Well-structured Models Research Articles

Various optimized machine learning techniques to predict agricultural commodity prices

AbstractRecent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

Evolutionary Polynomial Regression (EPR) has found widespread application and use for model structure development in engineering and science. This hybrid evolutionary approach merges real world data and explanatory variables to generate well-structured models in the form of polynomial equations. The simple and transparent models produced by this technique enable us to explore, via sensitivity analysis, the robustness of the derived models. Yet, existing EPR frameworks do not make explicit use of sensitivity analysis in the selection of robust and high-fidelity model structures. In this paper, we develop a multi-step sensitivity-driven method which combines the strengths of differential evolution and model selection via Monte Carlo simulation to explore the input–output relationships of model structures. In the first step, our hybrid approach automatically determines the optimum number of terms of the polynomial equations. In a subsequent step, our algorithm explores the mean parametric response of each explanatory variable used in the mathematical formulation to select a final model structure. Finally, in our selection of the most robust mathematical structure, we take explicit consideration of the prediction uncertainty of the simulated output. We illustrate and evaluate our EPR method for different engineering problems involving modeling and prediction of the moisture content and creep index of soils. Altogether, our results demonstrate that the use of sensitivity analysis as an integral part of model structure search and selection will lead to robust models with high predictive ability.

Projections of the global energy system evolution 30 years later: Checking the lessons of the future by the past experience

This article compares the author’s projections of the global energy system evolution up to 2020 as made in the early 1990s, against the actual data. This type of analysis is a rare and therefore an interesting case. Typically, after pro­jections for decades ahead have been published, no one bothers to compare them with the reality. Long-term projections are expected to outline possible states of the explored systems and to develop policy recommendations. The question of whether such projections can be trusted is always in minds of the projections’ “consumers” (decision-makers and experts), but very rarely a clear answer is provided. This paper fills this gap and shows that the “lessons of the future” can be learned based on well-structured models and analytical schemes.

A dual search-based EPR with self-adaptive offspring creation and compromise programming model selection

Evolutionary polynomial regression (EPR) is extensively used in engineering for soil properties modeling. This grey-box technique uses evolutionary computing to produce simple, transparent and well-structured models in the form of polynomial equations that best explain the observed data. A key task is then to determine mathematical structures for modeling physical phenomena and to select the optimal EPR model. This requires an algorithm to search through the model structure space and successfully produce feasible solutions that honor a set of statistical metrics. The complexity of EPR models increases greatly, however, with the number of polynomial terms used to tune these models. In this paper, we propose an alternative EPR for modeling complex soil properties. We implement a dual search-based EPR with self-adaptive offspring creation as model structure search strategy and couple a compromise programming tool to select a model that is preferred statistically relative to models with different polynomial terms. We illustrate our method using real-world data to improve predictions of optimal moisture content and creep index for soils. Our results demonstrate that the models derived using the proposed methodology can predict soil properties with adequate accuracy, physical meaning and lower number of parameters and input variables.

Transition of care in pediatric oncohematology: a systematic literature review.

The transition of medical care from a pediatric to an adult environment is a psychological change, a new orientation that requires a self-redefinition of the individual, to understand that changes are taking place in his life. Up to 60 percent of pediatric patients who transition to adult services will experience one or more disease or treatment-related complication as they become adults. A nurse who knows how to recognize potential barriers at an early stage can play a pivotal role in the educational plan for the transition process. A literature search was undertaken of PUBMED, CINAHL and The Cochrane Library, with specific inclusion and exclusion criteria, including articles published in the lasts ten years.This literature review has been performed according to the PRISMA statement. Using the keywords in different combination 38 articles were found in The Cochrane Library, 5877 in PUBMED, 274 in CINAHL. 88 articles were selected after the abstract screening. 31 after removing the duplicates and reading the full text. The main themes surrounding transition of care that emerged from the synthesis are the organization of care within common models of transition, innovative clinical approaches to transition, and the experience of patients and caregivers. The transition from pediatric to adult care of cancer or SCD survivors is an emerging topic in pediatric nursing. The organization of care is affected by the lack of clear and well-structured organizational models. Further research is needed to deepen the understanding of some aspects of the transition.

Open Problem—Convergence and Asymptotic Optimality of the Relative Value Iteration in Ergodic Control

The relative value iteration scheme (RVI) for Markov decision processes (MDP) dates back to White (1963) , a seminal work, which introduced an algorithm for solving the ergodic dynamic programming equation for the finite state, finite action case. Its ramifications have given rise to popular learning algorithms (Q-learning). More recently, this algorithm gained prominence because of its implications for model predictive control (MPC). For stochastic control problems on an infinite time horizon, especially for problems that seek to optimize the average performance (ergodic control), obtaining the optimal policy in explicit form is only possible for a few classes of well-structured models. What is often used in practice is a heuristic method called the rolling horizon, or receding horizon, or MPC. This works as follows: one solves the finite horizon problem for a given number of steps N, or for an interval [0,T] in the case of a continuous time problem. The result is a nonstationary Markov policy, which is optimal for the finite horizon problem. We fix the initial action (this is the action determined at the Nth step of the value iteration (VI) algorithm) and apply it as a stationary Markov control. We refer to this Markov control as the rolling horizon control. This of course depends on the length of the horizon N. One expects that for well-structured problems, if N is sufficiently large, then the rolling horizon control is near optimal. Of course, this is a heuristic. The rolling horizon control might not even be stable. For a good discussion on this problem, we refer the reader to Della Vecchia et al. (2012) . Obtaining such solutions is further complicated by the fact that the value of the ergodic cost required in the successive iteration scheme is not known. This is the reason for the RVI.

The Impact of Social Capital on Human Wellbeing: Empirical Evidence from Azad Jammu and Kashmir (Pakistan)

Maximization of wellbeing is the exceedingly targeted objective that conventional economics going forward. Keeping in view its central place, economists developed well-structured models and tools in order to measure and investigate wellbeing. In received literature, on the subject, various factors have been investigated that affecting wellbeing. However, wellbeing which is viewed from different approaches and is of a different form is not shaping equally with different types of factors. In this context, this study is an attempt to investigate how subjective wellbeing is affecting by social capital. The basic hypothesis is that “individual wellbeing moves parallel with its social capital”. The hypothesis is empirically tested using primary data set of 848 individuals collecting form Azad Jammu and Kashmir (Pakistan). The empirical estimates indicate that keeping other factors constant, an individual that embodied more social capital enjoy more wellbeing in their life. JEL Classification: B24, I30, C43

A formal approach to modeling and verification of business process collaborations

In the last years we are observing a growing interest in verification of business process models that, despite their lack of formal characterization, are widely adopted in industry and academia. To this aim, a formalization of the execution semantics of business process modeling languages is essential. In this paper, we focus on the OMG standard BPMN 2.0. Specifically, we provide a direct formalization of its semantics in terms of Labeled Transition Systems. This approach permits to avoid possible miss-interpretations, due to the usage of the natural language in the standard specification, and to overcome issues due to the mapping of BPMN to other formal languages, which are equipped with their own semantics. Our operational semantics is given for a relevant subset of BPMN elements focusing on the capability to model collaborations among organizations via message exchange. One of its distinctive aspects is the suitability to model business processes with arbitrary topology. This allows designers to freely specify their processes according to the reality, without the limitation of defining well-structured models. The provided formalization is also implemented by exploiting the capabilities of Maude. This implementation takes a collaboration model as an input and, explores all the model executions. By relying on it, automatic verification of properties related to collaborations has been carried out via the Maude model checker. We illustrate the benefits of our approach by means of a simple, yet realistic, running example concerning a travel booking scenario.

Use of evolutionary computing for modelling some complex problems in geotechnical engineering

In this paper, the feasibility of using evolutionary computing for solving some complex problems in geotechnical engineering is investigated. The paper presents a relatively new technique, i.e. evolutionary polynomial regression (EPR), for modelling three practical applications in geotechnical engineering including the settlement of shallow foundations on cohesionless soils, pullout capacity of small ground anchors and ultimate bearing capacity of pile foundations. The prediction results from the proposed EPR models are compared with those obtained from artificial neural network (ANN) models previously developed by the author, as well as some of the most commonly available methods. The results indicate that the proposed EPR models agree well with (or better than) the ANN models and significantly outperform the other existing methods. The advantage of EPR technique over ANNs is that EPR generates transparent and well-structured models in the form of simple and easy-to-use hand calculation formulae that can be readily used by practising engineers.

Complete parameter bounds and quasiidentifiability conditions for a class of unidentifiable linear systems

Lack of unique structural identifiability for parameters of dynamic system models is a very common situation with practical experimental schemes, particularly when studying biological systems. However, for well-structured (e.g., multicompartmental) models, it is often possible to localize unidentifiable parameters between finite limits (“interval identifiability”), using the same data base, and under certain conditions these limits nearly coincide. Two new results in this area are presented: (1) The smallest ranges on all unidentifiable rate constants and pool sizes of the most general n-compartment mammillary system are derived, in an easy-to-program algorithmic form, for the common case of input forcing and output measurements in the central pool only. From these results we see why elimination rate constants (“leaks”) are difficult to distinguish from zero, whereas exchange rate constants between pools, and pool sizes, may be bounded very tightly in certain circumstances. (2) The notion of quasiidentifiability, or sufficient identifiability for practical purposes, is introduced to quantify these circumstances. Each of the rate constants between central and peripheral pools, and all pool sizes, are quasiidentifiable if the magnitude of the ratio of the coefficient to the eigenvalue of the slowest mode is very much greater than the largest coefficient in the sum-of-exponentials response function. Also quasiidentifiability is a necessary condition for applicability of noncompartmental analysis to estimate pool sizes and residence times of mammillary systems with “leaky” noncentral pools.