Non-stochastic Methods Research Articles

A probabilistic economic and environmental impact assessment of a cyanobacteria-based biorefinery

Microbial based biofuels represent a potential promising solution as an environmentally favorable transportation fuel. Cyanobacteria have many of the same advantages as microalgae: ability for rapid growth in otherwise non-arable regions, suitability for genetic engineering, and simple nutritional needs. Additionally, cyanobacteria can be engineered to secrete valuable co-products that can be harvested independent from the produced biomass. However, little work has been done to identify the processes and the economic and environmental impacts associated with a large-scale cyanobacteria-to-fuels facility. The present study is a concurrent techno-economic and life cycle assessment of a facility that generates fuels and methyl laurate, an oleochemical, from the cyanobacterial species Synechocystis sp. PCC 6803. The biorefinery model includes all aspects of cultivation, separation of the secreted methyl laurate, biomass harvesting and fuel processing via hydrothermal liquefaction (HTL) of the dewatered biomass. The assessments leverage Monte Carlo analysis (MCA) to address uncertainty and variability inherent in the most significant input parameters, replacing them with probabilistic functions. For the facility configuration producing both fuels and the oleochemical co-product, the MCA average minimum fuel selling price (MFSP) is $2.47 per decimeter (dm3) or $9.34 per gallon of gasoline equivalent (gge) with the corresponding average global warming potential determined to be 118 g CO2-eq-MJ−1. The case producing only fuels results in an MCA average MFSP of $2.01-(dm3)−1 ($7.60-gge−1) and an average environmental impact of 100 g CO2-eq-MJ−1. These results are compared to static optimistic and conservative scenario analysis estimates, illustrating the over- and under-estimation of outcomes associated with non-stochastic methods. Suggested facility improvements include increases in pond productivity of both the biomass and methyl laurate oil production, as well as improvements to carbon utilization and bio-crude yield from HTL processing.

Trimmed Statistical Estimation via Variance Reduction

In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set while (2) simultaneously fitting the trimmed model on the uncontaminated data that remains. To solve the resulting nonconvex optimization problem, we introduce a fast stochastic proximal-gradient algorithm that incorporates prior knowledge through nonsmooth regularization. For data sets of size n, our approach requires O(n2/3/ℇ) gradient evaluations to reach ℇ-accuracy, and when a certain error bound holds, the complexity improves to O(κn2/3 log(1/ℇ)), where κ is a “condition number.” These rates are n1/3 times better than those achieved by typical, nonstochastic methods.

Influence of pseudopotentials on excitation energies from selected configuration interaction and diffusion Monte Carlo

Due to their diverse nature, the faithful description of excited states within electronic structure theory methods remains one of the grand challenges of modern theoretical chemistry. Quantum Monte Carlo (QMC) methods have been applied very successfully to ground state properties but still remain generally less effective than other non-stochastic methods for electronically excited states. Nonetheless, we have recently reported accurate excitation energies for small organic molecules at the fixed-node diffusion Monte Carlo (FN-DMC) within a Jastrow-free QMC protocol relying on a deterministic and systematic construction of nodal surfaces using the selected configuration interaction (sCI) algorithm known as CIPSI (Configuration Interaction using a Perturbative Selection made Iteratively). Albeit highly accurate, these all-electron calculations are computationally expensive due to the presence of core electrons. One very popular approach to remove these chemically-inert electrons from the QMC simulation is to introduce pseudopotentials (also known as effective core potentials). Taking the water molecule as an example, we investigate the influence of Burkatzki-Filippi-Dolg (BFD) pseudopotentials and their associated basis sets on vertical excitation energies obtained with sCI and FN-DMC methods. Although these pseudopotentials are known to be relatively safe for ground state properties, we evidence that special care may be required if one strives for highly accurate vertical transition energies. Indeed, comparing all-electron and valence-only calculations, we show that using pseudopotentials with the associated basis sets can induce differences of the order of 0.05 eV on the excitation energies. Fortunately, a reasonable estimate of this shift can be estimated at the sCI level.

Monte Carlo systems used for treatment planning and dose verification.

General-purpose radiation transport Monte Carlo codes have been used for estimation of the absorbed dose distribution in external photon and electron beam radiotherapy patients since several decades. Results obtained with these codes are usually more accurate than those provided by treatment planning systems based on non-stochastic methods. Traditionally, absorbed dose computations based on general-purpose Monte Carlo codes have been used only for research, owing to the difficulties associated with setting up asimulation and the long computation time required. To take advantage of radiation transport Monte Carlo codes applied to routine clinical practice, researchers and private companies have developed treatment planning and dose verification systems that are partly or fully based on fast Monte Carlo algorithms. This review presents acomprehensive list of the currently existing Monte Carlo systems that can be used to calculate or verify an external photon and electron beam radiotherapy treatment plan. Particular attention is given to those systems that are distributed, either freely or commercially, and that do not require programming tasks from the end user. These systems are compared in terms of features and the simulation time required to compute aset of benchmark calculations.

Robust Design of Structures with Uncertain Parameters and Frequency Constraints Using Non-Probabilistic Methods

The structural physical properties are often uncertain due to manufacture errors, measurement errors and other factors. Consequently, the vibration frequencies and corresponding eigenvectors are also uncertain. Robust design selects suitable design variables so that structural performance is insensitive to the various causes of variation without eliminating possible variations of variables. In practice robust design methods can be classified into probabilistic methods and non-probabilistic methods respectively. A new non-probabilistic robust design method based on the set theoretical convex method is presented in the present paper. The method not only inherits the advantages of existing non-stochastic methods, but also conquers the disadvantages of these methods.

Experience of mapping spatial structure of Cs-137 in natural landscape and patterns of its distribution in soil toposequence

Mapping of 137Cs activity was performed to test the idea of a non-random (deterministic) character of distribution of chemical elements and their compounds in space. Spatial distribution of the particular chemical elements and their compounds is proposed to be treated as a continuous single whole numerical field with a specified structure where each value and its change are related to the others. Direct measurements of the Chernobyl 137Cs activity and its mapping within the study plots confirmed the regular character of the radionuclide spatial structure on different scale levels. The observed spatial structure was suggested to reflect the secondary transformation of the initial fallout due to lateral and vertical migration of 137Cs with water as the main agent of physical and chemical migration related both to meso- and micro-relief features. Proposed deterministic approach is believed to stimulate development of the non-stochastic mathematical and physical methods in the analysis and forecast of spatial structure of geochemical fields.

Evaluation of Record Linkage Methods for Iterative Insertions

There have been many developments and applications of mathematical methods in the context of record linkage as one area of interdisciplinary research efforts. However, comparative evaluations of record linkage methods are still underrepresented. In this paper improvements of the Fellegi-Sunter model are compared with other elaborated classification methods in order to direct further research endeavors to the most promising methodologies. The task of linking records can be viewed as a special form of object identification. We consider several non-stochastic methods and procedures for the record linkage task in addition to the Fellegi-Sunter model and perform an empirical evaluation on artificial and real data in the context of iterative insertions. This evaluation provides a deeper insight into empirical similarities and differences between different modelling frames of the record linkage problem. In addition, the effects of using string comparators on the performance of different matching algorithms are evaluated. Our central results show that stochastic record linkage based on the principle of the EM algorithm exhibits best classification results when calibrating data are structurally different to validation data. Bagging, boosting together with support vector machines are best classification methods when calibrating and validation data have no major structural differences. The most promising methodologies for record linkage in environments similar to the one considered in this paper seem to be stochastic ones.

Fitting complex potential energy surfaces to simple model potentials: Application of the simplex‐annealing method

A stochastic method of optimization, which combines simulated annealing with simplex, is implemented to fit the parameters of a simple model potential. The main characteristic of the method is that it explores the whole space of the parameters of the model potential, and therefore it is very efficient in locating the global minimum of the cost function, in addition to being independent of the initial guess of the parameters. The method is employed to fit the complex intermolecular potential energy surface of the dimer of water, using as a reference the spectroscopic quality anisotropic site-site potential of Feller et al. The simple model potential chosen for its reparameterization is the MCY model potential of Clementi et al. The quality of the fit is assessed by comparing the geometry of the minimum, the harmonic frequencies, and the second virial coefficients of the parameterized potential with the reference one. Finally, to prove more rigorously the robustness of this method, it is compared with standard nonstochastic methods of optimization.

Smallholder Technical Efficiency with Stochastic Exogenous Production Conditions

There is a large literature on the estimation of frontier production functions, much of it applied to low-income agriculture. However, much of this literature largely ignores nature's role in agricultural production. Because exogenous, natural production conditions (e.g., rainfall, soil quality, pest infestation, plant disease, weed growth) are rarely uniform or symmetrically distributed within a population or a sample thereof, this omission generally leads to downward bias in producers' estimated efficiency and to biased estimates of both the parameters of the production frontier and the correlates of true technical inefficiency. Using panel data from 464 traditional rice plots in Cote d'Ivoire, we show that controlling for stochastic, exogenous, natural production conditions in estimating the production frontier significantly increases smallholder rice farmers' estimated efficiency, whether estimated using parametric, stochastic or nonparametric, nonstochastic methods. The resulting frontier parameter estimates are also more consistent with theoretical predictions than are those of a frontier estimated without controlling for exogenous production conditions. Conventional estimates of technical efficiency may then mislead policymakers' perceptions of overall efficiency levels and of the sources of such inefficiency.

Selection of Phcs for Evaluation of Family Welfare Programme in a Developing Country : Alternative Methods and Applications

The existence of large number of Primary Health Centres (PHCs) and the paucity of resources (money, time and labour), often compel the policy makers to selecting a set of few representative PHCs as the spatial bases for evaluation of family welfare programme. In this context, this paid develops simple (non-stochastic and non-axiomatic) methods for selecting representative PHCs in different levels of programmers' performance, such as, good, average and poor levels of performance. The alternative methods are implemented for actual PHC data in the Chitradurga district of Karnataka State in South India. The empirical results indicate the operational validity of analytical methods and the sensitivity of selection of representative PHCs with regard to the methods. From the policy viewpoint, the alternative classifications of PHCs according to performance levels indicate the need for differential and area-specific approach to evaluation of famiIy welfare programme. Finally, subject to the availability of comparable performance data, the simple methods developed in this paper can be replicated for selection of PHCs elsewhere in developing countries.

Kriging and Splines: An Empirical Comparison of their Predictive Performance in Some Applications

Abstract In disciplines such as soil science, ecology, meteorology, water resources, mining engineering, and forestry, spatial prediction is of central interest. A sparsely sampled spatial process yields imperfect knowledge of a resource, from which prediction of unobserved parts of the process are to be made. A popular stochastic method that solves this problem is kriging. But the appropriateness of kriging—and, for that matter, of any method based on probabilistic models for spatial data—has been frequently questioned. A number of nonstochastic methods have also been proposed, the leading contender of which appears to be splines. There has been some debate as to which of kriging and splines is better—a debate that has centered largely on operational issues, because the two methods are based on different models for the process. In this article the debate is turned to where it ultimately matters—namely, the precision of prediction based on real data. By dividing data sets into modeling sets and prediction...