Correct Model Research Articles

Determination of Wind Force Acting on an Object in the Shape of a Bent Pipe

The aim of the paper is to determine the aerodynamic forces acting on a torus-shaped structure fragment at high wind velocity which are impossible to obtain from the existing standard EN 1991-1-4 (the so-called wind standard). The most important problem is the correct modeling of turbulence and laminar-turbulent transition in the conditions of flow interference resulting from the presence of other obstacles. For this reason, forces are obtained by two methods: Fluid-Structure Interaction (FSI, force transfer) and User-Defined Functions (UDF). Variations of the total aerodynamic lift force of the half of the torus with angle β and velocity of wind w, and the formula for estimating the horizontal force Pz perpendicular to drag force are presented. Additionally, useful engineering parameters (such as pressure distribution and air velocity field) are determined. The forces of wind influence on two cylinders and a torus-shaped object are obtained and compared

Temporal and Atemporal Asymmetries in Causation

This article proposes a new account of causal asymmetry and of how it relates to temporal asymmetry. The key concept on which the account is based is that of inclusion, i.e. of an object being “in” another. Thus, part I develops the notion of what is “possible with respect to” a given object, and what is not, based on what is included in it. This leads to a counterfactual dependence asymmetry which is independent of the direction of time. Part II provides a crash course in local time, describing how to derive temporal precedence (“before”) and time’s characteristic asymmetry in terms only of states of physical objects called “recorders”, a derivation which yields a relativistically correct model of time’s arrow. Putting the counterfactual and temporal asymmetries together allows, in part III, to propose an explanation of why causes are observed to precede their effects in time. The upshot is that, even though many of our intuitions about causal asymmetry are profoundly connected to our temporal perspective, there remains a robust counterfactual asymmetry whereby a change in an object requires something distinct from it.

On the use of feed-forward neural networks in the context of surrogate aeroelastic simulations

AbstractFor a few decades now, the proliferation of digital computers has driven the development of increasingly complex models to study the physical phenomena that are part of our reality. Particularly, in the field of aeronautics and renewable energy (wind), correct aeroelastic modeling is crucial for many reasons: structural and aerodynamic optimization, determining operational envelopes, and avoiding destructive aeroelastic phenomena such as divergence or flutter, among others. Furthermore, the study of systems involving multiple fields of physics (aerodynamics, structural dynamics, control, etc.) is characterized by exhibiting highly nonlinear phenomena (limit cycle oscillations, bifurcations, chaos, etc.), which are very challenging to capture with linear approximations or simplified models. In this work, we present a comprehensive statistical analysis of the performance of shallow feed-forward neural networks (FNNs) to capture supercritical Hopf bifurcations when dealing with aeroelastic flutter. The FNNs are trained by considering data sets generated by using two different aeroelastic models of increasing complexity. For the structural model, we consider a two-degree-of-freedom model consisting of an airfoil oscillating in pitch and plunge. The aerodynamic forces are accounted for by using two different flow solvers: (1) a non-compressible two-dimensional linear (but ergodic) model based on Wagner’s theory (referred as Fung’s model), which results in analytical expressions for the lift and aerodynamic moment, and (2) a two-dimensional version of the well-known unsteady vortex-lattice method (UVLM). The assessment of the resulting FNN-based models is carried out through a Monte Carlo experiment over R replicates. As a measure of performance, we use the mean-squared error test associated with the estimators, here the system’s response and its consistent aerodynamic coefficients. We also discuss, in detail, the behavior of FNN-based surrogate aeroelastic frameworks when they are trained with data coming from Fung-based or UVLM-based aeroelastic simulations. Furthermore, we highlight a number of challenges faced by shallow FNNs, as well as some difficulties when integrated into surrogate aeroelastic environments. Finally, we provide explanations to questions raised throughout the article and conjecture some others without a definitive answer.

Borrowed legitimacy and beseeched resources: How competing professionals negotiate identities and forge symbiosis within Chinese community corrections

The recent formalization of Chinese community corrections (CCC), with its explicit emphasis on the professional provision of rehabilitative services to offenders, signals Chinese policymakers striving to create a penal modernist project. It also instigates antagonism between justice and service-oriented professionals, who seek to assert and carve out respective jurisdictions over rehabilitative work. There is a dearth of research touching on the ways in which potential tensions, or contractionary professional identities, are managed in practice. Drawing on a set of ethnographic and interview data gleaned from a large Chinese city, which pioneered and implemented the professional model of community corrections, this study examines the negotiation of professional power and interprofessional dynamics in rehabilitative work. The analysis suggests that during professional contestation, CCC actors deploy strategies to maintain identity boundaries, while making compromises and ceding jurisdictional authority to the opposing group. The negotiated order is accomplished in this hybridized institutional field, primarily through variegated demands of professionals involved for capacity enhancement, legitimacy reinforcement, or pragmatic survival. A symbiotic, but continually fractious, configuration is thus forged, blurring professional boundaries and breeding reciprocal benefits that sustain collaboration. Implications for ameliorating CCC's rehabilitative work are discussed.

AUTOMATED MULTI-MODEL PREDICTION AND EVALUATION FOR CONNECTING RAINFALL PREDICTION INFORMATION AND SINGLE-YEAR OPERATIONAL PLAN OF LAHOR-SUTAMI DAM

There is a gap between existing climate information and the needs of annual dam operational planning. This study aims to demonstrate that the percentile approach currently used for planning is not optimal, especially now that automation has become more accessible. The purpose of this study is to design an automated forecasting and evaluation system based on 36 10-days rainfall projections using a multi-model approach. This approach comprises a percentile, ARIMA, ECMWF+ARIMA, IOD DMI regression, ERSST regression, and ensemble methods models. Additionally, this study aims to demonstrate how a verified, multi-model-based rainfall forecast can provide more reliable assurance for the annual operational planning of Lahor-SutamiDam, simulated operationally in November 2022 for the 2022/2023 planning cycle. Data utilized include historical 10-days rainfall data from 1991 to 2023, ECMWF raw and corrected model outputs, Nino-Dipole index, and global sea surface temperature. The verification method employs four criteria based on MAE and fit index. An operational simulation approach is used for training-testing period segmentation, while a 10-year window is applied to account for possible climate-change-induced shifts in relationships. Single linear regression is used to avoid overfitting. The automation system was developed using R-Statistics. Results indicate that the current approach is only optimal for 58% of locations. Superior methods identified include ECMWFcorrected, ERSST regression, and Ensemble models. A case study for 2022/2023 demonstrates that the forecast results outperform the existing plan for at least 78% of the projected periods.

Reporting Uncertainty Around Health State Values: A Standard Method and Worked Example.

Papers reporting value sets typically only report the standard errors (SEs) around each estimated coefficient in value set models. This is important information but does not help those building cost effectiveness models, who need to know the uncertainty around the values of health states in order to conduct sensitivity analyses. This paper's aim is to demonstrate how SEs around HRQoL values can be calculated, using the example of the UK EQ-5D-3L value set. We show how information from a model's variance/covariance matrix can be used to estimate SEs for every health state value, whether it is part of the modelling data set or not. Data from the Measurement and Valuation of Health study were used to replicate the original UK value set and the variance/covariance matrix, and to produce standard errors around the values for all 243 EQ-5D-3L states. The range of the SEs is small compared to the range of the health state values, but are conditional on a correct model specification and may be sensitive to alternative specifications. Reporting these SEs should become routine practice in reporting value sets, to ensure users are provided with information of parameter uncertainty. These SEs only capture one specific aspect of the sources of uncertainty around HRQoL values, but represent a first step toward a more complete account of uncertainty in the preference weights used to estimate QALYs.

Modified Parsimonious Model Averaging Under Heteroscedasticity

ABSTRACTWe propose a modified parsimonious model averaging method under heteroscedasticity by incorporating the covariance matrix into the weight choice criterion. We demonstrate that when correctly specified models exist in the candidate model set, the weight assigned to the smallest correct model tends to 1. This leads to the consistency of variable selection and the asymptotic normality of the modified parsimonious model averaging estimator. Furthermore, we prove its asymptotic optimality within the given weight space when all candidate models are misspecified. Numerical simulations indicate that, in the presence of heteroscedasticity, the modified parsimonious model averaging estimator outperforms the unmodified version in terms of prediction accuracy. As an illustration example, we apply this method to predict housing rental prices in Fengtai District, Beijing.

Failure in Constructing the Mathematical Model in Real-World Problems

Constructing the correct mathematical model is the main key to solving mathematical modeling problems, but students most often fail at this process. Understanding the failure characteristics of constructing a model can be used to correct student errors. This study aims to discuss the characteristics of failures in constructing mathematical models carried out by the students in algebra subject matter. This study used an exploratory descriptive design approach and involves 41 mathematics education students to solve mathematical modeling test questions. The results showed four types of failure characteristics in constructing the model. First, misunderstand type is characterized by disability in capturing important facts from the problem and compiling a model only procedurally. Second, misconnection type is marked by incompetence in making connections between important facts. Third, missspeculation type is noted by the incapacity in making speculations about important facts. Fourth, misslogic type is signed by the inability to use logical facts. Therefore, lecturers or teachers must be more careful to improve the teaching process to be more comprehensive and carry out appropriate scaffolding to overcome the students’ difficulties in solving mathematical modeling problems, especially in constructing mathematical models. The appearance of each error indicates the need for certain repairs. Error correction should be handled based on the characteristics that arise.

A European soil organic carbon monitoring system leveraging Sentinel 2 imagery and the LUCAS soil data base

The Worldsoils project has developed a pre-operational Soil Organic Carbon (SOC) monitoring system in a cloud environment. The system predicts topsoil organic carbon content at regional and continental scales from Earth Observation (EO) satellite data with a continuous cover over Europe. The system utilizes spectral models for croplands and a digital soil mapping approach for permanently vegetated areas such as grasslands and forests. Models strongly rely on soil reflectance composites from the Sentinel 2 multispectral instrument providing the median reflectance for all valid pixels over a period of three years. The bare soil frequency, a proxy for the degree of crop cover, is clearly lower in a Mediterranean pilot region compared to croplands in temperate regions. This is due to the extensive crop cover in the Mediterranean with winter cereals and fodder crops. The graphical user interface provides SOC content and the prediction interval ratio (i.e. 90 % uncertainty interval divided by the median) for 50 m pixels in three pilot regions and 100 m pixels for the rest of Europe. The SOC prediction algorithms are reasonable compared to others at the continental scale (R2: 0.41 for croplands and 0.28 for permanently vegetated areas). Apart from tree crops in Macedonia (Greece) the soil reflectance composite attributes the correct model to validation sets of cropland and grassland in the pilot regions. The SOC prediction is satisfactory in Wallonia (Belgium; R2 0.51) but is less accurate in Greece and the Czech Republic. In particular in Greece, the poor performance is linked to the low bare soil frequency due to the abundance of tree crops, cereals and fodder crops. The monitoring system can reproduce spatial patterns in SOC content similar to the ones obtained from a detailed regional algorithm using the new generation of hyperspectral satellites. However, the very high values in kettle holes in a morainic landscape of Northern Germany are underestimated.

Differences in the Parsing of the Present Tense Verb in Qur’anic Verses in Order to Develop Arabic Language Skill

This research aims to expose the meanings, and the difference in the structure of the present tense in the frequent Qur’anic readings with an explanation of the impact of this difference in the noble verse, and it aims to provide correct linguistic models that contribute to building the linguistic ability of the students. The methodology was followed in this research, and this research included an introduction, a methodology section, result and discussion, and the last is conclusion. As for the introduction, it defined the subject of the research, the reasons for its selection, its objectives, the method used, the basic rule, previous studies, and the study plan. It focuses on linguistic analogy, meaning of harf jarr, linguistic skills, its definition and methods of obtaining. The present tense verb is preceded by harf lām and atf that considering the lām as a reasoning. It is not a condition for the present tense to be in the accusative case that the lam be clear in its reasoning. The lam and atf has function in the present tense verb “to be made” was read with the kasra of the lam and the accusative, and with the sukun of the lam and the jazm in the context of al-fi’l al-mudhari’, and it is clear that the reading of the kasra of the lam and the accusative was directed by considering the lam for the reasoning and the accusative of “to be made” with (that) is implicitly permissible.

Recursive self-feedback improves spontaneous speech in chronic aphasia within real-world settings

ABSTRACT Background Spontaneous speech generation, often impaired in chronic nonfluent aphasia, is essential for natural communication. Treatments aiming to optimize spontaneous speech in real-world settings are critical for improving the quality of life for persons with nonfluent aphasia (PWNA). Traditional treatments frequently rely on models and feedback from external agents, which may not be sustainable in the long-term. Preliminary evidence suggests that PWNA can use self-feedback alone, through recursive self-feedback, to improve their production of scripted sentences. This study developed and examined the impact of spontaneous speech with recursive self-feedback on enabling PWNA to self-improve their spontaneous speech in real-world settings. Method Using a cross-over design, 3 participants received 2 treatments at their homes: spontaneous speech with recursive self-feedback and spontaneous speech with speech model. Recursive self-feedback treatment involved iterative self-monitoring and minimization/correction of errors during spontaneous speech response to narrative prompts. The speech model treatment provided corrective model responses to prompts from an external source which served as reference frames to help improve the participants’ responses. Participants utilized a mobile app to practice the treatments intensively at home for 2 hours each day over a period of 12 to 14 days, spanning 2 to 3 weeks. Direct and generalized treatment effects on microlinguistic measures of spontaneous speech were determined using general linear mixed effects model and standardized mean difference. Results Both treatments led to significant improvements in microlinguistic measures of spontaneous speech, with recursive self-feedback treatment showing broader post-treatment effects on treated prompts and generalization effects. to untreated prompts. Conclusion Recursive self-feedback is a promising procedure for enhancing spontaneous speech in PWNA, providing a simple and flexible self-directed treatment option. This procedure relies primarily on speech-auditory stimulations and self-feedback loops to improve spontaneous speech, suggesting its potential applicability across various spoken language cultures. To validate these findings and assess their broader relevance, further research is recommended.

Bayesian Selection of Relaxed-clock Models: Distinguishing Between Independent and Autocorrelated Rates.

In Bayesian molecular-clock dating of species divergences, rate models are used to construct the prior on the molecular evolutionary rates for branches in the phylogeny, with independent and autocorrelated rate models being commonly used. The two classes of models, however, can result in markedly different divergence time estimates for the same dataset, and thus selecting the best rate model appears important for obtaining reliable in- ferences of divergence times. However, the properties of Bayesian rate model selection are not well understood, in particular when the number of sequence partitions analysed increases and when age calibrations (such as fossil calibrations) are misspecified. Further- more, Bayesian rate model selection is computationally expensive as it requires calculation of marginal likelihoods by MCMC sampling, and therefore methods that can speed up the model selection procedure without compromising its accuracy are desirable. In this study, we use a combination of computer simulations and real data analysis to investigate the sta- tistical behaviour of Bayesian rate model selection and we also explore approximations of the likelihood to improve computational efficiency in large phylogenomic datasets. Our simulations demonstrate that the posterior probability for the correct rate model converges to one as more molecular sequence partitions are analysed and when no calibrations are used, as expected due to asymptotic Bayesian model selection theory. Furthermore, we also show the model selection procedure is robust to slight misspecification of calibrations, and reliable inference of the correct rate model is possible in this case. However, we show that when calibrations are seriously misspecified, calculated model probabilities are com- pletely wrong and may converge to one for the wrong rate model. Finally, we demonstrate that approximating the phylogenetic likelihood under an arcsine branch-length transform can dramatically reduce the computational cost of rate model selection without compro- mising accuracy. We test the approximate procedure on two large phylogenies of primates (372 species) and flowering plants (644 species), replicating results obtained on smaller datasets using exact likelihood. Our findings and methodology can assist users in selecting the optimal rate model for estimating times and rates along the Tree of Life.

Urban drainage efficiency evaluation and flood simulation using integrated SWMM and terrain structural analysis

A method for evaluating urban drainage system efficiency and simulating pluvial flooding is presented, incorporating rainwater inlet limitations through an integrated SWMM and terrain structural analysis (the corrected model). The corrected model is calibrated using data from two flood events and compared to an overflow model (non-corrected, without considering inlet restrictions) under the same conditions to assess its performance. The results show that the relative error of the flood peak in simulations ranges from 0.27 % to 2.80 %, while the Nash efficiency coefficient ranges from 0.3184 to 0.9504, indicating reasonable accuracy and significant comparability to the overflow model. Drainage efficiency evaluation reveals significant variation among drainage units, decreasing with increasing rainfall until surface depressions fill, after which surface drainage improves notably. This highlights the critical role of surface drainage in overall system efficiency and flood dynamics. Furthermore, two types of urban storm-induced floods are identified: infiltration excess and saturation excess floods. Modeling all floods as saturation excess may lead to substantial misestimations, stressing the need for accurate modeling that accounts for drainage unit characteristics. Understanding urban flood mechanisms is essential for improving modeling accuracy and optimizing drainage system design and flood management strategies.

Sensitivity of Bayesian Networks to Errors in Their Structure.

There is a widespread belief in the Bayesian network (BN) community that while the overall accuracy of the results of BN inference is not sensitive to the precision of parameters, it is sensitive to the structure. We report on the results of a study focusing on the parameters in a companion paper, while this paper focuses on the BN graphical structure. We present the results of several experiments in which we test the impact of errors in the BN structure on its accuracy in the context of medical diagnostic models. We study the deterioration in model accuracy under structural changes that systematically modify the original gold standard model, notably the node and edge removal and edge reversal. Our results confirm the popular belief that the BN structure is important, and we show that structural errors may lead to a serious deterioration in the diagnostic accuracy. At the same time, most BN models are forgiving to single errors. In light of these results and the results of the companion paper, we recommend that knowledge engineers focus their efforts on obtaining a correct model structure and worry less about the overall precision of parameters.

Improving Tensor Regression by Optimal Model Averaging

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty about which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, we prove that the model averaging estimator is asymptotically optimal. When correct models are included in the set of candidate models, we prove the consistency of parameters and the convergence of the model averaging weight. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Comparison of Convergence for Different Models and Loss Function: Evidence from Transformer, Diffusion and RNN

Abstract. With the development of science and technology, people are using machine learning as a method to make life easier. However, the performance corresponding to different models will produce different predictions and results for one thing. In order to improve the performance of the model, this paper investigates the influence of the learning rate hyperparameter on the model's performance, and demonstrates it through the convergence of the loss function. After experimental research, it has been found that different models exhibit significant differences in their performance when processing the same dataset. Meanwhile, different learning rates also have a significant impact on the performance of the model. Therefore, after selecting the correct model for machine learning, one should also adjust a relatively good hyperparameter to make the entire process smoother. Based on the analysis, one will gain a basic understanding of the optimal learning rates for transformer, diffusion, and RNN models when training MNIST. It is convenient for people to set better hyperparameters and obtain better prediction and decision-making results when using these three models, so that one can demonstrate better performance when using these three models.

Dataset preparation for CROHME 2019 for training a neural network

Subject of research. This article examines the process of data preparation for training neural network models that address the task of recognizing complex handwritten mathematical expressions and converting them into typesetting systems like LaTeX or MathML. The dataset under study is CROHME 2019, which contains images of handwritten mathematical expressions and their corresponding LaTeX annotations. The objective of this research is to develop and describe a data preparation process that ensures correct model training and minimizes errors in numerical labels and transformations. The study focuses on the following key stages: loading and preprocessing images, parsing LaTeX annotations, linking images with their corresponding annotations, normalizing pixel values, creating and verifying the tokenizer vocabulary, and preparing the tokenizer itself. The methods include data analysis and processing using specialized tools for working with images and text annotations. The main outcome of this work is the creation of a dataset ready for use in training the neural network model. The prepared dataset ensures accurate alignment between images and annotations, as well as correct conversion of mathematical expressions into LaTeX code.

A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation

Accurate prognostication of degradation plays an essential role in effectively enhancing the operational lifespan of proton exchange membrane fuel cells (PEMFCs). This paper proposes a novel enhanced correctional grey Verhulst model (IHS-CRGVM-RE), designed to prognosticate the degradation process of PEMFCs using the voltage of PEMFCs stack as a health indicator. First, the inverse hyperbolic sine function transformation is employed to attain optimal smoothness in data treatment. Then, the background value within the grey Verhulst model framework is modified based on cellular automata with rectangle techniques. Finally, a residual correction mechanism is applied to delineate the influences of error outcomes concerning PEMFCs degradation. Rigorous validation is provided via a comprehensive analysis based on two distinct PEMFCs datasets. The results demonstrate that the proposed model outperforms other data-driven models in prognostication accuracy, highlighting its significant importance for prognosticating the lifespan of PEMFCs.

A Restoring Force Correction Model for Enveloped Steel Jacket-Confined Seismic-Damaged Rectangular Recycled Aggregate Concrete-Filled Steel Tubular Columns

In order to establish a suitable restoring force correction model for enveloped steel jacket (ESJ)-confined seismic-damaged rectangular recycled aggregate concrete-filled steel tubular (RRACFST) columns, based on experimental research, a study of the seismic performance and parameters of ESJ-confined seismic-damaged RRACFST columns was carried out. The restoring force theory, model test, and OpenSees simulation of ESJ-confined seismic-damaged RRACFST columns were conducted. Firstly, a trilinear model of the skeleton curve and a suitable restoring force model for ESJ-confined seismic-damaged RRACFST columns were established. The results were compared with the model test results, and it was found that the two results had good consistency. Secondly, the initial damage of the RRACFST column was simulated by the reducing material properties method, and a correct numerical model for ESJ-confined seismic-damaged RRACFST columns was proposed. The influence mechanism of seismic parameters of the RRACFST column was clarified. Finally, the seismic parameter combination with the best seismic performance for ESJ-confined seismic-damaged RRACFST columns was established; namely, the replacement rate of recycled coarse aggregate is 50%, the concrete strength is C40, the axial compression ratio is 0.3, the strength of the rectangular steel tube is Q345, the wall thickness of the steel tube is 4 mm, and the slenderness ratio is 7.5.

“I will raise my hand and say ‘I over-trust Autopilot’. I use it too liberally” – Drivers’ reflections on their use of partial driving automation, trust, and perceived safety

Introduction: Partially automated cars are on the road. Trust in automation and perceived safety are critical factors determining use of automation. Background: Drivers misuse partially automated driving systems. Misuse is associated with mis-calibrated trust in the automation. Research gap: Little is known about the factors impacting the perceived safety when using partial driving automation. Research objective: The main objective of the present study is to provide a comprehensive driver perspective on the psychological aspects of automation use pertaining to trust in automation, perceived safety, and its relationship with use of automation. Method: Semi-structured interviews (n = 103) were conducted with users of partially automated driving systems. Supplemented with content analysis, natural language processing (NLP) techniques were applied to perform automatic text processing. Guided seed-term analysis was conducted to identify the number of occurrences of the subcategories in the dataset. Main results: We identified human operator-related, automation-related, and environmental factors of trust and perceived safety. The identified factors were more strongly associated with perceived safety than with trust. Participants with physical and visual impairments reported to feel safer using the automation compared to driving manually. Neurotic behavior during manual driving contributed to lower trust and perceived safety using the automation. A correct mental model of the capabilities and limitations of the automation did not guarantee proper automation use. A novel conceptual, process-oriented model, titled PTS-a (predicting trust in and perceived safety of automation use), synthesizes the results of the data analysis. Informed by the cognition-leads-to-emotions approach, the model posits that trust as cognition precedes perceived safety as affective construct. Trust and perceived safety determine how human operators (mis-, dis-)use the automation. Future research: We recommend future research to perform experimental studies to identify cognitive-related thoughts and beliefs pertaining to trust in automation and perceived safety to contribute to the operationalization of these constructs, and unravel the nature of their relationship.