Model Simplification Research Articles

Reproducible Benchmark for the SNAP 8 Experimental Reactor at Dry Conditions

This work provides fully reproducible benchmark models of the Systems for Nuclear Auxiliary Power (SNAP) 8 Experimental Reactor. Validation benchmarks of the criticality configuration experiments under dry conditions with no coolant are presented. In addition, relevant reactivity worth experiments, and measurement of power distributions are validated and presented here. Discrepancies between modeled and experimental results are at most 110 pcm for fuel and poison worths, and critical configurations that do not manipulate control elements are within 50 pcm. Larger discrepancies found in reflector element worth experiments are due to experimental methodology which was noted as being simplified due to limited calculational capabilities at the time, while discrepancy in the control element manipulated critical configuration is due the absence of structural components for modeling simplification. Power distributions closely follow what was observed in experiment with expected peaking in reflecting elements. All models are well documented with cited references describing and justifying assumptions, simplifications, and adjustments to reproduce the models with any nuclear safety codes; model inputs and outputs are stored in the snapReactors GitHub repository.

Analysis of deformations of road embankments founded on displacement columns improving soft subsoil

Drilled displacement columns, constructed in the form of unreinforced or reinforced concrete elements, are currently a very commonly used method of improving soft subsoil, creating an alternative to more expensive pile foundations. A frequently used solution for improving soft soils of road or railway embankments is to design a regular pattern of columns of relatively small diameter. Columns along the perimeter of the improved area are reinforced with rigid steel profiles, while the internal ones are made as concrete elements. Column heads are usually covered with a load transfer platform (layer of compacted granular fill) which is additionally reinforced with geosynthetics. The application of soil improvement with displacement columns is not always successful. It is due to the errors and shortcomings occurring at the design stage, including simplifications in modelling, to construction faults, which may include insufficient experience of contractors and/or improper supervision. Referring to the real object that failed, the article provides the results of numerical parametric analyses taking into account the influence of the key design parameters such as: the stiffness of the load transfer layers, the amount and stiffness of the geosynthetic reinforcement as well as the column spacing. The article presents comparisons of numerical results obtained with the finite element analyses for various approaches to geometry modelling (axisymmetric, 2D and 3D). The simulations indicate that the use of the axisymmetric model of a single column in routine design may lead to the deformations exceeding the serviceability limit states.

Fast urban inundation simulation with RIM2D for flood risk assessment and forecasting

IntroductionUrban pluvial flooding is a growing concern worldwide as consequence of rising urban population and climate change induced increases in heavy rainfall. Easy-to-implement and fast simulation tools are needed to cope with this challenge.MethodsThis study describes the development of the parsimonious, GPU-accelerated hydraulic model RIM2D for urban pluvial flood simulations. This is achieved by considering the built-up urban area as flow obstacles, and by introducing capacity-based approaches to consider urban drainage by infiltration on pervious surfaces and sewer drainage from roofs and sealed surfaces. The model performance was analyzed by simulating 8 heavy rainfall events in a test area in the city of Dresden, Germany. For these events detailed discharge measurements of sewer discharge are available, providing a unique dataset for evaluating the sewer drainage simulation, which is of high importance for realistic pluvial inundation simulations in urban areas.Results and discussionWe show that the model simulates the temporal dynamics of the sewer discharge and the sewer volume within acceptable ranges. Moreover, the erratic variation of the simulated to measured sewer discharge suggests that the deviations from the measurements are caused by the precipitation input rather than the model simplifications. We conclude that RIM2D is a valid tool for urban inundation simulation. Its short simulation runtimes allow probabilistic flood risk assessments and operational flood forecasts.

Mathematical model explains differences in Omicron and Delta SARS-CoV-2 dynamics in Caco-2 and Calu-3 cells.

Within-host infection dynamics of Omicron dramatically differs from previous variants of SARS-CoV-2. However, little is still known about which parameters of virus-cell interplay contribute to the observed attenuated replication and pathogenicity of Omicron. Mathematical models, often expressed as systems of differential equations, are frequently employed to study the infection dynamics of various viruses. Adopting such models for results of in vitro experiments can be beneficial in a number of aspects, such as model simplification (e.g., the absence of adaptive immune response and innate immunity cells), better measurement accuracy, and the possibility to measure additional data types in comparison with in vivo case. In this study, we consider a refinement of our previously developed and validated model based on a system of integro-differential equations. We fit the model to the experimental data of Omicron and Delta infections in Caco-2 (human intestinal epithelium model) and Calu-3 (lung epithelium model) cell lines. The data include known information on initial conditions, infectious virus titers, and intracellular viral RNA measurements at several time points post-infection. The model accurately explains the experimental data for both variants in both cell lines using only three variant- and cell-line-specific parameters. Namely, the cell entry rate is significantly lower for Omicron, and Omicron triggers a stronger cytokine production rate (i.e., innate immune response) in infected cells, ultimately making uninfected cells resistant to the virus. Notably, differences in only a single parameter (e.g., cell entry rate) are insufficient to obtain a reliable model fit for the experimental data.

Approaches to simplify industrial energy models for operational optimisation

Minimising energy consumption in today's industrial sector is a crucial objective for achieving established climate goals. One effective strategy to enhance efficiency is optimising energy system operations within industries. The initial step in establishing operational optimisation involves developing a comprehensive model of the energy system. This model necessitates a specific structure to meet optimisation requirements. However, creating a model from scratch incurs substantial effort. While numerous companies possess energy models, they often lack the requisite structure for optimisation. Consequently, simplifying existing models can significantly reduce the effort needed to implement operational optimisation.This paper investigates the simplification of intricate industrial energy system models for optimisation purposes. The subsequent sections analyse two distinct approaches. One approach involves linearisation, while the other utilises neural networks. To facilitate a comparative analysis of these approaches, a reference model is developed. The assessment of these methodologies includes an investigation into optimisation robustness, computation time, accuracy concerning the reference model, and the effort required for developing and maintaining the simplified models. It proved that both approaches are suitable for operational optimisation. Linearisation exhibits superior computational efficiency compared to the neural network approach. The linearisation modelling approach together with the optimisation only required a few milliseconds for the calculation. The neural network approach needed 3 h for the calculation of the optimum with the genetic algorithm. The simulation of the neural network itself only required a few milliseconds. Hence, an improvement of the genetic algorithm is needed. However, the accuracy of linearisation falls short of that achieved by neural networks. The linearisation achieves a mean average percentage error from only 13%. In comparison the neural network's mean average error is 2.3%. Therefore, the linearisation must be improved. The impact using a piecewise linearisation on the results will be analysed in further research.

MA-Net: Rethinking Neural Unit in the Light of Astrocytes

The artificial neuron (N-N) model-based networks have accomplished extraordinary success for various vision tasks. However, as a simplification of the mammal neuron model, their structure is locked during training, resulting in overfitting and over-parameters. The astrocyte, newly explored by biologists, can adaptively modulate neuronal communication by inserting itself between neurons. The communication, between the astrocyte and neuron, is bidirectionally and shows the potential to alleviate issues raised by unidirectional communication in the N-N model. In this paper, we first elaborate on the artificial Multi-Astrocyte-Neuron (MA-N) model, which enriches the functionality of the artificial neuron model. Our MA-N model is formulated at both astrocyte- and neuron-level that mimics the bidirectional communication with temporal and joint mechanisms. Then, we construct the MA-Net network with the MA-N model, whose neural connections can be continuously and adaptively modulated during training. Experiments show that our MA-Net advances new state-of-the-art on multiple tasks while significantly reducing its parameters by connection optimization.

Learning Hybrid Dynamics Models with Simulator-Informed Latent States

Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this problem is to train recurrent models. However, predictions with these models are often not physically meaningful. Further, they suffer from deteriorated behavior over time due to accumulating errors. Often, simulators building on first principles are available being physically meaningful by design. However, modeling simplifications typically cause inaccuracies in these models. Consequently, hybrid modeling is an emerging trend that aims to combine the best of both worlds. In this paper, we propose a new approach to hybrid modeling, where we inform the latent states of a learned model via a black-box simulator. This allows to control the predictions via the simulator preventing them from accumulating errors. This is especially challenging since, in contrast to previous approaches, access to the simulator's latent states is not available. We tackle the task by leveraging observers, a well-known concept from control theory, inferring unknown latent states from observations and dynamics over time. In our learning-based setting, we jointly learn the dynamics and an observer that infers the latent states via the simulator. Thus, the simulator constantly corrects the latent states, compensating for modeling mismatch caused by learning. To maintain flexibility, we train an RNN-based residuum for the latent states that cannot be informed by the simulator.

Abstract 7378: Using game theory to investigate the therapeutic potential of disrupting tumor-microenvironment interactions

Abstract Cancer is a complex disease involving the interplay of several types of malignant and nonmalignant cells. The growth of tumors and surrounding stromal cells can be modeled as an ecosystem, where populations of cancerous and healthy cells interact and compete for resources. In this work, we have explored the application of game theoretic models of cancer to the design of novel anticancer therapies. Notably, a game theoretic-model of tumor-stromal interactions can capture the impact of interactions between cell types and can be used to investigate frequency-dependent parameters describing the impact of drug action during the initiation and growth of metastatic cancers. Previous studies using game theoretic models to describe cancer have focused on explaining progression patterns of specific tumors with known tumor-stromal interactions. This study takes a more general approach to cancer growth dynamics, modeling cancer as a simple game between healthy and cancer cells. This simplified model is used to make general predictions about cancer progression and the efficacy of treatment strategies. Our work suggests that disruption of beneficial interactions between healthy and cancer cells may be a viable treatment strategy for small tumors and preventing metastasis by ensuring that the healthy cell strategy is evolutionarily stable. The efficacy of treatment strategies that disrupt cancer cell cooperation or tumor-stromal dynamics changes as the cancer cell frequency changes. Our work suggests that drugs that target tumor-stromal cell interactions would need to be combined with other types of treatments to be effective against large tumors. Although based on a simplistic model of cancer initiation and progression, the framework derived here can be extended to the real-world design of experimental anticancer therapeutics, and fundamental analyses of this nature can be of practical utility in setting strategy for Target Product Profile design. Citation Format: Debra Van Egeren, Andrew Chen, Madison Stoddard, Lin Yuan, Arijit Chakravarty. Using game theory to investigate the therapeutic potential of disrupting tumor-microenvironment interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7378.

Evaporation of binary liquids from a capillary tube

Evaporation of multi-component liquid mixtures in confined geometries, such as capillaries, is crucial in applications such as microfluidics, two-phase cooling devices and inkjet printing. Predicting the behaviour of such systems becomes challenging because evaporation triggers complex spatio-temporal changes in the composition of the mixture. These changes in composition, in turn, affect evaporation. In the present work, we study the evaporation of aqueous glycerol solutions contained as a liquid column in a capillary tube. Experiments and direct numerical simulations show three evaporation regimes characterised by different temporal evolutions of the normalised mass transfer rate (or Sherwood number $Sh$ ), namely $Sh (\tilde{t} ) = 1$ , $Sh \sim 1/\sqrt {\tilde{t} }$ and $Sh \sim \exp (-\tilde{t} )$ , where $\tilde {t}$ is a normalised time. We present a simplistic analytical model that shows that the evaporation dynamics can be expressed by the classical relation $Sh = \exp ( \tilde{t} )\,\mathrm {erfc} ( \sqrt {\tilde{t} })$ . For small and medium $\tilde{t}$ , this expression results in the first and second of the three observed scaling regimes, respectively. This analytical model is formulated in the limit of pure diffusion and when the penetration depth $\delta (t)$ of the diffusion front is much smaller than the length $L(t)$ of the liquid column. When $\delta \approx L$ , finite-length effects lead to $Sh \sim \exp (-\tilde{t} )$ , i.e. the third regime. Finally, we extend our analytical model to incorporate the effect of advection and determine the conditions under which this effect is important. Our results provide fundamental insights into the physics of selective evaporation from a multi-component liquid column.

On the electrokinetic remediation of Pb-contaminated soil: A coupled electro-transport-reaction modelling study based on chemical reaction kinetics

The accumulation of lead (Pb) in soil resulted from industrialization and urbanization poses a threat to human health and the ecosystem. This study proposes a mathematical model for Pb migration and transformation in soil porous media, aiming to guide the design of electrokinetic remediation schemes for Pb-contaminated soils. To improve the validity of the model, the chemical reactions considered in the model are all based on chemical reaction kinetics, which were usually overlooked for model simplification. The model quantitatively describes various physical and chemical processes of Pb at the soil-pore fluid interface and in the pore fluid, including diffusion, electromigration, electroosmosis, electrolytic water reaction, precipitation, adsorption/desorption, protonation/deprotonation reaction, and water self-ionization reaction. The numerical results show that the pH value is a key factor affecting the distribution of Pb in the soil and determining the removal efficiency of Pb. The effects of different enhancement methods on Pb concentration distribution and removal efficiency were evaluated with this model. It was found that placing a cation exchange membrane at the cathode boundary while using 0.01 M nitric acid as anode electrolyte can effectively improve Pb removal efficiency from 3.9% to 93.6%. The developed model can be used to guide the design of the enhanced electrokinetic remediation schemes.

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R2C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSECV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R2P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSEP) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R2C increased by 0.15%, 0.41%, and 0.34%, RMSECV decreased by 0.53%, 0.32%, and 0.24%, R2P increased by 0.21%, 0.63%, and 0.35%, RMSEP decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.

Hydrological model skills change with drought severity; insights from multi-variable evaluation

Hydrological models often do not properly simulate streamflow (Q) during extreme events, including droughts. Limited abilities in simulating Q during droughts may arise from a misrepresentation of Q generating processes during these periods, but little research has focused on distributed, process-based models over human-affected areas and extreme events. To shed more light into model consistency during these periods, we evaluated the ability of the hydrological model Continuum in simulating Q over the human-affected Po river basin in Italy during droughts of different severity over the last 13 years, including the severe 2022 event. To investigate the causes for potential model deterioration during severe droughts, we assessed the simulation of evapotranspiration (ET) and Terrestrial Water Storage (TWS) against independent remote sensing-based benchmarks, and possible inconsistencies in forcing and benchmark data. Finally, we included a moderate drought in the calibration period, as potential strategy to improve model performances during severe droughts. The model represented well Q (KGE = 0.81 for the outlet of the basin), ET (r = 0.94) and TWS (r = 0.76) over the whole study period. Focusing on Q and specific sub-periods, model performances were comparable during wet years (2014 and 2020) and moderate droughts (2012 and 2017), with KGE across the 38 study sub-catchments of 0.59±0.32 (mean ±standard deviation) during wet years and 0.55±0.25 during moderate droughts. The model simulated Q well for the outlet section of the basin also during the severe 2022 drought (KGE = 0.82). However, performances across the sub-catchments declined in 2022 (KGE = 0.18±0.69). For the severe drought, we detected a decrease in model performances for ET, in particular over human-affected croplands (mean decrease in r by 105% and mean increase in nRMSE by 86%). Furthermore, calibrating during a moderate drought did not improve model performances in 2022 (KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on them. Our study highlighted decreased model skills specifically during a severe drought and identified the neglection of irrigation as the most plausible cause for this. Given projected increases in severe droughts and the frequent modelling simplification of human activities, despite their heavy interference in many regions, our findings are highly relevant to move towards more robust hydrological modelling in a changing climate and the Anthropogenic era, to support management and adaptation strategies.

Genetic parameter estimates for methane emission from breath during lactation and potential inaccuracies in reliabilities assuming a repeatability versus random regression model

Methane emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and establishing optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm) recorded in the feed bins of milking robots on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with first- to fifth-order Legendre polynomials for the additive genetic and within-lactation permanent environmental effect. The mean heritability (± SE) was 0.17 ± 0.04, and the mean within-lactation repeatability was 0.56 ± 0.03. The genetic correlations between DIM were high and ranged from 0.34 ± 0.36 to 1.00 ± <0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± <0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus the RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long of a recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and in lower genetic gains than predicted from the reliability.

An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR)

Background and objectiveIn everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patient-oriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption of Clinical Decision Support Systems (CDSSs) based on Artificial Intelligence (AI) algorithms. However, the black-box nature of AI algorithms has hampered a large adoption of AI-based CDSSs in clinical practice. In this study, an innovative AI-based method to compress AI-based prediction models into explainable, model-agnostic, and reduced decision support systems (NEAR) with application to healthcare is presented and validated. MethodsNEAR is based on the Shapley Additive Explanations framework and can be applied to complex input models to obtain the contributions of each input feature to the output. Technically, the simplified NEAR models approximate contributions from input features using a custom library and merge them to determine the final output. Finally, NEAR estimates the confidence error associated with the single input feature contributing to the final score, making the result more interpretable. Here, NEAR is evaluated on a clinical real-world use case, the mortality prediction in patients who experienced Acute Coronary Syndrome (ACS), applying three different Machine Learning/Deep Learning models as implementation examples. ResultsNEAR, when applied to the ACS use case, exhibits performances like the ones of the AI-based model from which it is derived, as in the case of the Adaptive Boosting classifier, whose Area Under the Curve is not statistically different from the NEAR one, even the model's simplification. Moreover, NEAR comes with intrinsic explainability and modularity, as it can be tested on the developed web application platform (https://neardashboard.pythonanywhere.com/). ConclusionsAn explainable and reliable CDSS tailored to single-patient analysis has been developed. The proposed AI-based system has the potential to be used alongside the clinical guidelines currently employed in the medical setting making them more personalized and dynamic and assisting doctors in taking their everyday clinical decisions.

The Effect of Nanoparticles on Drug Distribution in The Mathematical Model of Blood Flow

This research examines the influence of nanoparticles in the distribution of drugs in healthy blood flow on linear, angular velocity and blood temperature. Construction and simplification of a blood flow model based on boundary layer equations, dimensionless variables, stream functions, and similarity variables. The initial step is to establish a dimensional blood flow model. Using dimensionless variables, the equation is simplified into a dimensionless equation. A similarity equation is generated by converting the non-dimensional equation. The nanoparticles used are . At the linear velocity and temperature of blood flow is in the highest position. At the angular velocity of blood flow, the position of blood flow with nanoparticles is in the uppermost position. This research is used to estimate the velocity and temperature of blood flow with the influence of nanoparticles as drug distribution.

Research on precise and standardized numerical simulation strategy for vehicle aerodynamics

PurposeThe purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.Design/methodology/approachError sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.FindingsErrors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.Originality/valueBuilding upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.

Tool‐tip vibration prediction based on a novel convolutional enhanced transformer

AbstractSuperior surface finish remains a fundamental criterion in precision machining operations, and tool‐tip vibration is an important factor that significantly influences the quality of the machined surface. Physics‐based models heavily rely on assumptions for model simplification when applied to complex high‐end systems. However, these assumptions may come at the cost of compromising the model's accuracy. In contrast, data‐driven techniques have emerged as an attractive alternative for tasks such as prediction and complex system analysis. To exploit the advantages of data‐driven models, this study introduces a novel convolutional enhanced transformer model for tool‐tip vibration prediction, referred to as CeT‐TV. The effectiveness of this model is demonstrated through its successful application in ultra‐precision fly‐cutting (UPFC) operations. Two distinct variants of the model, namely, guided and nonguided CeT‐TV, were developed and rigorously tested on a data set custom‐tailored for UPFC applications. The results reveal that the guided CeT‐TV model exhibits outstanding performance, characterized by the lowest mean absolute error and root mean square error values. Additionally, the model demonstrates excellent agreement between the predicted values and the actual measurements, thus underlining its efficiency and potential for predicting the tool‐tip vibration in the context of UPFC.

A VLBA-uGMRT search for candidate binary black holes: study of six X-shaped radio galaxies with double-peaked emission lines

ABSTRACT Identifying methods to discover dual active galactic nucleus (AGN) has proven to be challenging. Several indirect tracers have been explored in the literature, including X/S-shaped radio morphologies and double-peaked (DP) emission lines in the optical spectra. However, the detection rates of confirmed dual AGN candidates from the individual methods remain extremely small. We search for binary black holes (BBH) in a sample of six sources that exhibit both X-shaped radio morphology and DP emission lines using the Very Long Baseline Array (VLBA). Three out of the six sources show dual VLBA compact components, making them strong candidates for BBH sources. In addition, we present deep uGMRT images revealing the exquisite details of the X-shaped wings in three sources. We present a detailed precession modeling analysis of these sources. The black hole separations estimated from the simplistic geodetic precession model are incompatible with those estimated from emission line offsets and the VLBA separations. However, precession induced by a non-coplanar secondary black hole is a feasible mechanism for explaining the observed X-shaped radio morphologies and the black hole separations estimated from other methods. The black hole separations estimated from the double-peaked emission lines agree well with the VLBA compact component separations. Future multifrequency VLBA observations will be critical in ruling out or confirming the BBH scenario in the three galaxies with dual component detections.

A mathematical model to start a discussion on cybersex addiction

Cybersex is the use of the Internet and its services to achieve sexual arousal and pleasure. This use, however, can become excessive and uncontrolled. Common consequences of a problematic engagement in online sexual activities are the deterioration of real-life romantic relationships and the occurrence of sexual dysfunctions, even in young population. Here, a simplistic mathematical model based on differential equations is proposed to investigate the impact of cybersex on the human population growth. This model is analysed from a dynamical system theory perspective. This study can be presented in a class on differential equations and it can be the starting point for talking about Internet sex addiction.