Intuitive Model Research Articles

Complex system fault diagnosis based on interpretive structural model

To realize the fault diagnosis of complex systems, a fault diagnosis method for complex systems based on the explanatory structural model is proposed. According to the fault mechanism analysis, the fault association matrix of system elements is established. The explanatory structural model is applied to transform the fault association relationship of complex systems into an intuitive hierarchical structural model through matrix transformation, so as to realize the structuring and hierarchical propagation of system faults. The PageRank algorithm is introduced to evaluate the influence and impact of system element faults. According to the influence and impact of the same-layer components and the fault transmission logic, the main cause of system fault transmission is clarified, which provides a basis for fault diagnosis. Finally, a specific application is carried out with a certain device as an example to verify the effectiveness of the method.

Emotion-Driven Moral Evaluation: A Mechanistic Study Based on the Drift-Diffusion Model.

Moral evaluation is identified as the first stage in the theory of moral judgment, and academics believe that it may align with the social intuitionist model. This study aims to prove that the model's emotional dominance hypothesis applies to moral evaluation by presenting a computational decision-making model that mathematically formalizes this emotional dominance decision-making process. We also compared different types of valence evaluation tasks to test the emotional priority hypothesis. We used a convenience sampling method to randomly recruit 30 enrolled college students. The drift-diffusion model was employed to analyze reaction times for words with various emotional and moral valences Additionally, we designed different valence evaluation tasks based on the response relevance hypothesis and evaluated the processing order through reaction time comparisons. The analysis revealed that the emotional mechanism of immoral evaluation differs from moral evaluation. An increase in emotional valence accelerates the speed of evidence accumulation (v) for moral evaluation (M = 1.21, 0.2% < 0 < 99.8%) but lowers decision caution (a) in immoral evaluation (M = -0.64, 96.1% < 0 < 3.9%). In contrast, moral valence does not have a significant influence on evaluation processes (v, M = -0.28, 72.1% < 0 < 27.9%; a, M = -0.32, 79.3% < 0 < 20.7%). Furthermore, We found no significant difference in reaction times between moral and immoral words in the emotional evaluation task (F(1,29) = 0.55, p = 0.464, partial η2 = 0.02), but a significant difference existed in the moral evaluation task (F(1,29) = 17.99, p < 0.001, partial η2 = 0.38), indicating that the tendency of relatively fast immoral evaluation in emotional evaluation tasks may be caused by emotional priority. Our findings support the intuitive model's emotional dominance hypothesis and introduce a new emotional mechanism into moral evaluation. This study clarifies the distinct emotional processes in moral and immoral evaluations, fills a gap in the research on moral evaluation, and offers insights into human decision-making in moral contexts.

Synergistic application of digital outcrop characterization techniques and deep learning algorithms in geological exploration

In order to meet the needs of geologists for the analysis of data characterizing field outcrops (rock sections or formations exposed on the ground surface), this study developed a field digital outcrop visualization platform based on Cesium (a 3D geospatial visualization technology) digital outcrop characterization technology. The platform was developed based on WebGL (a protocol for rendering interactions on web pages), which overcame the shortcomings of traditional software in terms of visualization, cross-device, cross-platform, and ease of use. Firstly, UAV inclined photography is used for data collection, which transforms a large amount of geological data into an intuitive 3D geological model, while the visualization platform provides rich measurement and mapping tools for the identified features, which more intuitively displays the outcrop information, helps geological explorers to understand the geological conditions in the field more quickly and comprehensively, and improves the analysis efficiency and ease-of-use of outcrop characterization data. Combined with the improved VGG19 (a deep convolutional neural network architecture) algorithm model, it has excellent performance in dealing with the fine texture and complex structure of rocks, which significantly improves the accuracy of lithology identification. The synergistic application of this technology provides geologists with a faster and more comprehensive means to understand the geological conditions in the field. The reliability of combining the Cesium digital outcrop characterization technology with the VGG19 lithology identification algorithm in geological exploration is verified through case studies. The synergistic application of this technology will greatly enhance the efficiency and ease of analysis of outcrop characterization in the field, and provide new perspectives for future research in geosciences.

How rational inference about authority debunking can curtail, sustain, or spread belief polarization.

In polarized societies, divided subgroups of people have different perspectives on a range of topics. Aiming to reduce polarization, authorities may use debunking to lend support to one perspective over another. Debunking by authorities gives all observers shared information, which could reduce disagreement. In practice, however, debunking may have no effect or could even contribute to further polarization of beliefs. We developed a cognitively inspired model of observers' rational inferences from an authority's debunking. After observing each debunking attempt, simulated observers simultaneously update their beliefs about the perspective underlying the debunked claims and about the authority's motives, using an intuitive causal model of the authority's decision-making process. We varied the observers' prior beliefs and uncertainty systematically. Simulations generated a range of outcomes, from belief convergence (less common) to persistent divergence (more common). In many simulations, observers who initially held shared beliefs about the authority later acquired polarized beliefs about the authority's biases and commitment to truth. These polarized beliefs constrained the authority's influence on new topics, making it possible for belief polarization to spread. We discuss the implications of the model with respect to beliefs about elections.

Plasma opacity induced by laser-driven movement of background ions

Abstract The transition threshold from relativistically transparent regime to opaque regime in the interaction of an ultra-intense laser pulse and a bulk plasma with mobile background ions is investigated. The threshold corresponds to the onset of laser hole-boring. We show that for an ultra-intense laser, the threshold depends on the ion composition of the plasma, and the corresponding plasma density is significantly lower than that with ion immobile plasma. These are supported by 1D PIC simulations by using hydrogen plasma and fully ionized carbon plasma. The movement of background ions modifies the dynamics and distribution of electrons in the plasma, which results in the Doppler-red-shift of the incident laser and the increase of the effective plasma density. An intuitive model, which gives the dependence of the transparency-opaqueness threshold on laser intensity and frequency, as well as the ion composition, is established. It is tested by multi-dimensional PIC simulations with hydrogen plasma.

Multi-Behavior Recommendation with Personalized Directed Acyclic Behavior Graphs

A well-developed recommendation system can not only leverage multi-typed interactions (such as page view, add-to-cart, and purchase) to better identify user preferences, but also demonstrate high performance, low complexity, and strong interpretability. However, many existing solutions for multi-behavior recommendation fall short of intuitive modeling of real-world scenarios, leading to overly complex models with massive parameters and cumbersome components. In particular, they share two critical limitations: (1) Some pioneering models are built upon the strict assumption of cascade effects across behaviors, which contradicts multifarious behavior paths in practical applications. (2) Existing approaches fail to explicitly capture the unique idiosyncrasies of users and even neglect the inherent nature of items involved in the multi-behavior interactions. To this end, we propose a novel Directed Acyclic Graph Convolutional Network (DA-GCN) for the multi-behavior recommendation task. Specifically, we pinpoint the partial order relations within the monotonic behavior chain and extend it to personalized directed acyclic behavior graphs to exploit behavior dependencies. Then, a GCN-based directed edge encoder is employed to distill rich collaborative signals embodied by each directed edge. In light of the information flows over the directed acyclic structure, we propose an attentive aggregation module to gather messages from all potential antecedent behaviors, representing distinct perspectives to understand the terminated behavior. Thus, we obtain comprehensive representations for the follow-up behavior through learnable distributions over its preceding behaviors, explicitly reflecting personalized interactive patterns of users and underlying properties of items simultaneously. Finally, we design a customized multi-task learning objective for flexible joint optimization. Extensive experiments on public benchmarking datasets fully demonstrate the superiority of DA-GCN with significant performance improvement and computational efficiency over a wide range of state-of-the-art methods. Our code is available at https://github.com/xizhu1022/DA-GCN .

Reinforcing mindware or supporting cognitive reflection: Testing two strategies for addressing a persistent learning challenge in the context of air resistance

In this study, we have explored the effectiveness of two instructional approaches in the context of the motion of objects falling at terminal speed in the presence of air resistance. We ground these instructional approaches in dual-process theories of reasoning, which assert that human cognition relies on two thinking processes. Dual-process theories suggest multiple possible avenues by which instruction might impact student reasoning. In this paper, we compare two possible instructional approaches: one designed to reinforce the normative approach (improving the outputs of the intuitive process) and another that guides students to reflect on and analyze their initial ideas (supporting the analytic process). The results suggest that for students who have already demonstrated a minimum level of requisite knowledge, instruction that supports analysis of their likely intuitive mental model leads to greater learning benefits in the short term than instruction that focuses solely on providing practice with the normative mindware. These results have implications for the design of instructional materials and help to demonstrate how dual-process theories can be leveraged to explain the success of existing research-based materials. Published by the American Physical Society 2024

An effective drift-diffusion model for pandemic propagation and uncertainty prediction

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

Enhanced formula-based method to estimate burnup, initial enrichment, and cooling time using limited gamma-ray source data of reference PWR spent nuclear fuel assemblies

This study proposes a framework to develop a model for estimating burnup (BU), initial enrichment (IE), and cooling time (CT) of spent nuclear fuel (SNF) overcoming the limitations of existing models in determining IE. Database (DB) of gamma-ray sources for PWR SNF was generated using ORIGAMI. Target gamma radionuclides were selected based on the ease and credibility of measurement. The concept of Product of Powers of Radioactivity (PPR) was suggested, and PPRs for modeling were selected through evaluation of all possible PPRs. Functions established for PPRs were applied to estimate BU, IE, and CT (BIC) of PWR SNF assemblies. Optimized PPR combinations for estimating BU, IE, and CT respectively, as well as three variables simultaneously with the highest accuracy, were suggested. The estimation model proposed in this study enhanced IE estimations, maintaining the reliability of BU and CT estimations. The above framework enables construction of intuitive BIC estimation models using only a small amount of data. This can help hasten the development of estimation models for SNF from various reactor types. Consequently, it is expected to contribute to the safety and safeguards of SNF management by improving the verification that SNF meets acceptance criteria and conforms to the declared information.

A novel nomogram to predict psoriatic arthritis in patients with plaque psoriasis.

To construct a predictive model for Psoriatic Arthritis (PsA) based on clinical and ultrasonic characteristics in patients with plaque psoriasis (PsP). Demographic, clinical, and ultrasound data were collected from patients with PsP and PsA between May 2019 and December 2022. A total of 212 patients with PsP and 123 with PsA in the training cohort, whereas the validation cohort comprised 91 patients with PsP and 49 with PsA. The multivariate logistic regression identified nail psoriasis (odds ratio [OR] 1.88, 95%CI: 1.07-3.29), synovitis (OR 18.23, 95% CI: 4.04-82.33), enthesitis (OR 3.71, 95%CI: 1.05-13.14), and bone erosion (OR 11.39, 95%CI: 3.05-42.63) as effective predictors for PsA. The area under the curve was 0.750 (95%CI, 0.691-0.806) and 0.804 (95%CI, 0.723-0.886) for the training and validation cohorts, respectively. The Hosmer-Lemeshow goodness-of-fit test showed good consistency for both the training cohort (p = 0.970) and the validation cohort (p = 0.967). Calibration curves also indicated good calibration for both cohorts. The DCA revealed that the predictive model had good clinical utility. We have developed a quantitative, intuitive, and convenient predictive model based on nail psoriasis, synovitis, enthesitis, and bone erosion to assess the risk of PsA in patients with plaque psoriasis.

Komunikasi Qur’ani dalam Sistem Pengasuhan di Pondok Pesantren Darul Huda Wonodadi

This research explores how Islamic boarding schools apply communication in accordance with the teachings of the Koran to shape the Islamic character of the students. The character building process is carried out through various activities participated in by students. However, in their learning and development process, problems such as violence between students and bullying often arise. Therefore, a serious approach is needed in dealing with problems that occur in the student learning process. The research uses Qur'anic communication and parenting theory. The research method used is qualitative descriptive analytical, by conducting research at the Darul Huda Islamic Boarding School and analyzing the findings using Qur'anic communication theory. The findings in this research state that the parenting system applies Qur'anic communication. The parenting here is based on the principles of Qoulan Ma'rufa, Qoulan Sadida, Qoulan Baligha, Qoulan Karima, Qoulan Maisura, Qaulan Layyina, Qaulan Tsaqila, Qoulan 'Azhima, and Ahsanu Qaulan. Implementing Qur'anic communication can also handle bullying cases. Darul Huda Islamic Boarding School uses a persuasive, wise, humanist, polite and intuitive model. Apart from that, the implementation of communication based on the Koran has a comprehensive and effective approach, method and model, which can be used by caregivers in preventing and dealing with violence and bullying that occurs among students.

Reasoning From Quantified Modal Premises.

Quantified modal inferences interest logicians, linguists, and computer scientists, but no previous psychological study of them appears to be in the literature. Here is an example of one: All those artists are businessmen. Paulo is possibly one of the artists. What follows? People tend to conclude: Paulo is possibly a businessman (Experiment 1). It seems plausible, and it follows from an intuitive mental model in which Paulo is one of a set of artists who are businessmen. Further deliberation can yield a model of an alternative possibility in which Paulo is not one of the artists, which confirms that the conclusion is only a possibility. The snag is that standard modal logics, which deal with possibilities, cannot yield a particular conclusion to any premises: Infinitely many follow validly (from any premises) but they do not include the present conclusion. Yet, further experiments corroborated a new mental model theory's predictions for various inferences (Experiment 2), for the occurrence of factual conclusions drawn from premises about possibilities (Experiment 3) and for inferences from premises of modal syllogisms (Experiment 4). The theory is therefore plausible, but we explore the feasibility of a cognitive theory based on modifications to modal logic.

SketchDream: Sketch-based Text-To-3D Generation and Editing

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing texture-less mesh models within predefined categories. Integrating sketch and text simultaneously for 3D generation promises enhanced control over geometry and appearance but faces challenges from 2D-to-3D translation ambiguity and multi-modal condition integration. Moreover, further editing of 3D models in arbitrary views will give users more freedom to customize their models. However, it is difficult to achieve high generation quality, preserve unedited regions, and manage proper interactions between shape components. To solve the above issues, we propose a text-driven 3D content generation and editing method, SketchDream, which supports NeRF generation from given hand-drawn sketches and achieves free-view sketch-based local editing. To tackle the 2D-to-3D ambiguity challenge, we introduce a sketch-based multi-view image generation diffusion model, which leverages depth guidance to establish spatial correspondence. A 3D ControlNet with a 3D attention module is utilized to control multi-view images and ensure their 3D consistency. To support local editing, we further propose a coarse-to-fine editing approach: the coarse phase analyzes component interactions and provides 3D masks to label edited regions, while the fine stage generates realistic results with refined details by local enhancement. Extensive experiments validate that our method generates higher-quality results compared with a combination of 2D ControlNet and image-to-3D generation techniques and achieves detailed control compared with existing diffusion-based 3D editing approaches.

Reasoning about possibilities: Modal logics, possible worlds, and mental models.

Everyone reasons about possibilities. This article explains how they could do so using mental models. The theory makes four major claims: 1. Correct inferences are necessary, referring only to facts or possibilities to which the premises refer and not ruling any of them out, for example: She left or hid; Therefore, it's possible that she left and possible that she hid. 2. A possibility such as that she hid, which is represented in an intuitive model, presupposes the possibility that it did not occur, she did not hide, which, if reasoners deliberate, is represented in the resulting model. 3. Reasoners condense consistent possibilities, such as the earlier pair, into one possibility: it is possible that she left and she hid. 4. Inconsistencies, such as she left or hid, and she neither left nor hid, refer to no possibilities whatsoever - they have an empty model - and so their only effects are local. Hence, any inference can be withdrawn with impunity if there is knowledge to the contrary. Experiments have corroborated each of these principles. They are incompatible with four essentials of standard modal logics, which concern deductions based on "possible" or "necessary". Their formal deductions correspond to valid inferences, which have no counterexamples in which the premises are true but the conclusion is false. And so the article examines the differences between the two approaches, and explores the adaptation of a modal logic to account for correct human reasoning. Its feasibility is an open question.

Classical guitar intonation and compensation: The well-tempered guitar.

An intuitive model of classical guitar intonation is presented that includes the effects of the resonant length of the fretted string, linear mass density, tension, and bending stiffness. An expression is derived for the vibration frequencies of a stiff string using asymmetric boundary conditions at the saddle and the fret. Based on logarithmic frequency differences ("cents") that decouple these physical effects, Taylor series expansions are introduced that exhibit clearly the origins of frequency deviations of fretted notes from the corresponding 12-tone equal temperament (12-TET) values. A simple in situ technique is demonstrated for measurement of the changes in frequency of open strings arising from small adjustments in length, and a simple procedure is proposed that any interested guitarist can use to estimate the corresponding frequency shifts due to tension and bending stiffness for their own guitars and string sets. Based on these results, a least-squares fit method is employed to select values of saddle and nut setbacks that map fretted frequencies-for a particular string set and guitar-almost perfectly onto their 12-TET targets. A general approach to tempering an "off-the-shelf" guitar is shown to further reduce the tonal errors inherent in any fretted musical instrument.

RESEARCH ON SOFTWARE FOR ERROR PROBABILITY PREDICTION IN BUSINESS PROCESS MODELS USING LOGISTIC REGRESSION

Business process modeling allows to graphically represent organizational activities and related events. It allows to identify areas for improving the organizational performance, define requirements for software solutions, and, in general, to facilitate communication between IT and business parties within or between different organizations. Therefore, at the stage of representing the activity in the form of a model, it is necessary to understand how likely it is that errors will occur during the implementation of the depicted business process. Thus, this study aims to improve the quality of business process models by solving the problem of predicting the error probability of business process execution. In order to assign error probabilities to each business process model from the training dataset, it is proposed to use one of the complexity metrics – the coefficient of network connectivity. To predict the error probability in business process execution, it is proposed to use the simplest and most intuitive machine learning model – logistic regression. As independent variables, it is proposed to choose the basic metrics of business process modeling – the number of nodes and arcs. Thus, the algorithm for solving the task includes steps related to calculating probabilities for the training data set, preparing training and test sets, determining regression parameters, visualization, and evaluation of training results. For the software that implements the proposed approach, a client-server architecture was chosen due to its flexibility and scalability. When developing software components, the Scikit-Learn machine learning library and the Python programming language were used to build a logistic regression mathematical model. The software tool is implemented as a web application based on MySQL, the Node JS platform, and the Express JS web framework. The quality assessment results of the developed prediction model indicate the suitability of the software tool for solving the problem of predicting the error probability of business process execution.

Cone beam neutron interferometry: From modeling to applications

Phase-grating moiré interferometers (PGMIs) have emerged as promising candidates for the next generation of neutron interferometry, enabling the use of a polychromatic beam and manifesting interference patterns that can be directly imaged by existing neutron cameras. However, the modeling of the various PGMI configurations is limited to cumbersome numerical calculations and backward propagation models which often do not enable one to explore the setup parameters. Here we generalize the Fresnel scaling theorem to introduce a k-space model for PGMI setups illuminated by a cone beam, thus enabling an intuitive forward propagation model for a wide range of parameters and experimental setups. The interference manifested by a PGMI is shown to be a special case of the Talbot effect, and the optimal fringe visibility is shown to occur at the moiré location of the Talbot distances. We derive analytical expressions for the contrast and the propagating intensity profiles in various conditions and provide the first analysis of the PGMI dark-field imaging signal when considering sample characterization. The model's predictions are compared to experimental measurements and good agreement is found between them. Last, we propose and experimentally verify a method to recover contrast at typically inaccessible PGMI autocorrelation lengths. The presented work provides a toolbox for analyzing and understanding existing PGMI setups and their future applications, for example extensions to two-dimensional PGMIs and characterization of samples with nontrivial structures. Published by the American Physical Society 2024

Tunable metamaterials with carrier-induced effective permittivity for active control of electromagnetic fields in semiconductor manufacturing device

Precise control of electromagnetic fields is critical in many advanced manufacturing processes, such as those used in the semiconductor industry, where device performance relies on precision and uniformity. Here, we introduce a solution to control electromagnetic fields via permittivity modulation without the limitations of resonance-based approaches, through a patterned semiconductor enabling permittivity tuning via carrier-density modulation. This carrier-responsive metamaterial (CRM) exhibits frequency-independent performance over a broad frequency spectrum and significant permittivity tunability through controlled semiconductor conductivity. Furthermore, the conductivity response and the tuning range can be easily modulated through the variation of semiconductor materials and geometrical parameters. We present an intuitive model that explains the relationship between the CRM’s structure and properties, including its effective permittivity and loss tangent. Supported by comprehensive simulations and experimental validations, our findings show that the effective permittivity can be increased by over 3.5 times with low dielectric loss across a wide frequency range. As an application, we explore the CRM’s potential in plasma control, revealing its ability to influence plasma density nearly 30% by modulating its effective permittivity, exhibiting CRM’s versatile functionality and potential impact across diverse technological domains.

A Novel Model to Minimize Time Series Forecasting Error using Convolutional Attention

The main consideration of this paper is time series forecasting using machine learning methods. While hundreds of studies have been conducted on this topic, we propose a novel, yet intuitive model, using technical indicators in combination with a focus on lagged relationships and the machine learning technique called BEDCA (Belief Encoder-Decoder with Convolutional Attention). BEDCA takes into consideration technical indicators to improve predictive accuracy. To evaluate our model, we used historical Coca-Cola stock data, room temperature data, and daily data including the number of COVID-19 case data. We found that our model trained relatively quickly and had a high accuracy (low loss). Overall, BEDCA outperformed the standard Long-Short Term Memory (LSTM) network on all three datasets, achieving MAE reduction of 74.1%, 41.3%, and 69.0%, respectively, although it took 3.05, 3.26, and 3.20 times longer to train on each dataset respectively. Our results highlight that convolutional attention is a promising form of attention and that technical indicators are important considerations for time series data.

Accurate Protein pKa Prediction with Physical Organic Chemistry Guided 3D Protein Representation.

Protein pKa is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-pKa values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein pKa prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a mean absolute error (MAE) of 0.33 pKa units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.