Representation Of System Research Articles

GrapheNet: a deep learning framework for predicting the physical and electronic properties of nanographenes using images.

In this work we introduce GrapheNet, a deep learning framework based on an Inception-Resnet architecture using image-like encoding of structural features for the prediction of the properties of nanographenes. The model is validated on datasets of computed structure/property data on graphene oxide and defected graphene nanoflakes. By exploiting the planarity of quasi-bidimensional systems and through encoding structures into images, and leveraging the flexibility and power of deep learning in image processing, Graphenet achieves significant accuracy in predicting the physicochemical properties of nanographenes. This approach is able to efficiently encode structures composed of hundreds of atoms, scaling efficiently with the size of the model and enabling the prediction of the properties of large systems, which contrasts with the limitations of current atomistic-level representations for deep learning applications. The approach proposed based on image encoding exhibit a significant numerical accuracy and outperforms the computational efficiency of current representations of materials at the atomistic level, with significant advantages especially in the representation of nanostructures and large planar systems.

Bipolar complex fuzzy near rings

In this article, we devise the novel concept of bipolar complex fuzzy (BCF) near rings (BCFNR), to fill a momentous research gap in connection of bipolar complex fuzzy sets with the theory of near rings in the existing literature. We expand the theory of near ring into the structure of BCF set (BCFS), offering a more suitable approach for the representation of algebraic systems with inherent ambiguity, bipolarity, and 2nd dimension information. Further, we introduce the concept of the bipolar complex fuzzy sub-near ring (BCFSNR), bipolar complex fuzzy left ideal (BCFLI), bipolar complex fuzzy right ideal (BCFRI), and bipolar complex fuzzy ideal (BCFI) in the near ring. After that, we prove the related theorem and results of these devised concepts. We also introduce theorems based on the homomorphism theory and Noetherian theory of near rings within the frame of bipolar complex fuzzy near rings. At the end of the manuscript, we reveal the application of BCFNRs in decision-making (DM) and illustrate it through an example.

KEGG: biological systems database as a model of the real world.

KEGG (https://www.kegg.jp/) is a database resource for representation and analysis of biological systems. Pathway maps are the primary dataset in KEGG representing systemic functions of the cell and the organism in terms of molecular interaction and reaction networks. The KEGG Orthology (KO) system is a mechanism for linking genes and proteins to pathway maps and other molecular networks. Each KO is a generic gene identifier and each pathway map is created as a network of KO nodes. This architecture enables KEGG pathway mapping to uncover systemic features from KO assigned genomes and metagenomes. Additional roles of KOs include characterization of conserved genes and conserved units of genes in organism groups, which can be done by taxonomy mapping. A new tool has been developed for identifying conserved gene orders in chromosomes, in which gene orders are treated as sequences of KOs. Furthermore, a new dataset called VOG (virus ortholog group) is computationally generated from virus proteins and expanded to proteins of cellular organisms, allowing gene orders to be compared as VOG sequences as well. Together with these datasets and analysis tools, new types of pathway maps are being developed to present a global view of biological processes involving multiple organism groups.

Collectivization as a page of history and its place in the national ideology of modern Russia

The article substantiates the necessity of the national ideology of the modern Russian Federation to be based on adequate representations of most important events of the Soviet period of the country’s history. Russian historiography will inevitably form a system of such representations in the near future. The authors confidently position collectivization of the peasant village as the central of the events in question, showing why the Soviet version of collectivization is approximately as far from the historical truth as the ideological interpretation of the 90s, which arrived to substitute the so-called «Marxism-Leninism». Local history investigations and family chronicles contain one of the principal proofs of this, confirming the consistence of Peasant Studies as a tool for historical and sociological research.

Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation.

The periodic number (PN) representation of the chemical systems, introduced by Dmitri Mendeleev, uncovers the fundamental principle of chemical similarity in a straightforward way. In this framework, the rows correspond to the principal quantum numbers of the elements' electronic configurations when considered isolated atoms. This systematic arrangement allows for a deeper understanding of the relationships and patterns among the elements. In this study, we propose a novel strategy for structure type (prototype) prediction by utilizing the PN concept to identify possible modifications and phase stability of unexplored chemical systems. Our PN-based crystal structure prediction (PNcsp) program, which evaluates similarity through PN neighboring in the phase map, provides the most probable prototypes for unknown and unreported modifications of given phases in binary and higher order chemical systems. We applied PNcsp to 59 distinct chemical systems whose equimolar phases are indicated in the respective phase diagrams but lack accurate experimental structure determination. Our methodology identified 93 prototypes for these 59 equiatomic phases, of which 47 exhibit mechanical and dynamic stability. Notably, this approach discovered 19 entirely novel, fully stable polymorphic phases, thereby expanding the known landscape of potential materials. Furthermore, we demonstrated that this method is also effective for nonequimolar and higher order systems.

Traditional Worldviews, Strategic Culture and Revolutionary Mentality: The Case of People’s Republic of China

This article explores China’s ambitions to become a global hegemon in the 21st century. The research traces the origins of these ambitions in China’s strategic culture and system of ideas and representations. Forming a geopolitical study in this way, this article zooms in the concepts of ‘middle’, ‘Middle Kingdom’ and Tianxia. ‘Middle’ symbolises China’s centrality and moral authority; ‘Middle Kingdom’ embodies the idea of a state where political power is concentrated; and Tianxia represents China’s historical visions of the global order. The research results show that China’s traditional worldviews suggest ambitions for a leading status in the international chessboard, which is confirmed by China’s aspects of revolutionary mentality and development of the military power. While the extent to which China will ascend to a leading role remains uncertain, understanding China’s identity—via its strategic culture—is crucial for grasping both China’s ambitious policy and its impact on the world.

Amplification of olfactory transduction currents implements sparse stimulus encoding.

Sensory systems must perform the dual and opposing tasks of being sensitive to weak stimuli while also maintaining information content in dense and variable sensory landscapes. This occurs in the olfactory system, where OSNs are highly sensitive to low concentrations of odors and maintain discriminability in complex odor environments. How olfactory sensory neurons (OSNs) maintain both sensitivity and sparsity is not well understood. Here, we investigated whether the calcium-activated chloride channel, TMEM16B, may support these dual roles in OSNs. We used multiphoton microscopy to image the stimulus-response density of OSNs in the olfactory epithelium. In TMEM16B knockout mice, we found that sensory representations were denser, and the magnitude of OSN responses was increased. Behaviorally, these changes in sensory representations were associated with an increased aversion to the odorant trimethylamine, which switches perceptual valence as its concentration increases, and a decreased efficiency of olfactory-guided navigation. Together, our results indicate that the calcium-activated chloride channel TMEM16B sparsens sensory representations in the peripheral olfactory system and contributes to efficient integrative olfactory-guided behaviors.

Total water level prediction at continental scale: Coastal ocean

We demonstrate recent progress made in the simulation of total water level (TWL) at continental scale, using the coastal ocean of US East Coast/Gulf of Mexico coast as an example. A key difference between the continental-scale and small-scale modeling is that the former requires a more accurate vertical datum. Using a geoid-based datum (xGEOID20b), a satellite altimetry product, and a state-of-the-art 3D unstructured-grid model, we significantly improve the accuracy for TWL both near- and off-shore. The average root-mean-square error at all NOAA stations is 14 cm. The non-tidal signals are found to be sensitive to the representation of a large-scale current system near the boundary and extending the domain extent to accommodate this system improves these signals.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.

QSIM: A Quantum-inspired hierarchical semantic interaction model for text classification

Semantic interaction modeling is a fundamental technology in natural language understanding that guides models to extract deep semantic information from text. Currently, the attention mechanism is one of the most effective techniques in semantic interaction modeling, which learns word-level attention representation by measuring the relevance between different words. However, the attention mechanism is limited to word-level semantic interaction, it cannot meet the needs of fine-grained interactive information for some text classification tasks. In recent years, quantum-inspired language modeling methods have successfully constructed quantized representations of language systems in Hilbert spaces, which use density matrices to achieve fine-grained semantic interaction modeling.This paper presents a Quantum-inspired hierarchical Semantic Interaction Model (QSIM), which follows the sememe-word-sentence language construction principle and utilizes quantum entanglement theory to capture hierarchical semantic interaction information in Hilbert space. Our work builds on the idea of the attention mechanism and extends it. Specifically, we explore the original semantic space from a quantum theory perspective and derive the core semantic space using the Schmidt decomposition technique, where: (1) Sememe is represented as the unit vector in the two-dimensional minimum semantic space; (2) Word is represented as reduced density matrices in the core semantic space, where Schmidt coefficients quantify sememe-level semantic interaction. Compared to density matrices, reduced density matrices capture fine-grained semantic interaction information with lower computational cost; (3) Sentence is represented as quantum superposition states of words, and the degree of word-level semantic interaction is measured using entanglement entropy.To evaluate the model’s performance, we conducted experiments on 15 text classification datasets. The experimental results demonstrate that our model is superior to classical neural network models and traditional quantum-inspired language models. Furthermore, the experiment also confirms two distinct advantages of QISM: (1) flexibility, as it can be integrated into various mainstream neural network text classification architectures; and (2) practicability, as it alleviates the problem of parameter growth inherent in density matrix calculation in quantum language model.

Sharkovskii’s theorem and the limits of digital computers for the simulation of chaotic dynamical systems

Chaos is a unique paradigm in classical physics within which systems exhibit extreme sensitivity to the initial conditions. Thus, they need to be handled using probabilistic methods commonly based on ensembles. However, initial conditions generated by digital computers fall within the sparse set of discrete IEEE floating point numbers which have non-uniform distributions along the real axis. Therefore, there are many missing initial conditions whose absence might be expected to degrade the computed statistical properties of chaotic systems. The universality of this problem is enshrined in Sharkovskii’s theorem which is the simplest mathematical statement of the fact that no finite number representation of a chaotic dynamical system can account for all of its properties and shows that the precision of the representation limits the accuracy of the resulting digital behaviour.

Model-based experiments as epistemic evidence in paleoecology

Where ordinary experiments are impossible and observational data scarce and indirect—particularly in paleoecosystems—computational experiments are often our only means to learn about reality. There are good arguments to count such model-based predictions as evidence, testing hypotheses and updating our beliefs about the world. However, the epistemic weight of computational experiments depends on an adequate model representation of the target system, transparency about predictive uncertainty, and the avoidance of confirmation bias. I argue that mechanistic models are particularly suited for paleoecological predictions but that iterative uncertainty analyses should guide their development. Using a Bayesian framework I propose preregistration and blinded analysis as tools to strengthen the epistemic value of computational experiments. Here, a preregistration marks the boundary between exploratory model development, which establishes credence in the model, and predictive model application, which tests hypotheses. As good modeling practice I suggest clarifying epistemic goals at the outset of a project and accordingly choose methods to maximize the epistemic weight of the computational experiment.

A Multilinear HJB-POD Method for the Optimal Control of PDEs on a Tree Structure

Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging problem. One approach to obtain an optimal feedback control is via the Dynamic Programming principle. Nevertheless, despite many theoretical results, this method has been applied only to very special cases since it suffers from the curse of dimensionality. Our goal is to mitigate this crucial obstruction developing a version of dynamic programming algorithms based on a tree structure and exploiting the compact representation of the dynamical systems based on tensors notations via a model reduction approach. Here, we want to show how this algorithm can be constructed for general nonlinear control problems and to illustrate its performances on a number of challenging numerical tests introducing novel pruning strategies that improve the efficacy of the method. Our numerical results indicate a large decrease in memory requirements, as well as computational time, for the proposed problems. Moreover, we prove the convergence of the algorithm and give some hints on its implementation.

Beyond the root: Geometric characterization for the diagnosis of syndromic heritable thoracic aortic diseases

Syndromic heritable thoracic aortic diseases (sHTAD), such as Marfan (MFS) or Loeys–Dietz (LDS) syndromes, involve high risk of life threatening aortic events. Diagnosis of syndromic features alone is difficult, and negative genetic tests do not necessarily exclude a genetic or hereditary condition. Periodic 3D imaging of the aorta is recommended in patients with aortic disease. Thus, an imaging-based approach aimed at identifying unique features of aortic geometry can be highly effective for diagnosing sHTAD and assessing risk. In this study, we present a method that can help identify the manifestations of sHTAD by focusing on the entire geometry of the thoracic aorta, rather than only using measurements of dilation of the aortic root. We analyze the geometric phenotype of 97 patients with genetically confirmed sHTAD (79 MF and 18 LDS) and of 45 healthy volunteers, using 3D aorta meshes obtained from phase contrast-enhanced magnetic resonance angiograms computed from 4D flow cardiac magnetic resonance. We build a geometric encoding of the aorta, based on a vessel coordinate system, and use several mathematical models to discriminate between controls and patients with sHTAD: a baseline scenario, based on aortic root dimensions only, a descriptor typically used in sHTAD patients; a low dimensional scenario, with a reduce encoding using principal component analysis; and a high-dimensional scenario, which included the full coefficient representation for geometry encoding, aiming to capture finer geometric details. The results indicate that considering the anatomy of the whole thoracic aorta can improve predictive ability. We achieve precision and sensitivity values over 0.8, with a specificity of over 70% in all the models used, while a single value classifiers (based only on aortic root diameter) demonstrated a trade-off between sensitivity and specificity. Using the mathematical properties of the vessel coordinate system representation, feature importance is mapped onto a set of anatomical traits that are used by the models to do the classification, thus providing interpretability of the results. This analysis indicates that in addition to the diameter of the aortic root, aortic elongation and a narrowing of the descending thoracic aorta may be markers of positive sHTAD.

Reduced Markovian models of dynamical systems

Leveraging recent work on data-driven methods for constructing a finite state space Markov process from dynamical systems, we address two problems for obtaining further reduced statistical representations. The first problem is to extract the most salient reduced-order dynamics for a given timescale by using a modified clustering algorithm from network theory. The second problem is to provide an alternative construction for the infinitesimal generator of a Markov process that respects statistical features over a large range of time scales. We demonstrate the methodology on three low-dimensional dynamical systems with stochastic and chaotic dynamics. We then apply the method to two high-dimensional dynamical systems, the Kuramoto-Sivashinky equations and data sampled from fluid-flow experiments via Particle Image Velocimetry. We show that the methodology presented herein provides a robust reduced-order statistical representation of the underlying system.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Learning accurate user and item feature representation for review-based recommender systems

Learning accurate user and item feature representation for review-based recommender systems

The Image of Palestine in the US Press 1919

Press play a crucial role in studying the process of creating the USA’s public perception of the State of Palestine of 1919. Newspapers and magazines enable a researcher to define the distinctive features of the ‘Other’ profile. The reconstructing and explaining of the Palestinian public image from the point of the traditional historical research methods prove insufficient. Being a peculiar phenomenon of American social, political, and cultural life, Palestine’s public conception requires an interdisciplinary examinational approach based on historical imagology. In 1919, there were three models of the State’s future under discussion in the US periodical literature. The first one considered it as two separate countries; the second one – as one Arabic-Jewish country; the third one – as the territory under control of the United Kingdom. Journalists raised questions regarding each of the models evolving representation of the political system in The Middle East: the prospects of the colonial institution; transferring from Colonialism to Postcolonialism; opportunities of applying the experience of Colonial Empires. Studying an informational discourse of the USA in 1919 will allow us to compile a comprehensive list of public images of the State of Palestine and the Middle East (from the territorial as well as political point of view); monitor their evolution, and discover its structural components.

Numerical Study of the Reaction Diffusion Prey–Predator Model Having Holling II Increasing Function in the Predator Under Noisy Environment

The current study deals with the stochastic diffusive prey–predator model with Holling II increasing function in the predator. As it is a population dynamics its population can be affected by various factors such as disease in prey–predator species, environmental fluctuation, etc. So, the underlying model is considered under the impact of a temporal multiplicative noise. The numerical solution of the governing model is obtained by finite difference schemes. To check the accuracy of the numerical solution, the stability and consistency of the schemes in the mean square sense are derived. All the schemes are consistent with the model and von Neumann stable. Several simulations are drawn to check the efficacy of the scheme. A test problem is investigated with numerical schemes and for the different values of the parameters, 3D and 2D plots are drawn. A graphical representation of the given system is drawn which shows the behavior of both species and the efficiency of the novel schemes. Hope so, these results will motivate the researcher to consider the stochastic prey–predator model for a better understanding of the ecosystem.