversion with L1 norm regularization dramatically enhances the accuracy and anti-noise ability.

Low-loss integrated dynamic polarization controller based on silicon photonics

<sec>A dynamic polarization controller (DPC) is an important component in fiber optic communication, optical imaging, and quantum technologies. The DPC can transform any input state of polarization (SOP) into any desired SOP to overcome polarization-related impairments resulting from high internally and externally induced birefringence. In this work, a low-loss silicon photonics-integrated DPC is designed and demonstrated experimentally. The whole chip is fabricated by using industry-standard silicon-on-insulator technology. Using the edge-coupling method, the coupler loss is reduced to less than 2 dB, and the total loss of DPC is reduced to 5.7 dB. Using a variable-step simulated annealing method, for a low-noise photodetector and high-static-extinction-ratio device, a dynamic polarization extinction ratio can reach more than 30 dB. The size of the DPC on the chip is 5.20 mm × 0.12 mm × 0.80 mm.</sec><sec>The DPC utilizes a 0°/45°/0°/45° structure, which can realize arbitrary polarization-based coordinate conversion with endless polarization control. The 0° and 45° transform structures and matrices are presented, and the principle of the 0° and 45° structures is explained in detail by using the Poincaré sphere.</sec><sec>A simulation using Lumerical is conducted to optimize the polarization rotator-splitter, which can transform the TM<sub>0</sub> mode light in one waveguide into the TE<sub>0</sub> mode light in the other waveguide while the TE<sub>0</sub> mode light in one waveguide remains unchanged. Based on the optimized structure, the static polarization extinction ratio of DPC can be measured to be a value greater than 40 dB.</sec><sec>The thermal phase shift (TPS) is characterized by using a Mach–Zehnder modulator. The length of the TPS is 400 μm, and the resistance of the metal heater is 2.00 kΩ. The maximum power consumed by the four TPSs is a total of 0.2 W. The modulation bandwidth of the DPC designed by our group is approximately 30 kHz. By considering an applied voltage of 5.6 V in the case of the TPS, the bandwidth–voltage product is 5.6 × 30 = 168 kHz·V.</sec><sec>To optimize the DPC parameters, such as the step length, electronic noise, and static polarization extinction ratio, numerical simulation results of the simulated annealing approach are analyzed in detail.</sec><sec>In conclusion, a low-loss silicon photonics-integrated DPC is designed and demonstrated experimentally. A dynamic polarization extinction ratio is obtained to be greater than 30 dB by using the variable-step simulated annealing method. The DPC is expected to be utilized in fiber or quantum communication systems to minimize size and further decrease costs.</sec>

Low-loss integrated dynamic polarization controller based on silicon photonics

<sec>A dynamic polarization controller (DPC) is an important component in fiber optic communication, optical imaging, and quantum technologies. The DPC can transform any input state of polarization (SOP) into any desired SOP to overcome polarization-related impairments resulting from high internally and externally induced birefringence. In this work, a low-loss silicon photonics-integrated DPC is designed and demonstrated experimentally. The whole chip is fabricated by using industry-standard silicon-on-insulator technology. Using the butting coupling method, the coupler loss is reduced to less than 2 dB, and the total loss of DPC is reduced to 5.7 dB. Using a variable-step simulated annealing method, for a low-noise photodetector and high-static-extinction-ratio device, a dynamic polarization extinction ratio can reach more than 30 dB. The size of the DPC on the chip is 5.20 mm × 0.12 mm × 0.80 mm.</sec><sec>The DPC utilizes a 0°/45°/0°/45° structure, which can realize arbitrary polarization-based coordinate conversion with endless polarization control. The 0° and 45° transform structures and matrices are presented, and the principle of the 0° and 45° structures is explained in detail by using the Poincaré sphere.</sec><sec>A simulation using Lumerical is conducted to optimize the polarization rotator-splitter, which can transform the TM<sub>0</sub> mode light in one waveguide into the TE<sub>0</sub> mode light in the other waveguide while the TE<sub>0</sub> mode light in one waveguide remains unchanged. Based on the optimized structure, the static polarization extinction ratio of DPC can be measured to be a value greater than 40 dB.</sec><sec>The thermal phase shift (TPS) is characterized by using a Mach–Zehnder modulator. The length of the TPS is 400 μm, and the resistance of the metal heater is 2.00 kΩ. The maximum power consumed by the four TPSs is a total of 0.2 W. The modulation bandwidth of the DPC designed by our group is approximately 30 kHz. By considering an applied voltage of 5.6 V in the case of the TPS, the bandwidth–voltage product is 5.6 × 30 = 168 kHz·V.</sec><sec>To optimize the DPC parameters, such as the step length, electronic noise, and static polarization extinction ratio, numerical simulation results of the simulated annealing approach are analyzed in detail.</sec><sec>In conclusion, a low-loss silicon photonics-integrated DPC is designed and demonstrated experimentally. A dynamic polarization extinction ratio is obtained to be greater than 30 dB by using the variable-step simulated annealing method. The DPC is expected to be utilized in fiber or quantum communication systems to minimize size and further decrease costs.</sec>

A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution

Production and distribution are two essential parts in supply chains. An integration of production and distribution has received amount of attention from both academia and industry. This article investigates an integrated scheduling problem of distributed flexible job shops and distribution. First, a mixed integer programming model is developed to minimize maximum completion time. Second, a cooperative evolutionary algorithm with simulated annealing methods is proposed in consideration of the problem's characteristics. A three-link integer string is designed to represent three parts, i.e., factory assignment, machine assignment and operation sequence, in a solution. Three populations are constructed to search the above three parts via genetic operations and simulated annealing methods, respectively. A cooperation strategy is designed to generate a schedule. Then, a heuristic rule is devised to determine the delivery scheme for distribution. Finally, experiments are carried out via extending a set of benchmark instances. The proposed algorithm is compared to four state-of-the-art algorithms and an exact solver CPLEX. The results and discussions verify that the developed model and method have strong competitiveness for solving the considered problems.

Deployment of edge computing nodes in IoT: Effective implementation of simulated annealing method based on user location

Deployment of edge computing nodes in IoT: Effective implementation of simulated annealing method based on user location

The mechanism underlying the transitions between stripes, helices, and stacked toroids in the cylindrical shell formed by AB diblock copolymers on a long nanocylinder.

The self-assembly of block copolymers on nanocylinders has attracted a lot of interest due to its potential application in biomedicine and other fields. In this study, the self-assembly phase behavior of AB diblock copolymers on long nanocylinders in soft confinement has been studied by using a simulated annealing method. A square phase diagram of the morphology was constructed by increasing the number of chains of copolymers (cn) and the cylindrical diameter (D). As a result, morphological transitions from striped to helical and axially stacked toroids, as well as reversible transitions, started to appear. By analyzing the chain packing in a fan-shaped region and calculating the mean-square end-to-end distance (DEE2) of the copolymers and number of AB contacts, both types of transitions were found to be driven by the competition between conformational entropy and AB interfacial energy. The number of stripes increased and the helical angle decreased with the increase in cylinder diameter. The chirality of the helix was found to be random.

Core-shell particle formation via Co-assembly of AB diblock copolymers and nanoparticles in 3D soft confinement.

Core-shell particle formation via co-assembly of AB diblock copolymers and nanoparticles in 3D soft confinement was studied using a simulated annealing method. Several sequences of soft confinement-induced core-shell particles were predicted as functions of the volume fraction of the nanoparticle to core-shell particles, the incompatibility between blocks, the volume fractions of A-blocks, the chain length of AB diblocks, the eccentricity of the nanoparticle, and the initial concentration of copolymers. Simulation results demonstrate that those factors are able to tune the morphology of the core-shell particles precisely. Calculated data indicate that the copolymer chain was located between a hard confinement wall composed of the nanoparticle and a soft confinement wall composed of solvents, and the arrangement direction of the copolymer chains was in a competitive equilibrium between the two. We anticipate that this work will be helpful and instructive for the preparation of polymer shells with different structures and shapes, as well as the study of self-assembly morphology of copolymers in a complex confinement systems.

Assessment of students’ pharmacist competency in dispensing cough medicines for self-medication: a simulated patient study

Background Cough is a symptom for which patients present to community pharmacists for relief. As future pharmacists, pharmacy students must be evaluated for their competence in providing self-medication services. Aims To measure the Indian pharmacy students’ capability (competency) in delivering self-medication consultation service (SMCS) to simulated cough patients and to identify the factors associated with the provision of appropriate advice. Settings and design A prospective, interventional, cross-sectional study using a simulated patient method describing the product and symptom-based request of chronic cough was used on students from a pharmacy school in India. The type and quantity of information gathered and advice delivered by student pharmacists were recorded. Materials and methods The data collected before and after pharmacy students received SMCS training were summarized. Statistical analysis used A descriptive-statistics was performed to identify the factors associated with the provision of advice. Result Participated students gathered information from simulated patients before providing advice to them. Direct medical referral which is considered optimized advice was provided by 46.7% and 91.1% of the 90 participants, before and after receiving self-medication consultation training (SMCT), respectively. Participants who gathered information on the duration and the nature of cough, past medical conditions, and medication use were positively associated with the delivery of optimum recommendations. Conclusion After receiving SMCT, pharmacy students’ competency to provide SMCS improved. Future studies are required to determine the elements that influence pharmacy students’ competence in providing SMCS.