Number Of Order Parameters Research Articles

Fuzzy Soft Sets and Decision Making in Ideal Nutrition

Issues in daily life, where making the best decisions is crucial, are frequently encountered. But, in the majority of these situations, the best course of action is uncertain. We must take into account a number of parameters in order to find the best possible solution to these difficulties. The best mathematical instrument for this is fuzzy soft set FSS theory in decision making. Nutrition is the process of supplying cells and organisms with the nutrients they need to grow and thrive and to sustain life. A healthy diet has the potential to prevent or mitigate numerous prevalent health issues. The purpose of this paper is to select a burning problem for the nutrition of students and successfully apply the FSS theory in decision making. We aim to prove that the approach to decision-making problems with imprecise data via FSSs is more accurate than other types of approaches, and we present a new approach to the FSS model and its applications in decision-making problems.

Can Neural Quantum States Learn Volume-Law Ground States?

We study whether neural quantum states based on multilayer feed-forward networks can find ground states which exhibit volume-law entanglement entropy. As a testbed, we employ the paradigmatic Sachdev-Ye-Kitaev model. We find that both shallow and deep feed-forward networks require an exponential number of parameters in order to represent the ground state of this model. This demonstrates that sufficiently complicated quantum states, although being physical solutions to relevant models and not pathological cases, can still be difficult to learn to the point of intractability at larger system sizes. Hence, the variational neural network approach offers no benefits over exact diagonalization methods in this case. This highlights the importance of further investigations into the physical properties of quantum states amenable to an efficient neural representation.

Predicting near-term, out-of-sample fish passage, guidance, and movement across diverse river environments by cognitively relating momentary behavioral decisions to multiscale memories of past hydrodynamic experiences

Predicting the behavior of individuals acting under their own motivation is a challenge shared across multiple scientific fields, from economic to ecological systems. In rivers, fish frequently change their orientation even when stimuli are unchanged, which makes understanding and predicting their movement in time-varying environments near built infrastructure particularly challenging. Cognition is central to fish movement, and our lack of understanding is costly in terms of time and resources needed to design and manage water operations infrastructure that is able to meet the multiple needs of human society while preserving valuable living resources. An open question is how best to cognitively account for the multi-modal, -attribute, -alternative, and context-dependent decision-making of fish near infrastructure. Here, we leverage agent- and individual-based modeling techniques to encode a cognitive approach to mechanistic fish movement behavior that operates at the scale in which water operations river infrastructure is engineered and managed. Our cognitive approach to mechanistic behavior modeling uses a Eulerian-Lagrangian-agent method (ELAM) to interpret and quantitatively predict fish movement and passage/entrainment near infrastructure across different and time-varying river conditions. A goal of our methodology is to leverage theory and equations that can provide an interpretable version of animal movement behavior in complex environments that requires a minimal number of parameters in order to facilitate the application to new data in real-world engineering and management design projects. We first describe concepts, theory, and mathematics applicable to animals across aquatic, terrestrial, avian, and subterranean domains. Then, we detail our application to juvenile Pacific salmonids in the Bay-Delta of California. We reproduce observations of salmon movement and passage/entrainment with one field season of measurements, year 2009, using five simulated behavior responses to 3-D hydrodynamics. Then, using the ELAM model calibrated from year 2009 data, we predict the movement and passage/entrainment of salmon for a later field season, year 2014, which included a novel engineered fish guidance boom not present in 2009. Central to the fish behavior model’s performance is the notion that individuals are attuned to more than one hydrodynamic signal and more than one timescale. We find that multi-timescale perception can disentangle multiplex hydrodynamic signals and inform the context-based behavioral choice of a fish. Simulated fish make movement decisions within a rapidly changing environment without global information, knowledge of which direction is downriver/upriver, or path integration. The key hydrodynamic stimuli are water speed, the spatial gradient in water speed, water acceleration, and fish swim bladder pressure. We find that selective tidal stream transport in the Bay-Delta is a superset of the fish-hydrodynamic behavior repertoire that reproduces salmon movement and passage in dam reservoir environments. From a cognitive movement ecology perspective, we describe how a behavior can emerge from a repertoire of multiple fish-hydrodynamic responses that are each tailored to suit the animal’s recent past experience (localized environmental context). From a movement behavior perspective, we describe how different fish swim paths can emerge from the same local hydrodynamic stimuli. Our findings demonstrate that a cognitive approach to mechanistic fish movement behavior modeling does not always require the maximum possible spatiotemporal resolution for representing the river environmental stimuli although there are concomitant tradeoffs in resolving features at different scales. From a water operations perspective, we show that a decision-support tool can successfully operate outside the calibration conditions, which is a necessary attribute for tools informing future engineering design and management actions in a world that will invariably look different than the past.

Ground-state stability and excitation spectrum of a one-dimensional dipolar supersolid

We study the behavior of the excitation spectrum across the quantum phase transition from a superfluid to a supersolid phase of a dipolar Bose gas confined to a one-dimensional geometry. Including the leading beyond-mean-field effects within an effective Hamiltonian, the analysis is based on Bogoliubov theory with several order parameters accounting for the superfluid as well as solid structure. We find fast convergence of the ground-state energy in the supersolid with the number of order parameters and demonstrate a stable excitation spectrum with two Goldstone modes and an amplitude mode in the low-energy regime. Our results suggest that there exists an experimentally achievable parameter regime for dysprosium atoms, where the supersolid phase exhibits a stable excitation spectrum in the thermodynamic limit and the transition into the supersolid phase is of second order driven by the roton instability.

Parametric model of young infants’ eardrum and ear canal impedances supporting immittance measurement results. Part I: Development of the model

Wideband acoustic immitance (WAI) measurements provide an objective means to detect pathological middle ear conditions. However, for ears of young infants, it is still difficult to make clear statements about the middle ear status based on WAI measurements. In order to gain a better understanding of WAI data obtained in young infants’ ears, a parametric electro-acoustic model of the ear canal and the middle ear of young infants is proposed. In this first part of the two-part paper, the development of the model for the healthy ear is presented. Based on an existing model for adult ears, the presented model is adapted to young infants’ ears, uses parameters suited to represent physiological properties, and uses a smaller number of parameters in order to reduce model complexity. A comparison of the acoustic input impedance of the ear predicted by the model with real ear measurements in young infants’ ears showed a good agreement in the main characteristics. Model predictions show that the medium frequency range (about 1–3 kHz) of the acoustic input impedance of the ear is dominated by the properties of the eardrum and the middle ear, indicating that pathological middle ear conditions can preferably be detected in this frequency range.

Labour Productivity as Indicator of Regional Economy Efficiency

The interrelation of labour productivity and economic situation in regions of the Russian Federation is really acute in current conditions, when such destructive factors as sanctions, inflation, instability on global markets due to coronavirus epidemic take place. Therefore, the key goal of the research is to estimate labour productivity in regions as a factor of economy efficiency. Among methods of research it is possible to mention comparative analysis of the indicator, typology of regions by a number of parameters in order to identify regional differentiation. It is assumed that the highest labour productivity is typical of raw-materials regions, which specialize on extraction and export of mineral resources. However, we should take into account that natural and climatic factors in this group of regions increase labour costs and therefore, cut labour productivity. The authors show that in regions with conventional specialization on manufacturing industry (when the raw-materials sector retains a certain importance) the said indicator demonstrates a high value, even in spite of some unfavorable trends on labour market. The authors used the following materials and research findings for preparing the article: results of deep interviews of employment service workers in the Ryazan region conducted in 2021 within the frames of the research ‘Digitalization of Labour Market and Employment in Russia: Trends and Mechanisms of Development’; materials of expert discussions and surveys of employment service heads in regions of the Central Federal District within the frames of the research ‘Organizational and Finance Mechanisms to Support Population Employment in 2021-2023, which Aims at Unemployment Reduction’

City Space Recycling: The Example of Brownfield Redevelopment

In many parts of the world the manufacturing and mining industries have left a huge mark on the spatial structure of entire regions, as well as individual cities. Urban tissue has been growing for years around the dynamically developing industrial plants and mines during the period of intensive industrialization. As a result of industrial restructuring, abandoned and degraded post-industrial areas created problematic places in the structure of cities. Moreover, many of them are located in the direct neighbourhood of the centers of these cities, in the strict urban fabric. Nowadays, in the era of balancing urban development, creating compact, smart and eco cities, one of the important problems faced by city authorities, architects and urban planners is solving the issue of optimal shaping of city structure, including revitalization of post-industrial areas. The article deals with the subject of transforming brownfield areas into modern urban tissue, related to the surrounding areas, and at the same time referring to its industrial heritage. For this purpose, a number of studies were carried out, including comparative analyzes. Therefore, five examples of projects from Europe related to the transformation of brownfield sites for the purposes of industrial and technology parks were selected. This list includes two examples of Polish parks located in Upper Silesia and three examples of areas located in Germany in the Metropolis of the Ruhr Area. Selected areas were analyzed and evaluated in terms of a number of parameters in order to identify the most important factors positively influencing transformations and problem areas requiring the most attention. Based on the results of the case study, a model concept for post-industrial area development was prepared for the purposes of developing an industrial and technological park and recreational areas, complementing the leisure offer for the surrounding residential areas. The location in former Baildon ironworks in Katowice in Upper Silesia was chosen as the place of the conceptual design. Upper Silesia is known as one of the most urbanized and industrialized regions of Poland, which is still struggling with the negative effects of industrial restructuring. Research results indicate that the areas of former mines and production plants, thanks to their strengths, can be attractively transformed into other functions to become again a showcase of the entire city, providing an incentive to learn about the industrial history of the region. The introduction of a diversified program and giving a new character to areas with huge investor potential, helps to return the city to areas of great social, economic and spatial importance.

A longitudinal model for functional connectivity networks using resting-state fMRI

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects' visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex.

Bayesian-Style History Matching: Another Way To Underestimate Forecast Uncertainty?

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 175121, “Bayesian-Style History Matching: Another Way To Underestimate Forecast Uncertainty?” by Jeroen C. Vink, Guohua Gao, and Chaohui Chen, Shell, prepared for the 2015 SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. The paper has not been peer reviewed. Current theoretical formulations of assisted-history-matching (AHM) problems within the Bayesian framework [e.g., ensemble-Kalmanfilter (EnKF) and randomized-maximum- likelihood (RML) problems] are typically based on the assumption that simulation models can reproduce field data accurately within the measurement error. However, this assumption does not hold for AHM problems of real assets. This paper critically investigates the impact of using realistic, inaccurate simulation models. In particular, it demonstrates the risk of underestimating uncertainty when conditioning real-life models to large numbers of field data. Introduction Improved simulation and history-matching techniques have still not cured the chronic ailment of systematically underestimating uncertainty in forecast results. This is because it is very easy to forget or ignore uncertain factors or model assumptions; also, the method used to constrain a model to historical data may excessively reduce uncertainties. In this paper, the authors highlight the fact that stochastic, Bayesian-style history-matching methods can very easily lead to unrealistically reduced uncertainty ranges in forecast results. The complete paper discusses Bayesian-style history matching and uncertainty quantification in detail. Large-Data Danger Stochastic history matching has two contrasting stages. First, one identifies a large number of uncertain model parameters in order to be confident that future field-production results will be contained within the range of forecast results associated with the prior model uncertainty. Second, when actual field data have become available, one uses large quantities of data to reduce this prior uncertainty. The reservoir engineer must decide not only which model parameters and field data to use, but also how many. The effect of increasing the number of uncertain model parameters is intuitively clear: Every parameter that has an impact on a specific forecast result will add to the uncertainty of this result.

Uranium Isotopes Separation Technique

This work describes the atomic laser isotope separation (LIS). The (LIS) plant means calculating the values of a large number of parameters in order to optimize some objective function. Here we use simple model to describe the elementary physical processes: evaporation, vapor expansion, interaction between photons and atoms, ion extraction etc...concentrated on theoretical and empirical bases. An optimization process for the separation of Uranium isotopes is described and discussed.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

Uranium Isotopes Separation Technique

This work describes the atomic laser isotope separation (LIS). The (LIS) plant means calculating the values of a large number of parameters in order to optimize some objective function. Here we use simple model to describe the elementary physical processes: evaporation, vapor expansion, interaction between photons and atoms, ion extraction etc...concentrated on theoretical and empirical bases. An optimization process for the separation of Uranium isotopes is described and discussed.

Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies.

BackgroundThis protocol concerns the assessment of cost-effectiveness of hospital health information technology (HIT) in four hospitals. Two of these hospitals are acquiring ePrescribing systems incorporating extensive decision support, while the other two will implement systems incorporating more basic clinical algorithms. Implementation of an ePrescribing system will have diffuse effects over myriad clinical processes, so the protocol has to deal with a large amount of information collected at various ‘levels’ across the system.Methods/DesignThe method we propose is use of Bayesian ideas as a philosophical guide.Assessment of cost-effectiveness requires a number of parameters in order to measure incremental cost utility or benefit – the effectiveness of the intervention in reducing frequency of preventable adverse events; utilities for these adverse events; costs of HIT systems; and cost consequences of adverse events averted. There is no single end-point that adequately and unproblematically captures the effectiveness of the intervention; we therefore plan to observe changes in error rates and adverse events in four error categories (death, permanent disability, moderate disability, minimal effect). For each category we will elicit and pool subjective probability densities from experts for reductions in adverse events, resulting from deployment of the intervention in a hospital with extensive decision support. The experts will have been briefed with quantitative and qualitative data from the study and external data sources prior to elicitation. Following this, there will be a process of deliberative dialogues so that experts can “re-calibrate” their subjective probability estimates. The consolidated densities assembled from the repeat elicitation exercise will then be used to populate a health economic model, along with salient utilities. The credible limits from these densities can define thresholds for sensitivity analyses.DiscussionThe protocol we present here was designed for evaluation of ePrescribing systems. However, the methodology we propose could be used whenever research cannot provide a direct and unbiased measure of comparative effectiveness.

Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

Summary The goal of the present research is forecasting the inflow of Dez dam reservoir by using Auto Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average (ARIMA) models while increasing the number of parameters in order to increase the forecast accuracy to four parameters and comparing them with the static and dynamic artificial neural networks. In this research, monthly discharges from 1960 to 2007 were used. The statistics related to first 42 years were used to train the models and the 5 past years were used to forecast. In ARMA and ARIMA models, the polynomial was derived respectively with four and six parameters to forecast the inflow. In the artificial neural network, the radial and sigmoid activity functions were used with several different neurons in the hidden layers. By comparing root mean square error (RMSE) and mean bias error (MBE), dynamic artificial neural network model with sigmoid activity function and 17 neurons in the hidden layer was chosen as the best model for forecasting inflow of the Dez dam reservoir. Inflow of the dam reservoir in the 12 past months shows that ARIMA model had a less error compared with the ARMA model. Static and Dynamic autoregressive artificial neural networks with activity sigmoid function can forecast the inflow to the dam reservoirs from the past 60 months.

Functional test of pedotransfer functions to predict water flow and solute transport with the dual-permeability model MACRO

Abstract. Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisations usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = −0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the estimation of water flow parameters. Overall, the results are encouraging for the use of this modelling set-up to estimate pesticide leaching risks at the regional-scale, especially where the objective is to identify vulnerable soils and "source" areas of contamination.

Dynamic mechanical properties of an electrorheological fluid under large-amplitude oscillatory shear strain

In the present study, the shear stress response and the dynamic mechanical properties of an electrorheological fluid are experimentally investigated for small/large shear strain amplitude at moderate range of frequencies and different field intensities. A new efficient constitutive model has also been proposed, which can accurately predict the measured experimental data. Compared with the Fourier transformation rheology, the proposed model requires less number of parameters in order to predict the stress response and the mechanical properties, including storage and loss moduli for different strain amplitudes, frequencies, and field intensities. This leads to simplify the parameter identification in order to predict the material response using the optimization methods.

Parameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow Forecasting

In this study the ability of Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models in forecasting the monthly inflow of Dez dam reservoir located in Teleh Zang station in Dez dam upstream i s estimated. ARIMA model has found a widespread application in many practical sciences. In addition, dam reservoir inflow forecasting is done by some methods such as ordinary linear regression, ARMA and artificial neural networks. On the other hand, application of both ARMA and ARIMA models simultaneously in order to compare their ability in autoregressive forecast of monthly inflow of dam reservoir has not been carried out i n previous researches. Therefore, this paper attempts to forecast the inflow of Dez dam reservoir by using ARMA and ARIMA models while increasing the number of parameters in order to increase the forecast accuracy to four parameters and comparing them. In ARMA and ARIMA models, the polynomial was derived respectively with four and s ix parameters to forecast the inflow. By comparing root mean square error of the model, it was determi ned that ARIMA model can forecast inflow to the Dez reservoir from 12 months ago with lower error t han the ARMA model.

Ability of Box-Jenkins Models to Estimate of Reference Potential Evapotranspiration (A Case Study: Mehrabad Synoptic Station, Tehran, Iran)

The evapotranspiration rate has a vital role in agricultural water management. In this paper, ability of Box-Jenkins models in forecasting the reference potential evapotranspiration is estimated. For this purpose meteorological data of Mehrabad synoptic station in Tehran was selected. Using this data and according to eight famous evapotranspiration equations amounts of evapotranspiration were forecasted by Box-Jenkins models. Equations of reference potential evapotranspiration that used in this study include: FAO Penman Monteith (FPM), FAO Blaney Criddle (FBC), Turc, FAO Radiation Macking (FRM), Priestley Taylor (PT), Hargreaves Samani (HS), Thornthwaite (TW), and Corrected Jensen Haise (CJH). A box-Jenkins model has found a widespread application in many practical sciences. In addition, evapotranspiration forecasting is done by some methods such as remote sensing, genetic algorithm, and artificial neural networks. On the other hand, application of both Box-Jenkins models simultaneously in order to compare their ability in forecast of evapotranspiration has not been carried out in previous researches. Therefore, this paper attempts to forecast the evapotranspiration and meteorological data by using Box-Jenkins models while increasing the number of parameters in order to increase the forecast accuracy to five parameters and comparing them. By comparing root mean square error of the models, it was determined that Box-Jenkins models are appropriate approaches to evapotranspiration forecasting.

Tuning jammed frictionless disk packings from isostatic to hyperstatic

We perform extensive computational studies of two-dimensional static bidisperse disk packings using two distinct packing-generation protocols. The first involves thermally quenching equilibrated liquid configurations to zero temperature over a range of thermal quench rates r and initial packing fractions followed by compression and decompression in small steps to reach packing fractions φ(J) at jamming onset. For the second, we seed the system with initial configurations that promote micro- and macrophase-separated packings followed by compression and decompression to φ(J). Using these protocols, we generate more than 10(4) static packings over a wide range of packing fraction, contact number, and compositional and positional order. We find that disordered, isostatic packings exist over a finite range of packing fractions in the large-system limit. In agreement with previous calculations, the most dilute mechanically stable packings with φ min ≈ 0.84 are obtained for r > r*, where r* is the rate above which φ(J) is insensitive to rate. We further compare the structural and mechanical properties of isostatic versus hyperstatic packings. The structural characterizations include the contact number, several order parameters, and mixing ratios of the large and small particles. We find that the isostatic packings are positionally and compositionally disordered (with only small changes in a number of order parameters), whereas bond-orientational and compositional order increase strongly with contact number for hyperstatic packings. In addition, we calculate the static shear modulus and normal mode frequencies (in the harmonic approximation) of the static packings to understand the extent to which the mechanical properties of disordered, isostatic packings differ from partially ordered packings. We find that the mechanical properties of the packings change continuously as the contact number increases from isostatic to hyperstatic.

Preparation for the alignment of the ALICE inner tracking system

Tracking detectors in high-energy physics experiments require an accurate determination of a large number of alignment parameters in order to allow a precise reconstruction of tracks and vertices. In addition to the initial optical survey, the use of tracks in a special software alignment is essential. The methods in use for the alignment of the ALICE Inner Tracking System are presented, starting from residual-based procedure to fitting systems with many thousands of parameters. The two methods are reviewed and the preliminary results showed.