Implicit Relations Research Articles

A generalized common fixed point theorem in complex valued $b-$metric spaces

In this work we are interested in the generalization of coincidence point and fixed point theorem for a 4-tuple of mappings satisfying a new type of implicit relation in complex valued $b-$metric spaces.

Development of an electronic stopping power model based on deep learning and its application in ion range prediction

Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations, which is promising for solving traditional physical challenges. A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid, where accurate prediction of stopping power is a long-time problem. In this work, we develop a deep-learning-based stopping power model with high overall accuracy, and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy, and the corresponding projected range. This electronic stopping power model, based on deep learning algorithm, could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.

Finite element model for a coupled thermo-mechanical system in nonlinear strain-limiting thermoelastic body

We investigate a specific finite element model to study the thermoelastic behavior within the context of nonlinear strain-limiting constitutive relation. As a special subclass of implicit relations, the thermoelastic response of our interest is such that stresses can be arbitrarily large but strains remain small, particularly in the neighborhood of crack-tips. In the present communication, we consider a two-dimensional coupled system — linear and quasi-linear partial differential equations for temperature and displacements, respectively. A standard finite element method of continuous Galerkin is then employed to obtain the numerical solutions for the field variables, where two distinct temperature distribution of the Dirichlet type are considered for boundary condition. From a domain with an edge-crack, we find that the near-tip strain growth in the proposed model is slower than the growth of stress, which is the salient feature compared to the predictions of singular strain based on the classical linearized description of the elastic body. In essence, the model can be inherently consistent with the assumption of linearized elasticity and infinitesimal strain theory. This study can provide a theoretical and computational framework to develop physically meaningful models and examine other coupled multi-physics such as an evolution of complex network of cracks induced by thermal shocks.

Modeling of Trailing Suction Hopper Dredger: A Two-Step Method Integrating Deep Neural Network and Human Knowledge

Learning the quantitative and explainable relation between the dredging performance and operating variables from big sensor data is an important yet challenging problem in Trailing Suction Hopper Dredger (TSHD) applications. The deep learning technique cannot be used directly due to two factors, of which one is its black-box nature, and the other is the one-to-many mapping from the dredging performance to the domain of parameters. In this paper we propose a two-step framework combining deep learning and human knowledge for the modeling of dredging performance. The first step is parameter learning, in which deep neural network is adopted to find the implicit relation between operating variables and the performance. Based on this implicit model, we regenerate a new dataset, which is combined with human knowledge in the second step to build the explicit model with human knowledge. The explicit formula between operating variables and dredging performance are obtained by solving a sparse optimization problem. With this formula, we can choose feasible combinations of controllable variables for given performance indices. The effectiveness and advantage of our proposed scheme are verified by practical data collected in a sand extracting project.

Board Committee Overlap and the Use of Earnings in CEO Compensation Contracts

Using proxy statement data describing the terms of compensation contracts, we examine how overlapping membership between compensation and audit committees influences the use of earnings metrics in compensation. Although research predicts that such overlap could either increase or decrease the reliance on earnings, we find that firms with overlapping directors rely less on earnings-based performance measures in incentive contracts without altering the overall level of performance-contingent cash bonuses. In addition, we provide evidence that firms substitute earnings measures with measures less subject to earnings management. Our findings are robust to potential alternative explanations, extend to an implicit relation between earnings and compensation for a larger sample, and are not driven by the tendency toward an overlapping committee structure more broadly. This paper was accepted by Suraj Srinivasan, accounting.

Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System

Recently, online social networks have enriched the users’ lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in. The most advanced graph neural network based social recommendation methods start to utilize the higherorder social relations, e.g. the friends of friends, to reveal users’ preferences. However, existing high-order methods ignore the implicit social relations among users and the users’ interests changing dynamically over time. In this paper, we propose a Multi-Order Hypergraph Convolutional Neural Network (MOHCN) for dynamic social recommendation system to improve the recommendation task, which models the users’ dynamic interest evolution at the session level. To compensate for the lack of social information of some users, we combine the implicit social relations obtained from user-item interaction graph with the explicit social relations from user-user social graph through hypergraph modeling. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed MOHCN compared with the state-of-the-art methods.

A methodology for the detection of functional relations of mechatronic components and assemblies in brownfield systems

The huge potential of the Digital Twin and its benefits in use cases such as virtual commissioning or reconfiguration planning for production systems is often described and well known. One reason that the usage of Digital Twins is becoming increasingly important are current trends, such as individualized mass production, which lead to more frequent reconfigurations of production systems or require higher flexibility. At the same time, many production systems have been in productive use for some time, so-called brownfield systems. Most of them do not have a Digital Twin, but would benefit from one, if available. However, the creation of a Digital Twin is a significant additional effort, due to the creation of the underlying models and the relations between them. Some promising approaches for the creation of domain-specific models already exist, which can be used for the creation of the unconnected parts of a Digital Twin. However, there are still few results for the creation of inter-domain relations, which cause problems in the creation of Digital Twins. This paper therefore presents a methodology for the automated reconstruction of functional relations as well as the separation of functionally related component groups of the plant and its software. For this purpose, the methodology imports the native PLC code via XML, extracts the relevant information from the code and enriches the resulting information model with explicit information about implicit relations in the code. Based on this, various elements of the PLC code are analyzed and the relationship of individual mechatronic components, that are available as signals, are examined and quantified. Thus, a part of the required relations for a Digital Twin is recognized automatically. The methodology and its benefits for the Digital Twin creation is presented and explained using a modular production system with several manufacturing stations as a proof-of-concept.

Unified IT&OT Modeling for Cybersecurity Analysis of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) engineering profoundly relies on modeling methods to represent the system and study the operation and cybersecurity of CPSs. The operation of a CPS is the result of the collaboration between Information Technology (IT) and Operational Technology (OT) components. While OT focuses on the system's process physics, the emphasis of IT is on information flow. Consequently, different system models are utilized to study various aspects of CPSs, which may infer different views of the same system. The increasing complexity of CPSs and the high number of cyberattacks against Industrial Control Systems (ICSs) and CPSs in recent years have highlighted the necessity of considering these interrelations based on a unified model to analyze cybersecurity of CPSs. However, the diversity of engineering fields and implicit relations and dependencies between them have made it difficult to integrate the modeling methods towards a unified IT&OT model of CPSs.In this paper, we propose a comprehensive method, based on bond graphs, to model CPS and analyze their cybersecurity. Unlike existing methods, modeling the cyber layer along with the physical layer based on the system flow provides a holistic graphical representation of a CPS, which facilitates collaboration between IT and OT experts.

Cone S-metric spaces and some new fixed point results for contractive mapping satisfying $\phi$-maps with implicit relation

In this article, we use implicit relations to establish some new fixed point results in the setting of cone S-Metric spaces for $phi$-map type contractive conditions. An example is provided to support our results. Our results extend, unify, and generalize several results from the current literature. In particular, the results presented in this paper improve and generalize the corresponding results of Sedghi and Dung[8], which used the ideas of Saluja, G. S. [19].

Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention Networks

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DanSmp</small> , a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DanSmp</small> is capable of improving stock prediction with the constructed MKG.

Friction factor from velocity profiles in smooth turbulent channel flows

Fluid dynamics plays an important role in many Renewable energies studies, i.e. wind and tidal turbines, wave energy geothermal and solar power, …). Friction factor f is an important parameter for the determination of pressure drop in different processes and systems. In this study, we use DNS data of turbulent smooth channels to evaluate different methods. First, a recalibration of Dean’s correlation (Dean, 1978) is proposed. The aim of the study is to obtain accurate wall friction factors from velocity profiles. On the one hand, we obtained two implicit analytical relations based on the law-of-the-wall: a logarithmic friction relation similar to that of pipes and a linear-logarithmic friction relation. On the other hand, we obtained f from the computation of the average velocity. It is first calculated from the law-of-the-wall and allows a good prediction of f for Reτ &gt; 395 but presents a gap for low Reτ which is related to inaccurate velocities. Low-Reynolds number effect in channel flows has been previously observed in different experimental and computational studies. In order to provide suitable friction factor values, it is important to predict velocities accurately on the overall channel height. We used therefore a more appropriate method which consists to use for y+ &lt; 20 the momentum equation with an eddy viscosity formulation (Absi, 2019) and the log-wake law for high y+ values. This method provides accurate friction factor values and allows good agreement with DNS data.

Поэтика песни в книге Е. А. Баратынского «Сумерки»

Genre complexity is a prominent feature of Baratynsky’s late-period writings. For some texts from the book of poems The Twilight, an implicit relation to the poetics of a song genre can be demonstrated. The article focuses on two such lyrical works from the poetry collection.

The ordered implicit relations and related fixed point problems in the cone $ b $-metric spaces

&lt;abstract&gt;&lt;p&gt;In this paper, we introduce an ordered implicit relation. We present some examples for the illustration of the ordered implicit relation. We investigate conditions for the existence of the fixed points of an implicit contraction. We obtain some fixed point theorems in the cone $ b $-metric spaces and hence answer a fixed-point problem. We present several examples and consequences to explain the obtained theorems. We solve an homotopy problem and show existence of solution to a Urysohn Integral Equation as applications of the obtained fixed point theorem.&lt;/p&gt;&lt;/abstract&gt;

Mapping the landscape of a wide interdisciplinary curriculum: a network analysis of a Korean university and the lessons learnt

Abstract Interdisciplinary programmes have become common in universities and research groups’ curricula. This study conducted a network analysis on a Korean university’s undergraduate curriculum and used several visualisation tools to assess keywords across courses and departments, revealing epistemological distances between the courses/departments and their concepts of study. This data-driven methodology defined the characteristics of close or neighbouring departments, making it possible to implement narrow interdisciplinarity through common subjects within the courses. Interestingly, a further projected network could determine the implicit relations between departments that are not considered close, which would make it possible to implement a wide interdisciplinary curriculum. The data-driven network analysis conducted in this study contributes to searching for new programmes for specific levels of interdisciplinarity on an empirical basis.

Phanariot Tongues: The Mavrocordatos Family and the Power of the Turkish Language in the Eighteenth-Century Ottoman Empire

Abstract The Phanariots — Grecophone Christian elites who ruled the Danubian principalities in the eighteenth century — were the only non-Muslims in the Ottoman Empire who claimed power by virtue of their command of the Turkish language. Why were they the rare exception and what does their story reveal about the ways in which power and language were intertwined in the early modern Ottoman Empire? The implicit power relations embedded in the Turkish language are rendered visible in a unique text written in 1731 in which Constantine Mavrocordatos, a Phanariot prince, attempted to school his younger brother in Turkish through a series of twelve, play-like dialogues. The dialogues did not aim to teach the formal grammar of Turkish but to demonstrate the power of speech by familiarizing the reader with the eloquent and witty repartee of Ottoman bureaucrats. Through an analysis of the text — which includes reestablishing its authorship and date of composition — the article examines the Phanariots’ liminal position in Ottoman governance, especially in the newly ascendant imperial bureaucracy, through the prism of language. In doing so, it also rewrites the place of the Mavrocordatos family in the story of the Enlightenment in the Ottoman Empire.

Social influence-based personal latent factors learning for effective recommendation

Social recommendation has become an important technique of various online commerce platforms, which aims to predict the user preference based on the social network and the interactive network. Social recommendation, which can naturally integrate social information and interactive structure, has been demonstrated to be powerful in solving data sparsity and cold-start problems. Although some of the existing methods have been proven effective, the following two insights are often neglected. First, except for the explicit connections, social information contains implicit connections, e.g., indirect social relations. Indirect social relations can effectively improve the quality of recommendation when users only have few direct social relations. Second, the strength of social influence between users is different. In other words, users have different degrees of trust in different friends. These insights motivate us to propose a novel social recommendation model SIER (short for Social Influence-based Effective Recommendation) in this paper, which incorporates interactive information and social information into personal latent factors learning for social influence-based recommendation. Specifically, user preferences are captured in behavior history and social relations, i.e., user latent factors are shared in interactive network and social network. In particular, we utilize an overlapping community detection method to sufficiently capture the implicit relations in the social network. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

Memorizing All for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clues from explicit connectives. An implicit discourse relation recognizer has to carefully tackle the semantic similarity of sentence pairs and the severe data sparsity issue. In this article, we learn token embeddings to encode the structure of a sentence from a dependency point of view in their representations and use them to initialize a baseline model to make it really strong. Then, we propose a novel memory component to tackle the data sparsity issue by allowing the model to master the entire training set, which helps in achieving further performance improvement. The memory mechanism adequately memorizes information by pairing representations and discourse relations of all training instances, thus filling the slot of the data-hungry issue in the current implicit discourse relation recognizer. The proposed memory component, if attached with any suitable baseline, can help in performance enhancement. The experiments show that our full model with memorizing the entire training data provides excellent results on PDTB and CDTB datasets, outperforming the baselines by a fair margin.

A bipotential-based macroscopic fatigue criterion of porous materials with a pressure-sensitive and non-associated plastic solid matrix and comparison with numerical simulation

The formulation of macroscopic strength criteria under cyclic loading of ductile porous media containing a pressure sensitive and non-associated plastic matrix is still a pending issue. In this paper, a new approach is developed by using the bipotential theory. In this framework, non Generalized Standard Materials (GSM) are transformed into a class of Implicit Standard Materials (ISM), allowing the recovery of the flow rule normality in a weak form of an implicit relation. The classical shakedown theorems are extended to the homogenization of ISM. The solution of a thick wall tube under uniform pressure is established with the assumption of vanishing plastic strain increment over a single stabilized cycle. A macroscopic fatigue criterion is further delivered for the first time for porous materials with a non-associated Drucker–Prager type solid matrix. It is now possible to separate the effects of frictional and dilatancy angles. It is found that the shakedown limit load under hydrostatic loading is only related to the friction angle, but not the dilatancy one. The safety domain defined by the established criterion under general cyclic loads is comparatively reduced with the decrease of dilatancy angle. The new criterion is fully assessed by comparing the theoretical predictions to FEM-based step-by-step simulations for different values of porosity and frictional and dilatancy angles. Finally, the new criterion can also be applied to associated solid matrix as a special case and it significantly improves the accuracy of predictions provided by previous works based on the Melan’s static theorem.

Extended Branciari quasi-b-distance spaces, implicit relations and application to nonlinear matrix equations

This study introduces extended Branciari quasi-b-distance spaces, a novel implicit contractive condition in the underlying space, and basic fixed-point results, a weak well-posed property, a weak limit shadowing property and generalized Ulam–Hyers stability. The given notions and results are exemplified by suitable models. We apply these results to obtain a sufficient condition ensuring the existence of a unique positive-definite solution of a nonlinear matrix equation (NME) mathcal{X}=mathcal{Q} + sum_{i=1}^{k}mathcal{A}_{i}^{*} mathcal{G(X)}mathcal{A}_{i}, where mathcal{Q} is an ntimes n Hermitian positive-definite matrix, mathcal{A}_{1}, mathcal{A}_{2}, …, mathcal{A}_{m} are n times n matrices, and mathcal{G} is a nonlinear self-mapping of the set of all Hermitian matrices that are continuous in the trace norm. We demonstrate this sufficient condition for the NME mathcal{X}= mathcal{Q} +mathcal{A}_{1}^{*}mathcal{X}^{1/3} mathcal{A}_{1}+mathcal{A}_{2}^{*}mathcal{X}^{1/3} mathcal{A}_{2}+ mathcal{A}_{3}^{*}mathcal{X}^{1/3}mathcal{A}_{3}, and visualize this through convergence analysis and a solution graph.

Evaluating the relationship between in vitro and in situ starch degradation rates

Determining starch degradability is important because of its implicit relation to gut health, ruminal acidosis, energy availability, feed intake, and animal productivity. In vitro assays for protein and fiber degradability have been useful tools for ration formulation; however, analogous assessments of starch degradability have limited update. Our objective was to test how an enzymatic in vitro starch degradation assay compared with the in situ technique in terms of its ability to rank feeds based on their starch degradation rates. Samples of 19 livestock feeds were obtained from 4 feed mills and subjected to in vitro and in situ starch degradation analyses. Seven ruminally cannulated wethers on forage were used for the in situ procedure. In vitro starch degradation was assessed using an acetate buffer technique with modified enzymatic incubation times: amylase (AML) 5 min and amyloglucosidase (AMG) 10 min; AML 10 min and AMG 20 min; AML 15 min and AMG 30 min; AML 30 min and AMG 60 min; AML 45 min and AMG 90 min; and AML 60 min and AMG 120 min. In situ rates were determined using the Ørskov method and in vitro predicted rates calculated assuming exponential decay. Relationships were analyzed as a linear mixed-effects model with in situ degradation rate as the response variable and in vitro predicted degradation rate, initial free glucose, starch, crude protein, neutral detergent fiber (NDF), and fat content as fixed effects. Interactions between in vitro predicted degradation rate and the nutrient percentages were also included. Feed source was included as a random effect. In vitro predicted starch degradation rates ranged from 2.98% to 279%/h and in situ degradation rates ranged from 1.53% to 42.8%/h. A relationship between in vitro and in situ methods was observed when starch, fat, NDF, and initial free glucose content were accounted for. Interactions between in vitro predicted degradation rate and starch and NDF contents were also observed, suggesting that these nutrients may confound the relationship between in vitro and in situ starch degradation measurements. Removing the rapidly degradable starch fraction from in situ analysis calculations improved the strength of the relationship between in situ and in vitro starch degradation rate. This in vitro assay may prove more useful for determining relative rankings of starch degradation rates among different feedstuffs rather than serving as an alternative to the in situ method. Additional investigation into assay repeatability and comparisons with true in vivo estimations of starch degradation are needed to confirm the usefulness of this in vitro screening technique.