Unknown Quantities Research Articles

Bayesian mean–variance analysis: optimal portfolio selection under parameter uncertainty

The paper solves the problem of optimal portfolio choice when the parameters of the asset returns distribution, for example the mean vector and the covariance matrix, are unknown and have to be estimated by using historical data on asset returns. Our new approach employs the Bayesian posterior predictive distribution which is the distribution of future realizations of asset returns given the observable sample. The parameters of posterior predictive distributions are functions of the observed data values and, consequently, the solution of the optimization problem is expressed in terms of data only and does not depend on unknown quantities. By contrast, the optimization problem of the traditional approach is based on unknown quantities which are estimated in the second step, and lead to a suboptimal solution. We also derive a very useful stochastic representation of the posterior predictive distribution whose application not only gives the solution of the considered optimization problem, but also provides the posterior predictive distribution of the optimal portfolio return which can be used to construct a prediction interval. A Bayesian efficient frontier, the set of optimal portfolios obtained by employing the posterior predictive distribution, is constructed as well. Theoretically and using real data we show that the Bayesian efficient frontier outperforms the sample efficient frontier, a common estimator of the set of optimal portfolios which is known to be overoptimistic.

Attempt of Costus arbuscular mycorrhizal inoculants association with leguminous foliage whey on chlorophyll of sunflower (Helianthus annus L.) plants

Reporters founded that mycorrhizal association in plant kingdom improves the plant growth. The present attempt has been on the use of Mycorrhiza for Helianthus growth in stress condition of the adverse red soil of konkan region, India and other climatic factors. Earlier reporters for the purpose to avoid chemical fertilisers, founded that Deproteinised juice (DPJ) or foliage whey induces appropriate plant growth and enhance soil fertility. During present investigation, mycorrhiza from Costus sp application to soil of potted sunflower plants influence was compared with organic DPJ addition obtained from fenugreek foliage during the process of green crop fractionation (GCF) technique. This whey is randomly disposed during this technology of leaf protein preparation, so appropriate use of it is advised. Whether the mycorrhiza and DPJ combination treatment were feasible for seed germination and the seedling growth of the Helianthus plant, it was analysed and compared with control. Studies were experimented on the effect of mycorrhiza treated alone and DPJ treated alone whether improves the plant growth. DPJ if added with mycorrhiza in soil, showed significant rate of germination. In earlier research, it was elucidated that the DPJ contains phytohormones auxins, gibberellins and cytokinins in unknown quantity which boosts the plant growth comparative to mycorrhizal influence. The chlorophyll content of the DPJ treated plants enhanced as compared with control while the content showed no significant difference with mycorrhiza.

Reverse optimization algorithm of velocity uniformity in microchannels based on a simplified resistance network model

Velocity distribution plays an important role on the performances of microchannel reactors. In order to obtain an ideal velocity distribution among microchannels, a reverse optimization algorithm is proposed based on a simplified resistance network model, in which the relationship between the pressure drop and resistance is established, and the relationship between the flow rate and the channel velocity is also provided. The reverse optimization algorithm was developed based on the assumption of equal velocity values in all microchannels. Some structural parameters are set as unknown quantities and then optimized. In this work, microchannel width and manifold shapes are selected for optimization by using the reverse optimization algorithm. The results indicate that the non-equal microchannel width (NEMW) model can achieve more uniform velocity distribution than the corresponding equal microchannel width (EMW) model. The non-linear manifold structure (NLMS) can also present relatively uniform velocity distribution, but relatively poor velocity uniformity of microchannels compared to the NEMW model. Considering the requirement of practical application, a mass calculation algorithm is proposed to optimize the structural parameters in a certain range. However, the computing time of this algorithm is too large.

Seismic detectability of carbonates in the deep Earth: A nuclear inelastic scattering study

AbstractCarbonates play an important role in the transport and storage of carbon in the Earth’s mantle. However, the abundance of carbon and carbonates in subduction zones is still an unknown quantity. To determine the most abundant accessory phases and how they influence the dynamical processes that operate within the Earth, investigations on the vibrational, elastic, and thermodynamic properties of these phases are crucial for interpreting seismological observations. Recently, the nuclear inelastic scattering (NIS) method has proved to be a useful tool to access information on the lattice dynamics, as well as to determine Debye sound velocities of Fe-bearing materials. Here we derive the acoustic velocities from two carbonate compositions in the FeCO3-MgCO3 binary system up to ~70 GPa using the NIS method. We conclude that more Mg-rich samples, in this case (Fe0.26Mg0.74)CO3, have ~19% higher sound velocities than the pure end-member Fe composition. In addition, we observed a significant velocity increase after the Fe2+ spin transition was complete. After laser heating of FeCO3 at lower mantle conditions, we observed a dramatic velocity drop, which is probably associated with thermal decomposition to another phase. Parallel to our NIS experiments, we conducted a single-crystal X-ray diffraction (SCXRD) study to derive the equation of states of FeCO3 and (Fe0.26Mg0.74)CO3. The combined information from NIS (i.e., Debye velocities) and SCXRD (i.e., densities and bulk moduli) experiments enabled us to derive the primary and shear wave velocities of our samples. Our results are consistent with results obtained by other methods in previous studies, including Brillouin spectroscopy, inelastic X-ray scattering, and DFT calculations, supporting NIS as a reliable alternative method for studying the elastic properties of Fe-bearing systems at high pressures and temperatures. Finally, we discuss the seismic detectability of carbonates. We determine that nearly 22 wt% CO2 must be present in the subduction slab to detect a 1% shear wave velocity decrease compared to non-carbonated lithologies at the transition zone to lower mantle boundary depths.

Accidental vs. deliberate overdose admissions to the emergency department in Bali's largest hospital - a look into the effects of mental health on Indonesia's substance abuse problem

BackgroundMental health care in Indonesia is under-resourced, drugs are easily available, and alcohol use is rising, increasing the risk of accidental and deliberate overdose. AimsIdentify the demographics of overdose patients presenting to the emergency department. Determine the substances commonly used in overdose; the percentage of accidental vs. deliberate overdoses; the psychiatric history of overdose patients; the reasons behind the overdoses occurring.Methods22 overdose patients over the age of 18 that had presented to the emergency department at the RSUP Sanglah Hospital in a 1-year period were included in the study. Data were collected from records kept by the hospital on gender, age, nationality, substance(s) used in overdose, presence of psychiatric disorder, whether the overdose was accidental or deliberate, and the reason behind the overdose. Microsoft Excel was used to collect and analyse the data.ResultsOverdoses were predominantly carried out by male Indonesians aged 25-34. Alcohol was the most common substance used (n=5, 22.7%). 55.5% (n= 12) of overdoses were accidental and 45.5% (n=10) were deliberate. 3 out of 22 patients had a diagnosed mental disorder. The unknown quantity consumed; unknown content consumed; over-consumption; and spiked drink caused an accidental overdose. Suicidality and family issues caused a deliberate overdose.ConclusionsOverdose rates were lower than expected. Accidental overdoses are likely due to easy access to substances, and a fear of retribution preventing admission to hospital. The high deliberate overdose rate plus low mental disorder rate suggests patients reach a mental-health crisis point before being diagnosed with a mental disorder.Â

Carbon stock trends of baldcypress knees along climate gradients of the Mississippi River Alluvial Valley using allometric methods

Carbon stock trends of the knees of Taxodium distichum likely vary across climate gradients of the southeastern United States and contribute an unknown quantity of “teal” carbon to inland freshwater wetlands. Knee metrics (e.g., density, height, biomass) were measured in mixed T. distichum swamps across the Mississippi River Alluvial Valley (MRAV) from Illinois to Louisiana. Based on their geometric similarity to a cone, the biomasses of field knees were estimated by relating the volume of their measured field dimensions to lab-measured water displacement volume and biomass via volume/mass regressions (biomass (g) = 7.2230149 + 0.292902 × volume). Knees had greater height in flooded conditions (maximum height = 163 cm; Goose Lake, Arkansas), and also in climate normal environments of mid-range precipitation and temperature (p < 0.0001). Overall, knee biomass ha−1 was 7.5 times greater in flooded vs. not flooded conditions (34.6 ± 7.3 vs. 4.6 ± 1.0 Mg ha−1, respectively). The overall mean of knee carbon biomass stock was substantial (flooded vs. not flooded conditions: 18.1 ± 3.7 Mg C ha−1 to 2.9 ± 0.7 Mg C ha−1, respectively; knee/tree live standing biomass: 17.9–5.2%, respectively). Clearly, T. distichum knees should not be ignored in blue (teal) carbon discussions of wetlands. Because knees respond to climate normal conditions, hotter/drier environments in the MRAV could lead to a decline in the contribution of knee carbon stock in the southeastern United States.

A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits.

A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on possibly a huge number of molecular markers, and with a Gaussian residual distribution posed. Each (one per marker) of the vectors of regression coefficients (T: number of traits) is assigned the same T−variate Laplace prior distribution, with a null mean vector and unknown scale matrix Σ. The multivariate prior reduces to that of the standard univariate Bayesian LASSO when The covariance matrix of the residual distribution is assigned a multivariate Jeffreys prior, and Σ is given an inverse-Wishart prior. The unknown quantities in the model are learned using a Markov chain Monte Carlo sampling scheme constructed using a scale-mixture of normal distributions representation. MBL is demonstrated in a bivariate context employing two publicly available data sets using a bivariate genomic best linear unbiased prediction model (GBLUP) for benchmarking results. The first data set is one where wheat grain yields in two different environments are treated as distinct traits. The second data set comes from genotyped Pinus trees, with each individual measured for two traits: rust bin and gall volume. In MBL, the bivariate marker effects are shrunk differentially, i.e., “short” vectors are more strongly shrunk toward the origin than in GBLUP; conversely, “long” vectors are shrunk less. A predictive comparison was carried out as well in wheat, where the comparators of MBL were bivariate GBLUP and bivariate Bayes Cπ—a variable selection procedure. A training-testing layout was used, with 100 random reconstructions of training and testing sets. For the wheat data, all methods produced similar predictions. In Pinus, MBL gave better predictions that either a Bayesian bivariate GBLUP or the single trait Bayesian LASSO. MBL has been implemented in the Julia language package JWAS, and is now available for the scientific community to explore with different traits, species, and environments. It is well known that there is no universally best prediction machine, and MBL represents a new resource in the armamentarium for genome-enabled analysis and prediction of complex traits.

Dynamic inverse wave problems—part I: regularity for the direct problem

For parameter identification problems the Fréchet-derivative of the parameter-to-state map is of particular interest. In many applications, e.g. in seismic tomography, the unknown quantity is modeled as a coefficient in a linear differential equation, therefore computing the derivative of this map involves solving the same equation, but with a different right-hand side. It then remains to show that this right-hand side is regular enough to ensure the existence of a solution. For second-order hyperbolic PDEs with time-dependent parameters the needed results are not as readily available as in the stationary case, especially when working in a variational framework. This complicates for example the reconstruction of a time-dependent density in the wave equation. To overcome this problem we extend the existing regularity results to the time-dependent case.

Adaptive Leaderless Consensus Control of Strict-Feedback Nonlinear Multiagent Systems With Unknown Control Directions

The leaderless consensus problem over strict-feedback nonlinear multiagent systems (MASs) with unknown model parameters and control directions is investigated. The main idea of the existing consensus strategies for strict-feedback nonlinear MASs with unknown control directions is leading agents toward predefined global leaders/exosystems. However, in several missions, agents need to reach autonomous agreement on an a priori unknown quantity for a desired state, and hence the existing results are not applicable in these missions. The main contribution of this article is designing an adaptive leaderless consensus control scheme for strict-feedback nonlinear MASs when agents' control directions are unknown and unidentical. First, we introduce decentralized local error surfaces designed based on each agent position and neighboring agents' positions. We show that as the error surfaces remain bounded and converge to zero, the boundedness of the agents' positions and achieving leaderless consensus in the MAS can be guaranteed. Then, based on the properties of the Nussbaum-type functions, a decentralized backstepping adaptive control law is proposed under which the local error surfaces remain bounded and converge to zero. Finally, the design is more clarified and evaluated via an example.

Adaptive Interconnection and Damping Assignment Passivity-Based Control of Interlinking Converter in Hybrid AC/DC Grids

In this article, a nonlinear adaptive control strategy is proposed for a power conversion system (PCS) based on the interconnection and damping assignment passivity-based control. The PCS unit includes a dc/dc boost converter cascaded with a dc/ac inverter. The PCS system injects the predetermined values of active/reactive powers from a dc grid to the ac grid while the voltage of the interlinking capacitor is fixed. A disturbance observer is designed and implemented to estimate the output voltage of the dc grid as an unknown quantity. The proposed control strategy is derived based on the extended nonlinear model of the PCS in the synchronous reference frame. This model includes the dynamics of converters, interlinking capacitor, and the output filter of dc/ac inverter. The control commands are calculated through the assignment of desired damping and interconnection matrixes to the closed-loop system using a desired Hamiltonian energy function. The proposed strategy provides soft active/reactive power and voltage/current variations following the changes in the reference commands. The proposed control law is more feasible for experimental digital signal processing modules compared to nonlinear complicated control laws. Finally, the effectiveness of the proposed controller is tested using a set of time-domain simulations using realistic models for PCS components.

How deaths can help clinicians and policy-makers understand the risks of novel psychoactive substances.

Novel psychoactive substances (NPS), especially those newly created, are largely an unknown quantity, particularly in terms of their potential serious adverse effects. This means that policy-makers and clinicians are under-informed about appropriate responses. Collation of detailed information on deaths related to NPS use can help in providing knowledge and understanding these aspects of the NPS phenomenon. The purpose of this review is to outline the role(s) which such evidence-based data can play in this respect. UK NPS-related cases demonstrate differences in definitions used by the General Mortality Registers, and differences between countries, not only in terms of the type of NPS implicated in deaths, but the number and extent of such deaths over time. NPS deaths are continuing to increase numerically and as a proportion of all drug-poisoning deaths. In order to better understand how specific molecules contribute to and/or cause death, detailed information collected by Special Mortality Registers can provide examples of substances' modes of action, adverse effects, symptomatology, treatment interventions, mechanisms of death, etc. This information can provide clinicians and policy-makers with objective information on the serious harms from such emerging molecules. Such evidence-based advice informs public health interventions, service provision and policy decisions on regulation and control of NPS. However, without reliable, accurate and complete information that is correctly collated, scientifically analysed and disseminated in a timely manner, an understanding of the phenomenon of what deaths can be ascribed to NPS, their characteristics and nature will remain unachieved, and thus limit what can be done to reduce them.

A strain-displacement mixed formulation based on the modified couple stress theory for the flexural behaviour of laminated beams

A novel strain-displacement variational formulation for the flexural behaviour of laminated composite beams is presented, which accurately predicts three-dimensional stresses, yet is computationally more efficient than 3D finite element models. A global third-order and layer-wise zigzag profile is assumed for the axial deformation field to account for the effect of both stress-channelling and stress localisation. The axial and couple stresses are evaluated from the displacement field, while the transverse shear and transverse normal stresses are computed by the interlaminar-continuous equilibrium conditions within the framework of the modified couple stress theory. Then, axial and transverse force equilibrium conditions are imposed via two Lagrange multipliers, which correspond to the axial and transverse displacements. Using this mixed variational approach, both displacements and strains are treated as unknown quantities, resulting in more functional freedom to minimise the total strain energy. The differential quadrature method is used to solve the resulting governing and boundary equations for simply-supported, clamped and cantilever laminated beams. The deflections and stresses from this variational formulation for simply supported beams agree well with those from a Hellinger-Reissner stress-displacement mixed model found in the literature and the 3D elasticity solution given by Pagano. These strain-displacement models also accurately predict the localised stresses near clamped and free boundaries, which is confirmed by the high-fidelity Abaqus models.

Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid

Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important practical application, this paper proposes a state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal. The proposed technique can extract the frequency of the distorted grid voltage signal and is able to quantify the grid unbalances. First, a dynamical model of the grid voltage signal is developed considering the disturbances. In the model, frequency of the grid is considered as a constant and/or slowly-varying but unknown quantity. Based on the developed dynamical model, a state observer is proposed. Then using Lyapunov function-based approach, a frequency adaptation law is proposed. The chosen frequency adaptation law guarantees the global convergence of the estimation error dynamics and as a consequence, ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case. Gain tuning of the proposed state observer is very simple and can be done using Matlab commands. Some guidelines are also provided in this regard. Matlab/Simulink based numerical simulation results and dSPACE 1104 board-based experimental results are provided. Test results demonstrate the superiority and effectiveness of the proposed approach over another state-of-the art technique.

Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

The existing infrared (IR) ship target segmentation methods may suffer serious performance degradation in the situation of diverse background clutters and ship targets. To cope with this problem, a novel ship target segmentation method is proposed in this paper. Initially, the IR image is transformed into the map of large eigenvalues of structure tensor (STLE), where the horizon line and ship target boundary can be explicitly characterized. According to the scene context clue, the automatic horizon line detection (AHLD) is proposed to efficiently judge the existence of horizon line and remove sky/land region clutters. Then, based on the intensity distribution of ship target and sea background, the adaptive maximum histogram entropy (AMHE) is presented to accurately perceive the brightness (dark or bright) of ship target, and coarsely segment the bright or dark ship target from sea background. After that, considering the ship target boundary information, the regions-of-interest (ROI) of ship target is located and the ship foreground map (SFM) is developed to address the under-segmentation. Finally, a new Watershed algorithm namely structure tensor and maximum histogram entropy modified Watershed transform (TEWT) is constructed to completely extract the whole ship target. Extensive experiments show that the proposed method outperforms the state-of-the-art methods, especially for IR images with intricate background clutter and heavy noise. Moreover, the proposed method can work stably for ship target with unknown brightness, uneven intensities, low contrast, variable quantities, sizes, and shapes.

Do Incidental Environmental Anchors Bias Consumers’ Price Estimations?

It is well-established that decision makers bias their estimates of unknown quantities in the direction of a salient numerical anchor. Some standard anchoring paradigms have been shown to yield pervasive biases, such as Tversky and Kahneman’s (1974) classic 2-step task which includes a comparative question followed by an estimation question. In contrast there is much less evidence for the claim that incidental environmental anchors can produce assimilative effects on judgments, such as the amount people are willing to pay for a meal being greater at a restaurant calledStudio 97 compared to one called Studio 17. Three studies are reported in which the basic incidental environmental anchoring method of Critcher and Gilovich (2008) is employed to measure consumer price estimations. No statistically significant evidence of incidental anchoring was obtained. In contrast, robust standard anchoring effects were found. The results suggest that anchoring is limited to situations which require explicit thinking about the anchor.

Nonparametric Time-Varying Vector Moving Average (infinity) Models

Multivariate dynamic time series models are widely encountered in practical studies, e.g., modelling policy transmission mechanism and measuring connectedness between economic agents. To better capture the dynamics, this paper proposes a wide class of multivariate dynamic models with time — varying coefficients, which have a general time — varying vector moving average (VMA) representation, and nest, for instance, time — varying vector autoregression (VAR), time — varying vector autoregression moving--average (VARMA), and so forth as special cases. The paper then develops a unified estimation method for the unknown quantities before an asymptotic theory for the proposed estimators is established. In the empirical study, we investigate the transmission mechanism of monetary policy using U.S. data, and uncover a fall in the volatilities of exogenous shocks. In addition, we find that (i) monetary policy shocks have less influence on inflation before and during the so — called Great Moderation, (ii) inflation is more anchored recently, and (iii) the long — run level of inflation is below, but quite close to the Federal Reserve's target of two percent after the beginning of the Great Moderation period.

УСКОРЕННОЕ ПОСТРОЕНИЕ КРИВЫХ ТИТРОВАНИЯ В СРЕДЕ MAPLE

The method of accelerated construction of titration curves using the mathematical computer program Maple is presented. The method is based on the exclusion of unnecessary unknown quantities from the system of algebraic equations and the construction of a graphical relationship between the desired quantities. Titration of mixtures of two components is considered, including mixed titration

Detection of Mercury in Water using Filter Paper Based Channel and Colorimetric-Android Readout

The heavy metals dissolved in water are exceptionally unsafe to human and marine health which causes numerious medical complications. This paper demonstrates the use of a Microfluidic paper-based channel (µPAD) and an easyto-use colorimetric android based application for the accurate detection of heavy metal Mercury (Hg2+) in water. Gold Nanoparticles (AuNPs) functionalized with Papain and 2,6- pyridinedicarboxylic acid is used to detect Hg for further colorimetric analysis. Droplet-based Microfluidic channel in star shape with a paper-based stencil, a hydrophobic barrier and a hydrophilic channel using Polyvinyl Alcohol (PVA) was created for this. Colorimetric detection is used to create a database which is used to calibrate the color range for other unknown quantities of Mercury (Hg2+ ) present in water. This experimental database was used to create a user-friendly Android Application based display. The real-time android application was calibrated to quantify mercury concentrations from 0.1g / Litre to 0.001mg / Litre by observing a change in color from red to blue. This built platform can be utilized as a basic low-cost and portable system for various other fluid testings.

Asymptotic evaluation of the state and stiffness of the van der Paul oscillator

In many applications of physics, biology, and other sciences, an approach based on the concept of model equations is used as an approximate model of complex nonlinear processes. The basis of this concept is the provision that a small number of characteristic types movements of simple mathematical models inherent in systems gives the key to understanding and exploring a huge number of different phenomena. With this approach it is a priori assumed that the entire physical diverseness can be represented in the form of fairly simple model equations. It is contributes to a qualitative study of complex systems for various physical nature since basic models individually are well studied, their parameters have a physical interpretation. In particular, it is well known that oscillatory motion of various systems with a stable limit cycle can be modeled by a system consisting of one or more coupled van der Pol oscillators. Such systems are widely represented in various technical devices and in the study and modeling of some biological functions of the body, such as cardiac activity, respiration, locomotor activity, etc. It is considered a typical situation for many practical applications of control theory when the complete state vector of the system is unknown and only some of the functions of the state variables -- the outputs of the system are accessible to measurement. Therefore, the problem of determining in real time the state and parameters of such systems based on the results of measuring the output signals are relevant. One of these inverse control problems, namely, the problem of observability and parameter identification of an model oscillatory system is considered in this article. For observation and identification scheme design the method of invariant relations developed in analytical mechanics is used. Its modification in control problems allows us to synthesize additional relationships between known and unknown quantities of a dynamical system that arise during the observed motion. The method does not involve linearization of the original system and is essentially non-linear. The constructed nonlinear observer provides an asymptotic estimation of unknown parameter and velocity of oscillations.

Numerical Investigation into the Effect of Damage Openings on Ship Hydrodynamics by the Overset Mesh Technique

Damage stability is difficult to assess due to the complex hydrodynamic phenomena regarding interactions between fluid and structures. Therefore, a detailed analysis of the flooding progression and motion responses is important for improving ship safety. In this paper, numerical simulations are performed on the damaged DTMB 5415 ship at zero speed. All calculation are carried out using CD Adapco Star CCM + software, investigating the effect of damage openings on ship hydrodynamics, including the side damage and the bottom damage. The computational domain is modelled by the overset mesh and solved using the unsteady Reynold-average Navier-Stokes (URANS) solver. An implicit solver is used to find the field of all hydrodynamics unknown quantities, in conjunction with an iterative solver to solve each time step. The Volume of Fluid (VOF) method is applied to visualize the flooding process and capture the complex hydrodynamics behaviors. The simulation results indicated that two damage locations produce the characteristic flooding processes, and the motion responses corresponding to the hydrodynamic behaviors are different. Through comparative analysis, due to the difference between the horizontal impact on the longitudinal bulkhead and the vertical impact on the bottom plate, the bottom damage scenario always has a larger heel angle than the side damage scenario in the same period. However, the pitch motions are basically consistent. Generally, the visualization of the flooding process is efficient to explain the causes of the motion responses. Also, when the damage occurs, regardless of the bottom damage or the side damage, the excessive heel angle due to asymmetric flooding is often a threat to ship survivability with respect to the pitch angle.