Representation Of Configuration Research Articles

Comparing COSTATIS and Generalized Procrustes Analysis with Multi-Way Public Education Expenditure Data

Governments serve a variety of purposes, and where governments spend their money has always been of concern to society. In particular, spending on public education is of great interest. However, the volume of this information can be difficult to manage. Therefore, the purpose of this work is to compare the COSTATIS method and generalized Procrustes analysis (GPA) when working with multi-way data. Despite the particular characteristics of each of them, they present similarities and differences that, when analyzed together, can provide complementary results to researchers. The COSTATIS consists of a co-inertia analysis of the compromise of two k-table analyses. The GPA method provides an optimal superimposed representation of individual configurations, and a common consensus configuration is constructed as the mean of all transformed configurations. In addition, the GPA method includes the translation, rotation and scaling of coordinates. In this study, both methods were applied, and the advantages and disadvantages of each are presented. The treated data are a sequence of tables from various countries where different public expenditures on education have been measured over time.

Graph-based configuration optimization for S-CO2 power generation systems

Configuration optimization for energy conversion systems is still under-researched compared with that in the chemical process area. As the basis of design and operation optimization, configuration optimization helps generate more advanced thermodynamic configurations and dramatically promotes the efficiency of thermal systems. However, the variable in configuration optimization involves discrete variables to make optimization more complicate when mixed with continuous design variables. Current configuration optimization research focuses on task-orient methods, superstructure, and heuristic algorithms. These methods tend to screen out candidates based on high levels of knowledge from researchers. In this paper, we first developed a graph-based configuration representation with minimum thermodynamic knowledge requirements. Therefore, the searching space shrinks without losing any possible optimum point. The representation is embedded in a simulated annealing-based optimizer. This optimization framework was validated by optimizing the S-CO2 power generation system's configurations with simple and complex component number limitations. This framework has an over 99% generation rate of feasible configurations. The simple and complex case study results show that the optimization framework converges to a recuperated system with an efficiency of 38% and a recompression system with 44% under maximum and minimum temperature of 850 K and 310 K. Moreover, the result of the simple case study was further justified with an exhaust search. Moreover, both case studies' convergence stabilities are confirmed by multiple and independent framework executions. Finally, the initial configuration's influence shows that the best configuration on lower volume searching space will significantly help with computational efficiency and whole converge process on high volume search space problem.

Topology of the energy landscape of sheared amorphous solids and the irreversibility transition.

Recent experiments and simulations of amorphous solids plastically deformed by an oscillatory drive have found a surprising behavior-for small strain amplitudes the dynamics can be reversible, which is contrary to the usual notion of plasticity as an irreversible form of deformation. This reversibility allows the system to reach limit cycles in which plastic events repeat indefinitely under the oscillatory drive. It was also found that reaching reversible limit cycles can take a large number of driving cycles and it was surmised that the plastic events encountered during the transient period are not encountered again and are thus irreversible. Using a graph representation of the stable configurations of the system and the plastic events connecting them, we show that the notion of reversibility in these systems is more subtle. We find that reversible plastic events are abundant and that a large portion of the plastic events encountered during the transient period are actually reversible in the sense that they can be part of a reversible deformation path. More specifically, we observe that the transition graph can be decomposed into clusters of configurations that are connected by reversible transitions. These clusters are the strongly connected components of the transition graph and their sizes turn out to be power-law distributed. The largest of these are grouped in regions of reversibility, which in turn are confined by regions of irreversibility whose number proliferates at larger strains. Our results provide an explanation for the irreversibility transition-the divergence of the transient period at a critical forcing amplitude. The long transients result from transition between clusters of reversibility in a search for a cluster large enough to contain a limit cycle of a specific amplitude. For large enough amplitudes, the search time becomes very large, since the sizes of the limit cycles become incompatible with the sizes of the regions of reversibility.

Continental Hydrologic Intercomparison Project, Phase 1: A Large‐Scale Hydrologic Model Comparison Over the Continental United States

AbstractHigh‐resolution, coupled, process‐based hydrology models, in which subsurface, land‐surface, and energy budget processes are represented, have been applied at the basin‐scale to ask a wide range of water science questions. Recently, these models have been developed at continental scales with applications in operational flood forecasting, hydrologic prediction, and process representation. As use of large‐scale model configurations increases, it is exceedingly important to have a common method for performance evaluation and validation, particularly given challenges associated with accurately representing large domains. Here, we present phase 1 of a comparison project for continental‐scale, high‐resolution, processed‐based hydrologic models entitled the Continental Hydrologic Intercomparison Project (CHIP). The first phase of CHIP is based on past Earth System Model intercomparisons and comprised of a two‐model proof of concept comparing the ParFlow‐CONUS hydrologic model, version 1.0 and a NOAA US National Water Model configuration of WRF‐Hydro, version 1.2. The objectives of CHIP phase 1 are: (a) describe model physics and components, (b) design an experiment to ensure a fair comparison, and (b) assess simulated streamflow with observations to better understand model bias. To our knowledge, this is the first comparison of continental‐scale, high‐resolution, physics‐based models which incorporate lateral subsurface flow. This model intercomparison is an initial step toward a continued effort to unravel process, parameter, and formulation differences in current large‐scale hydrologic models and to engage the hydrology community in improving hydrology model configuration and process representation.

Form Factor of the Two Fermions Bound State: The Case of Equal Masses and Vector Currentl

New form factor components of two relativistic with equal masses fermions bound state in the case of a vector current are obtained. Consideration is performed within the framework of the relativistic quasipotential approach on the basis of covariant Hamiltonian formulation of quantum field theory by transition to three-dimensional relativistic configurational representation in the case of two relativistic particles with equal masses and spin 1/2.

Videographic conceptual dynamic representation of bicuspid aortic valve anatomic configurations and structural inter-relationships.

Videographic conceptual dynamic representation of bicuspid aortic valve anatomic configurations and structural inter-relationships.

A Critical Review of Spatial Abilities in Down and Williams Syndromes: Not All Space Is Created Equal.

Down syndrome (DS, Trisomy 21) and Williams syndrome (WS) are two neurodevelopmental disorders of genetic origin that are accompanied by mild to moderate intellectual disability but exhibit distinct cognitive profiles. In this review we discuss our recent work characterizing the real-world spatial learning and memory abilities of adult individuals with DS and WS. We used several different paradigms in which participants locomote freely and have access to coherent input from all sensory modalities to investigate their fundamental egocentric (body-centered or viewpoint-dependent) and allocentric (world-centered or viewpoint-independent) spatial abilities. We found unequivocal evidence that most individuals with DS exhibit low-resolution egocentric and allocentric spatial learning and memory abilities similar to typically developing (TD) children in the same mental age range. In contrast, most individuals with DS exhibit impaired high-resolution allocentric spatial learning and facilitated response learning as compared to TD children. In comparison, whereas most individuals with WS also exhibit facilitated response learning, their low-resolution allocentric spatial learning and memory abilities are severely impaired as compared to both TD children and individuals with DS. Together with work from other laboratories using real-world or virtual reality paradigms, these findings indicate that in order to navigate in their environment most individuals with DS may use either egocentric route learning that does not integrate individual landmarks, or a low-resolution allocentric spatial representation that encodes the relationships between different locations (i.e., cognitive mapping). In contrast, since most individuals with WS are unable to build or use a low-resolution allocentric or configural representation of the environment they may use visually and verbally encoded landmarks as beacons to learn routes. Finally, we discuss the main neural structures implicated in these different spatial processes and explain how the relative preservation or impairment of specific brain functions may engender the unique cognitive profiles observed in individuals with these neurodevelopmental disorders.

A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The proposed approach improves picture consistency on a newborn's face. Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition. As a paradigm GCNMF is suggested for real-world newborn face recognition. Convolutional, pooling, and fully-connected layers, as well as a Neuro Fuzzy layer, form the Inherited Convolutional Neuro Multi-Fuzzy. The model employs hereditary, convolutional neural networks, and fuzzy logic to deal with ambiguity and imprecision in the input configuration representation. The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.

Wake and Performance Predictions of Two- and Three-Bladed Wind Turbines Based on the Actuator Line Model1

Abstract This paper challenges the standard wind turbine design numerically assessing the wake and aerodynamic performance of two- and three-bladed wind turbine models implementing downwind and upwind rotor configurations, respectively. The simulations are conducted using the actuator line model (ALM) coupled with a three-dimensional Navier Stokes solver implementing the k−ω shear stress transport turbulence model. The sensitivity of the ALM to multiple simulation parameters is analyzed in detail and numerical results are compared against experimental data. These analyses highlight the most suitable Gaussian radius at the rotor to be equal to twice the chord length at 95% of the blade for a tip-speed ratio (TSR) of ten, while the Gaussian radius at the tower and the number of actuator points have a low incidence on the flow field computations overall. The numerical axial velocity profiles show better agreement upstream than downstream the rotor, while the discrepancies are not consistent through all the assessed operating conditions, thus highlighting that the ALM parameters are also dependent on the wind turbine's operating conditions rather than being merely geometric parameters. Particularly, for the upwind three-bladed wind turbine model, the accuracy of the total thrust computations improves as the TSR increases, while the least accurate wake predictions are found for its design TSR. Finally, when comparing both turbine models, an accurate representation of the downwind configuration is observed as well as realistic power extraction estimates. Indeed, the results confirm that rotors with fewer blades are more suitable to operate at high TSRs.

Multisensory integration supports configural learning of a home refuge in the whip spider Phrynus marginemaculatus.

Whip spiders (Amblypygi) reside in structurally complex habitats and are nocturnally active yet display notable navigational abilities. From the theory that uncertainty in sensory inputs should promote multisensory representations to guide behavior, we hypothesized that their navigation is supported by a multisensory and perhaps configural representation of navigational inputs, an ability documented in a few insects and never reported in arachnids. We trained Phrynus marginemaculatus to recognize a home shelter characterized by both discriminative olfactory and tactile stimuli. In tests, subjects readily discriminated between shelters based on the paired stimuli. However, subjects failed to recognize the shelter in tests with either of the component stimuli alone. This result is consistent with the hypothesis that the terminal phase of their navigational behavior, shelter recognition, can be supported by the integration of multisensory stimuli as an enduring, configural representation. We hypothesize that multisensory learning occurs in the whip spiders' extraordinarily large mushroom bodies, which may functionally resemble the hippocampus of vertebrates.

Flexible ferroelectric wearable devices for medical applications.

SummaryWearable electronics are becoming increasingly important for medical applications as they have revolutionized the way physiological parameters are monitored. Ferroelectric materials show spontaneous polarization below the Curie temperature, which changes with electric field, temperature, and mechanical deformation. Therefore, they have been widely used in sensor and actuator applications. In addition, these materials can be used for conversion of human-body energy into electricity for powering wearable electronics. In this paper, we review the recent advances in flexible ferroelectric materials for wearable human energy harvesting and sensing. To meet the performance requirements for medical applications, the most suitable materials and manufacturing techniques are reviewed. The approaches used to enhance performance and achieve long-term sustainability and multi-functionality by integrating other active sensing mechanisms (e.g. triboelectric and piezoresistive effects) are discussed. Data processing and transmission as well as the contribution of wearable piezoelectric devices in early disease detection and monitoring vital signs are reviewed.

Accelerating Heterogeneous Multiscale Simulations of Advanced Materials Properties with Graph‐Based Clustering

AbstractHeterogeneous multiscale methods (HMM) capable of simulating asynchronously multiple scales concurrently are now tractable with the advent of exascale supercomputers. However, naive implementations display a large number of redundancies and are very costly. The macroscale model typically requires computations of a large number of very similar microscale simulations. In hierarchical methods, this is barely an issue as phenomenological constitutive models are inexpensive. However, when microscale simulations require, for example, high‐dimensional molecular dynamics (MD) or finite element (FE) simulations, redundancy must be avoided. A clustering algorithm suited for HMM workflows is proposed that automatically sorts and eliminates redundant microscale simulations. The algorithm features a combination of splines to render a low‐dimension representation of the parameter configurations of microscale simulations and a graph network representation based on their similarity. The algorithm enables the clustering of similar parameter configurations into a single one in order to reduce to a minimum the number of microscale simulations required. An implementation of the algorithm in the context of an HMM application coupling FE and MD to predict the chemically specific mechanical behavior of polymer‐graphene nanocomposites. The algorithm furnishes a threefold reduction of the computational effort with limited loss of accuracy.

On Threshold Resummation S-Factor for a System of Two Relativistic Spinor Particles with Arbitrary Masses

We present a new threshold resummation S-factor obtained for a composite system of two relativistic spin 1/2 particles of arbitrary masses interacting via a Coulomb-like chromodynamical potential. The analysis is performed in the framework of a relativistic quasipotential approach in the Hamiltonian formulation of the quantum field theory in the relativistic configuration representation. The pseudoscalar, vector, and pseudovector systems are considered. The difference in the behavior of the S-factor for these cases is discussed. A connection between the new and the previously obtained S-factors for spinless particles of arbitrary masses and relativistic spinor particles of equal masses is established.

Aging and the encoding of changes in events: The role of neural activity pattern reinstatement

When encountering unexpected event changes, memories of relevant past experiences must be updated to form new representations. Current models of memory updating propose that people must first generate memory-based predictions to detect and register that features of the environment have changed, then encode the new event features and integrate them with relevant memories of past experiences to form configural memory representations. Each of these steps may be impaired in older adults. Using functional MRI, we investigated these mechanisms in healthy young and older adults. In the scanner, participants first watched a movie depicting everyday activities in a day of an actor’s life. They next watched a second nearly identical movie in which some scenes ended differently. Crucially, before watching the last part of each activity, the second movie stopped, and participants were asked to mentally replay how the activity previously ended. Three days later, participants were asked to recall the activities. Neural activity pattern reinstatement in medial temporal lobe (MTL) during the replay phase of the second movie was associated with detecting changes and with better memory for the original activity features. Reinstatements in posterior medial cortex (PMC) additionally predicted better memory for changed features. Compared to young adults, older adults showed a reduced ability to detect and remember changes and weaker associations between reinstatement and memory performance. These findings suggest that PMC and MTL contribute to change processing by reinstating previous event features, and that older adults are less able to use reinstatement to update memory for changed features.

Comprehensive hydro-chemistry and geothermal water quality of Konkan, Maharashtra, India for sustainable industrial development

Geothermal fluids are potentially significant sources of valuable minerals, metals and can be utilized in industrial plants according to their practicality. The aim of this paper is to hydro-chemically evaluate the geothermal water which is being produced from eleven geothermal wells of Maharashtra, India. Water samples are grouped in facies by piper's trilinear plot, Stiff's polygonal diagram, Gibb's Plot and Durovpolt. Na+, Ca2+, Cl− and HCO3- are the most important ion in the water, which will affect the geochemistry of ground water. These ions are depositing in water after weathering of certain rocks like Mg2+ is depositing in water after the weathering of basic rock such as basalt which are ferromagnesium rich. The plots in present study will provide the visual representation of water ion configuration and in which regime they are lying like under evaporates, weathering or precipitation. Various Mechanical and Chemical operations, Cleaning, Fabrication, diluting of materials, chilling; Subsuming water to product; or sanitation purpose in Industry premises are major key units in Industries where water is inevitable. The water quality must be monitored to prevent pipe corrosion in utilities. For indicating the extent to which water flowing through pipes will precipitate or dissolve calcium carbonate, important factors such as Saturation Indices are used to test the quality of water. The present study shows that water of wells can be utilize with some additional processing of water through which the key parameter like Corrosion, TDS and temperature can be altered to make it compatible with certain equipment in Industries.

Fruit flies can learn non-elemental olfactory discriminations.

Associative learning allows animals to establish links between stimuli based on their concomitance. In the case of Pavlovian conditioning, a single stimulus A (the conditional stimulus, CS) is reinforced unambiguously with an unconditional stimulus (US) eliciting an innate response. This conditioning constitutes an ‘elemental’ association to elicit a learnt response from A+ without US presentation after learning. However, associative learning may involve a ‘complex’ CS composed of several components. In that case, the compound may predict a different outcome than the components taken separately, leading to ambiguity and requiring the animal to perform so-called non-elemental discrimination. Here, we focus on such a non-elemental task, the negative patterning (NP) problem, and provide the first evidence of NP solving in Drosophila. We show that Drosophila learn to discriminate a simple component (A or B) associated with electric shocks (+) from an odour mixture composed either partly (called ‘feature-negative discrimination’ A+ versus AB−) or entirely (called ‘NP’ A+B+ versus AB−) of the shock-associated components. Furthermore, we show that conditioning repetition results in a transition from an elemental to a configural representation of the mixture required to solve the NP task, highlighting the cognitive flexibility of Drosophila.

On simulation of the photon wave function to explain the Young’s experiment and prospects for its use in quantum cryptography

The method developed in previous works for building the photon wave function in configuration representation is briefly described. The simulation of such function (wave packet) appropriate to femtosecond laser radiation, using the distribution of Gauss of photon momenta in the state of a single laser impulse is expounded. By other appropriate simulated wave function of photon the light interference in Young’s experiment is explained within the frame of quantum mechanics without the attraction of transition probability amplitudes used in approach of quantum electrodynamics. This allows us considering interference phenomena for photons and particles from the same basis of the quantum-mechanical principle of superposition employed to the wave function built in the configuration space. Using the building apparatus of photon quantum mechanics, the Malus law postulated in quantum theory is justified. The relevance of new experiments to study the interaction of quantum objects between themselves and the physical vacuum, which would reveal the origins of the mathematical description of quantum states using the wave function of these quantum objects, is substantiated. It is suggested that the ideas of constructing the wave function of a free photon in the coordinate representation can find relevance in the field of quantum cryptography.

Algorithm for generating irreducible site-occupancy configurations

Generating irreducible site-occupancy configurations by taking advantage of crystal symmetry is a ubiquitous method for accelerating of disordered structure prediction, which plays an important role in condensed matter physics and material science. Here, we present a new algorithm for generating irreducible site-occupancy configurations, that works for arbitrary parent cell with any supercell expansion matrix, and for any number of atom types with arbitrary stoichiometry. The new algorithm identifies the symmetrically equivalent configurations by searching the space group operations of underlying lattice and building the equivalent atomic matrix based on it. Importantly, an integer representation of configurations can greatly accelerate the speed of elimination of duplicate configurations, resulting into a linear scale of run time with the number of irreducible configurations that finally found. Moreover, based on our new algorithm, we write the corresponding code named as disorder in FORTRAN programming language, and the performance test results show that the time efficiency of our disorder code is superior to that of other related codes (supercell, enumlib and SOD).

Data-Driven Critical Tract Variable Determination for European Portuguese

Technologies, such as real-time magnetic resonance (RT-MRI), can provide valuable information to evolve our understanding of the static and dynamic aspects of speech by contributing to the determination of which articulators are essential (critical) in producing specific sounds and how (gestures). While a visual analysis and comparison of imaging data or vocal tract profiles can already provide relevant findings, the sheer amount of available data demands and can strongly profit from unsupervised data-driven approaches. Recent work, in this regard, has asserted the possibility of determining critical articulators from RT-MRI data by considering a representation of vocal tract configurations based on landmarks placed on the tongue, lips, and velum, yielding meaningful results for European Portuguese (EP). Advancing this previous work to obtain a characterization of EP sounds grounded on Articulatory Phonology, important to explore critical gestures and advance, for example, articulatory speech synthesis, entails the consideration of a novel set of tract variables. To this end, this article explores critical variable determination considering a vocal tract representation aligned with Articulatory Phonology and the Task Dynamics framework. The overall results, obtained considering data for three EP speakers, show the applicability of this approach and are consistent with existing descriptions of EP sounds.

Predicate model of process knowledge when detecting and recognizing extended objects such as clouds, angel-echoes in surveillance radars

A predicate model of the process knowledge of inter-period processing of radar signals in the detection and recognition of extended objects and a decision-making method based on precedents have been developed. The main features and structural elements of the process knowledge model are presented. It is shown that the advantages of this model are related to the configuration and hierarchical representation of the process for studying the possible structures of single or groups of impulse signals within the same radar field of view based on the intelligent analysis of signals using the algebra of finite predicates. It is shown how this approach can be used to automate the process of detecting and recognizing extended objects such as clouds, atmospheric inhomogeneities of the angel-echo type. A method for processing process knowledge has been developed as a tool for creating universal algorithms for inter-period processing of signal information to ensure effective detection and recognition of various extended objects, including atmospheric inhomogeneities of the angel-echo type, by accumulating both signal (energy) and logical information in the analyzed cell and in its vicinity. The developed technology includes procedures for formalizing and analyzing the symbolic model of observed objects for making decisions based on precedents. Depending on the types of connections used in the model, classifying and functional networks are distinguished, where some elements of logical and network models are used. The idea of inference rules or decision rules is borrowed from logical models, and the description of knowledge in the form of a semantic neural network is borrowed from network models. In this combined model, procedural information is clearly highlighted. Instead of a logical conclusion, a conclusion or a decisive rule on knowledge appears. As a result of solving the system of predicate equations of process knowledge, we find the place, geometric dimensions and type of the symbolic model of an extended object.