Simulated Process Research Articles

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

Research on the conversion pattern of 6-gingerol in stir-fried ginger and the protective activity mechanism of its conversion products on HUVECs

Degradation of veterinary antibiotics with biochar addition in the simulated liquid fertilization process.

Impact of alginate coating on the stability and resveratrol delivery performance of hollow gliadin nanoparticles.

The global transition toward low-carbon energy has positioned hydrogen (H₂) as a key renewable fuel, particularly for applications in fuel cells that require ultra-high purity. Ensuring hydrogen quality is essential to prevent catalyst poisoning and system degradation, as defined in ISO 14687:2019 standards. This study presents a simulation-based analysis of hydrogen purity using a Gas Chromatography–Mass Spectrometry (GC–MS) modeling approach to evaluate three production pathways: green hydrogen (from electrolysis), grey hydrogen (from steam methane reforming), and a fuel cell–grade feedstock.The simulation predicts impurity profiles such as O₂, N₂, CO, CO₂, CH₄, sulfur compounds, and water vapor, comparing each with ISO threshold limits. Results indicate that green hydrogen generally complies with ISO standards, while grey hydrogen exceeds CO₂ and sulfur limits. The fuel cell–grade sample shows near-complete conformity due to simulated purification processes such as pressure swing adsorption.These findings highlight that analytical modeling can effectively predict hydrogen quality and compliance potential across different production routes. The study emphasizes that advancing hydrogen technology requires not only cleaner production methods but also reliable analytical simulations to support quality assurance and sustainability in future hydrogen economies.

Attention has become a vital form of capital in the digital age, yet the mechanisms underlying its allocation on social media remain poorly understood. Using a nationally representative, online and offline-integrated dataset of a Generation Z cohort in China, we provide large-scale evidence on the determinants of success in attracting attention. Our findings reveal that 'how you express yourself' (using various emojis and expressing multiple emotions) is more influential than 'who you are' (in terms of gender, education, family background and personality traits) in attracting attention on social media. Further analysis confirms a causal effect of the variety of emojis and types of emotions on attracted attention, while simulation processes using agent-based models suggest that empathy evocation is the primary underlying mechanism. We also show that the mode of expression is largely independent of individual characteristics and that the attention gained from highly appealing expressions is easier to acquire than to sustain, as it is highly sensitive to changes in expression modes over time. Overall, our research identifies three key features of attention capital allocation on social media: low alignment with traditional resources, considerable manipulability and ease of acquisition but difficulty sustaining it over time.

The virtual content in augmented reality (AR) can introduce misleading or harmful information, leading to semantic misunderstandings or user errors. In this work, we focus on visual information manipulation (VIM) attacks in AR, where virtual content changes the meaning of real-world scenes in subtle but impactful ways. We introduce a taxonomy that categorizes these attacks into three formats: character, phrase, and pattern manipulation, and three purposes: information replacement, information obfuscation, and extra wrong information. Based on the taxonomy, we construct a dataset, AR-VIM, which consists of 452 raw-AR video pairs spanning 202 different scenes, each simulating a real-world AR scenario. To detect the attacks in the dataset, we propose a multimodal semantic reasoning framework, VIM-Sense. It combines the language and visual understanding capabilities of vision-language models (VLMs) with optical character recognition (OCR)-based textual analysis. VIM-Sense achieves an attack detection accuracy of 88.94% on AR-VIM, consistently outperforming vision-only and text-only baselines. The system achieves an average attack detection latency of 7.07 seconds in a simulated video processing framework and 7.17 seconds in a real-world evaluation conducted on a mobile Android AR application.

This study investigates the impact of groove implementation on the hydraulic performance of sharp-edged trapezoidal side weirs, focusing on discharge coefficients and shear stress behavior. The simulation processes were carried out using the VOF (Volume of fluid) methodology in combination with the RNG (Re-normalize group) model for turbulence. The validation with experimental data by comparison showed that the relative error in the range of 0.4-2.6%. It was found from the results that the discharge coefficient increases in the no-grooved model and decreases in the grooved model. The identified variation of the discharge coefficient range through different Froude numbers lies between 0.6 and 0.8, where the discharge coefficient of the no-grooved model is larger by 2.68% compared to that of the grooved model. The grooved model was more effective for lower flow rates, while the no-grooved model was more effective for higher flow rates. In all cases, in both models, the discharge coefficient increases with the Froude number, with a greater increase observed in the no-grooved configuration (19.64% higher). The research indicated that grooves significantly reduce shear stresses at the crest of the weir, reducing further damage to the structure. The variation in shear stress between the two models was most evident under high flow conditions, demonstrating the efficiency of the grooved model in reducing harmful stresses and energy dissipating.

Abstract Turbulent mixing in ocean boundary layers is often fully parameterized as a subgrid‐scale process in realistic ocean simulations. However, recent submesoscale modeling studies have advanced to a horizontal grid spacing of (10 m) that is comparable to, or even smaller than, the typical depth of the turbulent surface boundary layer. Meanwhile, efforts toward realistic large‐eddy simulations (LES) nested within regional models require subdomains with similar grid spacings, where turbulent eddies are partially resolved in the mixed layer. The range of intermediate grid spacings, often known as the “gray zone,” presents challenges for model configuration and analysis, including uncertainties regarding the behavior of common turbulence closures outside of their ideal use cases. In this study, we evaluate three common configurations for subgrid turbulence—‐, Smagorinsky, and an implicit no‐closure method—in the gray zone resolutions for the ocean surface mixed layer. Results indicate that, in the gray zone with partially resolved boundary layer turbulence, ‐ can produce accurate mixed layer profiles with little sensitivity to grid spacing. However, it overly damps turbulent motions, significantly reducing small‐scale variability that could otherwise be captured. The Smagorinsky closure and the implicit method, in contrast, exhibit higher sensitivity to grid spacing, initially performing poorly but converging toward baseline solutions at finer grids. Our findings provide guidance for submesoscale and turbulent‐scale modeling, recommending Smagorinsky or implicit methods for nested domains which prioritize resolved turbulence, such as LES. The ‐ closure is suitable for simulations that aim to achieve accurate mean‐state representations rather than explicitly resolving detailed three‐dimensional turbulence.

Ti doping as an effective strategy for increasing the stability of strontium-copper-iron perovskite-based oxygen carriers.

An innovative approach combining electrochemistry with online quadrupole time-of-flight-mass spectrometry (EC-MS/MS) was employed to study the oxidative products and metabolic pathways of three bioactive compounds found in Phellodendri Chinensis Cortex, including phellodendrine, obaculactone, and obacunone. This advanced analytical technique provided detailed insights into their transformation processes. The simulation of phase I metabolism reactions was conducted within an electrochemical microreactor system. The heart of this apparatus was a reliably performing boron-doped diamond electrode. Meanwhile, a defined concentration of glutathione (GSH) was injected into the reaction system to obtain phase II metabolites. The metabolites were collected and concentrated for offline electrochemistry-ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry analysis (EC-UHPLC-MS/MS). The main converted products and metabolic pathways were further verified through in vitro liver microsome incubation experiments. It was revealed that the simulated metabolic process based on the electrochemical system effectively produced a variety of metabolites from the compounds, which were subsequently compared with those obtained from rat liver microsomal incubations. Without matrix interference, the drug metabolic process could be effectively simulated and analyzed using the electrochemical system. The findings underscore the utility of electrochemistry as a robust tool for preliminary investigations into the metabolism of natural products, offering a reliable and efficient alternative to traditional methods.

Variation and interaction of mycotoxin levels and nutrients during simulated deodorization process of maize germ oil

Twelve fat-soluble constituents from caramel pigment and their in vitro cytotoxic effects on Caco-2 cells and simulated processes of digestion and absorption

Microalgae are promising candidates for renewable biofuel production and nutrient-rich animal feed. Cultivating microalgae using wastewater can lower production costs but often results in biomass contamination and increases downstream processing requirements. This study presents a novel system that integrates an algae cultivator (AC) with a single-chamber microbial fuel cell (MFC) equipped with photosynthetic and air-cathode functionalities, separated by a ceramic membrane. The system enables the generation of electricity and production of clean microalgae biomass concurrently, in both light and dark conditions, utilizing wastewater as a nutrient source and renewable energy. The MFC chamber was filled with simulated potato processing wastewater, while the AC chamber contained microalgae Chlorella vulgaris in a growth medium. The ceramic membrane allowed nutrient diffusion while preventing direct contact between algae and wastewater. This design effectively supported algal growth and produced uncontaminated, harvestable biomass. At the same time, larger particulates and undesirable substances were retained in the MFC. The system can be operated with synergy between the MFC and AC systems, reducing operational and pretreatment costs. Overall, this hybrid design highlights a sustainable pathway for integrating electricity generation, nutrient recovery, and algae-based biofuel feedstock production, with improved economic feasibility due to high-quality biomass cultivation and the ability to operate continuously under variable lighting conditions.

Abstract To address the difficulty of fault detection in nonlinear, dynamic, and multi‐stage processes, a spatiotemporal neighbour centre distance (SNCD) statistic is proposed. SNCD is combined with t‐distributed stochastic neighbour embedding (t‐SNE) and back propagation neural network (BPNN) to develop the t‐SNE‐BPNN‐SNCD (tB‐SNCD) fault detection method. The t‐SNE‐BPNN leverages BPNN to learn the nonlinear implicit mapping relationships during the t‐SNE feature extraction and dimensionality reduction process, solving the problem of embedding new samples in t‐SNE. SNCD utilizes not only the spatial neighbour information of samples but also their temporal neighbour information, providing a more comprehensive extraction of process features, eliminating the autocorrelation of process data, and overcoming the difficulties posed by the dynamics of the process for fault detection. Since SNCD makes decisions based on the neighbourhood of samples, it is applicable to nonlinear, multi‐stage processes. The performance of tB‐SNCD is tested through numerical simulation processes and the Tennessee Eastman process, showing a higher fault detection rate compared to KPCA, DPCA, DKPCA, KNN, PC‐WKNN, and LOF methods. Particularly, when faults are time‐related, the fault detection rate of tB‐SNCD is significantly higher than that of classical methods.

Earthquakes can cause severe damage to infrastructure systems, particularly transportation networks. Bridges are the most critical yet vulnerable of all the structural elements of transportation networks. In seismic-prone regions, the repair sequence of the damaged bridges is crucial as different repair sequences lead to different economic and social consequences. Therefore, bridge criticality analyses are often carried out to determine the level of importance of various bridges in a transportation network. This paper presents a novel criticality analysis framework for transportation networks under seismic hazards in which the failure risk of bridges from the perspective of the user cost of the commuters in a community is determined and ranked using the agent-based modeling approach. The dynamic post-earthquake traffic demand of commuters is considered herein. An agent-based model is created to simulate the post-earthquake community recovery process to determine the time-variant traffic demand of the commuters. In the next step, agent-based modeling is adopted for the traffic simulation processes. The failure risk associated with bridge failure events is estimated based on the travel statistics of the commuters in terms of total travel time and distance. The proposed framework was applied to a virtual testbed, named Centerville, to showcase its applicability.

The aim of this study is to analyze the representation of geological, mining, processing, and environmental processes in platform Minecraft. Based on a methodological comparison of in-platform mechanics with technological and geoscientific procedures, the article assesses the degree of accuracy, simplification, and didactic applicability of individual processes related to the extraction and use of mineral resources. The analysis is structured into seven main thematic areas covering the entire resource value chain—from geological exploration through mining, ore beneficiation and processing, to quantitative indicators (e.g., waste-to-ore ratio), fluid resources, and environmental impacts. Special attention is given to the potential of modifications that significantly enhance the complexity and accuracy of simulated processes. The results show that Minecraft, enriched with thematic mods, can serve as an accessible and flexible tool for the popularization and education of industrial and geoscientific processes, while engaging a wide range of audiences.

Cellulose-based aerogel fibers with enhanced mechanical properties for thermal insulation and humidity response.

Long-standing methods in materials simulation can now generally predict crystalline structure for near-/stable materials with high accuracy, and independently of local materials chemistry. However, these methods, particularly density functional theory, are electronic structure-based and scale unfavorably with material complexity. This has hindered study of, e.g., configurationally disordered and glassy materials. More, electronic structure calculations are typically run at zero temperature. To bridge the gap between non-universal atomistic simulation and electronic structure modelling, universal machine learning forcefields (MLFFs) have recently attained a level of accuracy suitable for rapid prediction of crystal structure with near electronic structure accuracy. In my talk, I'll discuss the materials databases such as the Materials Project [1] which are used to train these potentials. I will also discuss how the Materials Project's thermodynamic information, including competition between polymorphs, is crucial for determining ground-state crystal structure. I'll highlight recent advances in training universal MLFFs with purpose-built datasets like MatPES [2] which aim to make these models more efficient while retaining their level of predictive accuracy. I'll showcase a range of applications of MLFFs, including benchmarks for ground-state structure prediction, determining positions of hydrogen atoms, representing disordered crystals from ICSD, and extensions to non-crystalline / glassy-like materials through a simulated melt-quench process.

With growing health awareness, low-saturated fat margarine is increasingly favored by consumers. Compared with the traditional modification process, enzymatic interesterification is an ideal means of modifying fats and oils due to its advantages of high selectivity, mild reaction conditions, and no production of trans fatty acids. In this study, five base oils were prepared by enzymatic interesterification of cottonseed oil (CSO) and palm stearin (PS). The enzymatic interesterification blended-based margarine (IB-M) was obtained by mixing base oil, emulsifier, and water. By observing the morphology and comparing the solid fat content, fatty acid and triglyceride compositions, 7:3 (CSO:PS) was finally selected for the preparation of IB-M, which had 11.56 ± 0.56% solid fat content at 20°C, 36.71 ± 0.02% fatty acid, and 1.15 ± 0.04% S3-type triglyceride. The analysis showed that the β' of IB-M was 59.49 ± 1.1%, which was significantly higher than that of the commercially available margarine (CM) (50.39 ± 0.9% and 50.04 ± 1.4%, p < 0.05). The analysis of the melting and crystallization properties indicates broadened peaks, reduced peak areas, and the presence of finer crystalline particles, suggesting improved melting behavior and enhanced plasticity. The rheological properties also indicated that the viscoelasticity of IB-M was less, and a smoother mouthfeel could be experienced in the mouth. Analysis of the in vitro simulated digestion process showed that IB-M showed a similar trend of change as CM, but had a higher content of released FFA (64.99 ± 0.9%), which made it easier for the human body to digest and absorb. Therefore, IB-M enhances CSO's value and supports nutritious functional products.