Representation Of System Research Articles

The role of language in transcending core knowledge.

What Babies Know (WBK) argues that core knowledge has a unique place in cognitive architecture, between fully perceptual and fully conceptual systems of representation. Here I argue that WBK's core knowledge is on the perception side of the perception/cognition divide. I discuss some implications of this conclusion for the roles language learning might play in transcending core knowledge.

A Stochastic Landscape Approach for Protein Folding State Classification.

Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer's and Parkinson's. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.

The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering

The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering

Sensitivity analysis of commutation failure in multi-infeed HVDC systems: Exploring the impact of ac system representations and fault types

This paper investigates commutation failure (CF) in line commutated HVdc converters within multi-infeed HVDC systems and thereby provides valuable insights for the operation and design of HVdc systems. CF susceptibility across various operational scenarios, fault types, and ac system representations at different frequencies is explored. An Electromagnetic Transients (EMT) model is constructed and parameter changes are applied using Monte-Carlo Simulation. The process is fully automated with the use of a controlling program written in Python, which varies parameter values and facilitates result retrieval and post-simulation analysis. A typical analysis results in several hundred thousands of simulation runs, requiring the procedure to be implemented on a parallel computing platform. The results show that two-phase faults are the most critical CF triggers, an aspect often overlooked in previous literature focused on three-phase or single-phase-to-ground faults. Furthermore, the frequency dependent characteristics of the ac network can result in very different CF susceptibilities.

Water quality and surroundings for systemic management of springs in rural areas

Springs enable the passage of groundwater to flow to the ground surface, forming and sustaining watercourses. Despite the importance of these ecosystems, their functionality and the availability of water in terms of quantity and quality in rural areas have been threatened. The objective of this study was to evaluate the water quality of springs under different care and preservation scenarios in rural areas and assess the need to combine water potability analysis with biological indicators and the effects of the interaction between aquatic and terrestrial environments on the characteristics of surroundings to generate a systemic and robust representation of the impact of human actions on these threatened environments. The study was developed considering the Ijuí River Basin, in the northwestern state of Rio Grande do Sul, Brazil. It involved the evaluation of springs in a native forest area protected by drainage and a preserved vegetation; in a reforested area with concrete protection and surrounding vegetation; and in a livestock area with no vegetation fragments in the surroundings and with an outcrop protected by concrete pipes. Physicochemical and microbiological analyses are essential for determining the potability of spring water. However, these are specific assessments that do not reflect historical environmental conditions. The use of bioindicators enables these analyses and, together with the determination of surroundings' characteristics, allows for evaluating the degree of preservation to ensure spring quality, providing perspectives that combine care and sustainable use.

Sistem Proporsional Terbuka Bukan Akar Politik Uang Dalam Pemilu

The influence of an open proportional representation system with a majority vote does not explain the rise of money politics and political corruption after the election period. Systemically, the influence of the electoral system is aimed more at establishing a stable party system and modeling coalition models to produce a model of political balance in managing relations between the Government and Parliament. Meanwhile, the quality of the implementation of the electoral system is actually determined by the quality of the institutionalization of political parties, including the tendency of the type of party. Money politics is the result of the poor quality of political institutions in a type of party that only focuses on electoral interests.

Artificial Intelligence in Modeling and Simulation

Modeling and simulation (M&S) serve as essential tools in various scientific and engineering domains, enabling the representation of complex systems and processes without the constraints of physical experimentation [...]

A Study on the Representation of Ancient Marriage System in Chinese Characters from a Diachronic Perspective

Chinese characters have endured through the ages, containing rich cultural connotations. This paper takes the diachronic changes in the marriage system as its basis and reveals how the ancient marriage system is reflected in Chinese characters through in-depth analysis of the origin and evolution of Chinese characters, providing a new perspective for understanding ancient Chinese society and culture.

Key drivers of vulnerability to rainfall flooding in New Orleans

IntroductionFuture urban stormwater flood risk is determined by the confluence of both climate-driven changes in precipitation patterns and the effectiveness of flood mitigation systems, such as urban drainage and pump systems. This is especially true in coastal cities protected by levee systems like New Orleans, where even present-day rainfall would be enough to cause serious flooding in the absence of extensive stormwater drainage and pumping. However, while the uncertainties associated with climate change have been well studied, uncertainties in infrastructure performance and operation have received less attention.MethodsWe investigated how these interrelated sets of uncertainties drive flood risk in New Orleans using a Robust Decision Making (RDM) approach. RDM is a framework for Decision Making Under Deep Uncertainty (DMDU) that leverages simulation models to facilitate exploration across many possible futures and the identification of decision-relevant scenarios. For our work, we leveraged a detailed Storm Water Management Model (SWMM) representation of the New Orleans urban stormwater management system to examine flood depths across the city when faced with different levels of future precipitation, sea-level rise, drainage pipe obstruction, and pumping system failure. We also estimated direct flood damage for each neighborhood in the city for this scenario ensemble. These damage estimates were then subjected to vulnerability analysis using scenario discovery—a technique designed to determine which combinations of uncertainties are most stressful to the system in terms of an outcome of interest (excess flood damage).ResultsOur results suggest that key drivers of vulnerability depend on geographic scale. Specifically, we find that possible climate-driven precipitation increases are the most important determinant of vulnerability at the citywide level. However, for some individual neighborhoods, infrastructure operation challenges under present day conditions are a more significant driver of vulnerability than possible climate-driven precipitation increases.

EXCOGITO, an Extensible Coarse-Graining Toolbox for the Investigation of Biomolecules by Means of Low-Resolution Representations.

Bottom-up coarse-grained (CG) models proved to be essential to complement and sometimes even replace all-atom representations of soft matter systems and biological macromolecules. The development of low-resolution models takes the moves from the reduction of the degrees of freedom employed, that is, the definition of a mapping between a system's high-resolution description and its simplified counterpart. Even in the absence of an explicit parametrization and simulation of a CG model, the observation of the atomistic system in simpler terms can be informative: this idea is leveraged by the mapping entropy, a measure of the information loss inherent to the process of coarsening. Mapping entropy lies at the heart of the extensible coarse-graining toolbox, EXCOGITO, developed to perform a number of operations and analyses on molecular systems pivoting around the properties of mappings. EXCOGITO can process an all-atom trajectory to compute the mapping entropy, identify the mapping that minimizes it, and establish quantitative relations between a low-resolution representation and the geometrical, structural, and energetic features of the system. Here, the software, which is available free of charge under an open-source license, is presented and showcased to introduce potential users to its capabilities and usage.

How Does the Use of Artificial Intelligence Reflect on Business Administration and Management? A Perspective on Knowledge Production at the Postgraduate Level

Objectives: The extensive adoption of AI applications in many industries raises concerns over their potential to significantly transform employment in various aspects, including job creation, automation, and decision-making procedures. The impact that AI has on employment dynamics presents an array of obstacles and opportunities for the fields of business administration and management. Given all these advancements, the present study conducts a thorough examination of postgraduate theses, recognizing them as a systematic representation of the expanding influence of AI in the fields of business administration and management. Postgraduate research, which entails a thorough academic investigation, provides a crucial perspective for understanding the present condition and future course of AI use in business environments. Through an in-depth examination of postgraduate theses, this research attempts to reveal trends in the application of AI technologies in various organizational contexts. Design/methodology/approach: The 73 master's and doctoral theses used in this study were obtained from the repository of the Council of Higher Education National Thesis Center. The sample spans a range of institutions, demonstrating the diversity and depth of AI research across the academic landscape of management studies. To comprehensively examine and understand the information categories encompassed in these theses, the research utilizes two techniques: document analysis and descriptive content analysis. Document analysis systematically examines the theses as data sources, enabling a thorough investigation of the material that focuses on the identification, assessment, and synthesis of information pertaining to AI in business administration and management. Descriptive content analysis classifies and facilitates a methodical examination of the data. The distribution of theses subjected to document analysis was based on thesis type, universities and sub-disciplines, publication year and language, sample characteristics, methodology, theories, AI application areas, and keywords. Results: The findings show that studies on AI have gained momentum in the last four years, with a high percentage of theses focusing on management, organization, and marketing. The quantitative research method has been the most preferred for postgraduate theses. Additionally, human resource management, machine learning, and artificial neural networks constitute most of the research focus. The data indicates that most master's theses concentrate on human resource management and marketing. The finance and information technology sectors are predominant in terms of industry focus. Practical implications: The findings have the potential to significantly assist practitioners in understanding the ongoing research on AI, enabling them to align their strategic planning with the latest discoveries and approaches in higher education. The prevalence of machine learning and artificial neural networks signifies an inclination towards increasingly complex AI implementations within organizations. Organizations may leverage this understanding to increase innovation, enhance decision-making procedures, and sustain a competitive advantage through the implementation of cutting-edge AI. Furthermore, this study can assist researchers, such as master's and doctoral students, with the topic, research question, data source, data collection tool, and analysis type selection in future studies. Originality/value: The study is expected to provide insights into research focus, scope, and methodology for future research by revealing the current state of AI research in business administration and management domains in Turkey. Through the examination of postgraduate theses, this study not only highlights the increasing academic attention towards AI but also establishes a foundation upon which subsequent research can be built. This study represents an initial effort to gain deeper insights into AI research in Turkey, specifically focusing on knowledge production within universities.

Neural network quaternion-based controller for port-Hamiltonian system

Abstract In this research article, a control approach for port-Hamiltonian PH systems based in a neural network (NN) quaternion-based control strategy is presented. First, the dynamics is converted by the implementation of a Poisson bracket in order to facilitate the mathematical model in order to obtain a feasible formulation for the controller design based on quaternion NNs. In this study, two controllers for this kind of of system are presented: the first one consists in the controller design for a PH system about its equilibrium points taking into consideration the position and momentum. This mean is achieved by dividing the quaternion neural controller into scalar and vectorial parts to facilitate the controller derivation by selecting a Lyapunov functional. The second control strategy consists in designing the trajectory tracking controller, in which a reference moment is considered in order to drive this variable to the final desired position according to a reference variable; again, a Lyapunov functional is implemented to obtain the desired control law. It is important to mention that both controllers take into advantage that the energy consideration and that the representation of many physical systems could be implemented in quaternions. Besides the angular velocity, trajectory tracking of a three-phase induction motor is presented as a third numerical experiment. Two numerical experiments are presented to validate the theoretical results evinced in this study. Finally, a discussion and conclusion section is provided.

Rural Priest in the System of Social Representations of the Russian Peasantry in the XIX – early XX centuries (on the Example of Penza Province)

Rural Priest in the System of Social Representations of the Russian Peasantry in the XIX – early XX centuries (on the Example of Penza Province)

Increasing the Performance of a Multi-Agent Knowledge Representation and Processing System

Increasing the Performance of a Multi-Agent Knowledge Representation and Processing System

A new government for New Zealand: Scope to densify, diversify and deliver?

New Zealand confirmed a new government on 24th November, 2023, marking a shift to the right in terms of the political principles which underpin the nation’s governance. The new government is the country’s first three-party coalition since it switched to a mixed-member proportional representation (MMP) system in 1996, following a referendum in 1993.1 It comprises a union between the centre-right National Party, which secured 38 per cent of the votes, alongside the libertarian ACT and populist NZ First parties, which secured 8 per cent and 6 per cent respectively.2 Coalition agreements have been signed but contain little specific detail on housing policy and the new government’s position remains to be clarified. Each constituent party went into the election claiming that more needed to be done, and the National Party, which secured pole position, campaigned on the basis that ‘[e]nding New Zealand’s housing shortage is critical for New Zealand’s future’.3 This paper sets out the circumstances which have contributed to the country’s housing affordability challenges and some of their wider impacts, alongside context on the coalition partners’ statements to date and a series of propositions that might help to address the shortfall in provision – being that the country must focus on how to densify its cities and diversify its housing stock as well as deliver new homes, with three suggested policy areas that may align with the thinking of the new government.

A quantum inspired approach to learning dynamical laws from data—block-sparsity and gauge-mediated weight sharing

Recent years have witnessed an increased interest in recovering dynamical laws of complex systems in a largely data-driven fashion under meaningful hypotheses. In this work, we propose a scalable and numerically robust method for this task, utilizing efficient block-sparse tensor train representations of dynamical laws, inspired by similar approaches in quantum many-body systems. Low-rank tensor train representations have been previously derived for dynamical laws of one-dimensional systems. We extend this result to efficient representations of systems with K-mode interactions and controlled approximations of systems with decaying interactions. We further argue that natural structure assumptions on dynamical laws, such as bounded polynomial degrees, can be exploited in the form of block-sparse support patterns of tensor-train cores. Additional structural similarities between interactions of certain modes can be accounted for by weight sharing within the ansatz. To make use of these structure assumptions, we propose a novel optimization algorithm, block-sparsity restricted alternating least squares with gauge-mediated weight sharing. The algorithm is inspired by similar notions in machine learning and achieves a significant improvement in performance over previous approaches. We demonstrate the performance of the method numerically on three one-dimensional systems—the Fermi–Pasta–Ulam–Tsingou system, rotating magnetic dipoles and point particles interacting via modified Lennard–Jones potentials, observing a highly accurate and noise-robust recovery.

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

AbstractWe assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models.

The Imagery of Death in the Visual Discourse of French Illumination of the 13th-15th Centuries: Iconographic Typologies and Artistic Practice

Classical historical and art studies in the context of the development of modern humanities are frequently synthesised with research on visual culture and social art history, forming a broad discourse on the study of the culture of a particular era, country, or community. This optics is taken as the basis for this study, wherein such a fundamental philosophical concept as the attitude towards death is considered from the standpoint of art history iconographic and cultural studies to identify worldviews and ideology in the development of visual narratives that cover and represent them. In this dimension, illuminated manuscripts are the research subject and object where, through the synthesis of text and image, the ability to semiotically read images and decode the symbolic content of cultural categories opens up wide interpretative possibilities for the researcher. The purpose of this study is to outline the wide variability of iconographic typologies associated with the theme of death in the late medieval European culture of the Latin West and to cover the features of the connection of visual culture with the system of worldview representations based on symbolism rooted in religion, folklore, and court aristocratic culture. The proposed study uses an interdisciplinary scientific approach and an art history approach. The research is based on the methods of stylistic and formal analysis, semiotics, and hermeneutics. The typological approach allowed identifying groups of images united by identical semantics of image interpretation, joint compositional and plot solutions designed to accessibly decode their semantics. First, the ontological and metaphysical foundations of the perception of death as a phenomenon in the philosophical and anthropological dimension in culture in general, and in western European culture in particular, are defined and characterised. In the context of the analysis of the features of the symbolic and iconographic interpretation of images of death in the visual culture of illumination of the 13th-15th centuries, groups of images are identified, united by identical semantics of interpretation of these images, joint compositional and plot solutions designed to accessibly decode their semantics, namely the imagery of the death of saints and clergy, imagery of the death of monarchs, the death of soldiers, noble chivalry, the death of fallen in battle, the death of women, imagery of suicides, as well as imagery of violent death, murder. This study covers a wide range of potential recipients in modern humanitarianism and can be useful for historians, medievalists, art historians, anthropologists, and cultural scientists.

Recurrence plot embeddings as short segment nonlinear features for multimodal speaker identification using air, bone and throat microphones

Speech is produced by a nonlinear, dynamical Vocal Tract (VT) system, and is transmitted through multiple (air, bone and skin conduction) modes, as captured by the air, bone and throat microphones respectively. Speaker specific characteristics that capture this nonlinearity are rarely used as stand-alone features for speaker modeling, and at best have been used in tandem with well known linear spectral features to produce tangible results. This paper proposes Recurrent Plot (RP) embeddings as stand-alone, non-linear speaker-discriminating features. Two datasets, the continuous multimodal TIMIT speech corpus and the consonant-vowel unimodal syllable dataset, are used in this study for conducting closed-set speaker identification experiments. Experiments with unimodal speaker recognition systems show that RP embeddings capture the nonlinear dynamics of the VT system which are unique to every speaker, in all the modes of speech. The Air (A), Bone (B) and Throat (T) microphone systems, trained purely on RP embeddings perform with an accuracy of 95.81%, 98.18% and 99.74%, respectively. Experiments using the joint feature space of combined RP embeddings for bimodal (A–T, A–B, B–T) and trimodal (A–B–T) systems show that the best trimodal system (99.84% accuracy) performs on par with trimodal systems using spectrogram (99.45%) and MFCC (99.98%). The 98.84% performance of the B–T bimodal system shows the efficacy of a speaker recognition system based entirely on alternate (bone and throat) speech, in the absence of the standard (air) speech. The results underscore the significance of the RP embedding, as a nonlinear feature representation of the dynamical VT system that can act independently for speaker recognition. It is envisaged that speech recognition too will benefit from this nonlinear feature.

Digital Representation Systems for Canonical Structures In the Plane

Digital Representation Systems for Canonical Structures In the Plane