Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.     

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP).

     

Gaming Prediction Markets: Equilibrium Strategies with a Market Maker

We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting is not a WPBE. In the infinite-stage model with unconditionally independent signals, there does not exist an equilibrium in which all information is revealed in a finite amount of time. We propose a simple discounted market scoring rule that reduces the opportunity for bluffing strategies. We show that in any WPBE for the infinite-stage market with discounting, the market price converges to the fully-revealing price, and the rate of convergence can be bounded in terms of the discounting parameter. When signals are conditionally independent, truthful betting is the unique WPBE for the infinite-stage market with and without discounting.     

Can the uncertainty in brass casting blending problem be managed? A probability/possibility transformation approach

Problems about the uncertainty in raw material compositions are a critical issue for the blending problems. It is feared that uncertainty in raw material compositions would often cause percent values of the actual blend to go out of specification limits. In this paper, the aleatory and epistemic uncertainties have been handled simultaneously in a blending optimization problem for brass casting. The aleatory and epistemic uncertainties are modeled by using probability and possibility theories respectively. However, the probabilistic and the possibilistic uncertainties are different from the each other. Therefore to solve the mathematical model, including these uncertainties, a transformation of any type of uncertainty to the other is needed. In this study, probabilistic uncertainties are transformed to the possibilistic uncertainties by considering Rong and Lahdelma’s (2008) and the Dubois, Prade, and Sandri (1993) and Dubois, Foulloy, Mauris, and Prade (2004) transformation approaches. This transformation process converts the former model to a possibilistic model. Then the possibilistic models, obtained from each transformation, are solved by using α cuts approach. The solutions of the two possibilistic models have shown that the model, which uses Dubois’s transformation, prepares blends with lower cost than the other model, which uses Rong and Lahdelma’s transformation.     

The MORPHEUS Heterogeneous Dynamically Reconfigurable Platform

Reconfigurable computing offers a wide range of low cost and efficient solutions for embedded systems. The proper choice of the reconfigurable device, the granularity of its processing elements and its memory architecture highly depend on the type of application and their data flow. Existing solutions either offer fine grain FPGAs, which rely on a hardware synthesis flow and offer the maximum degree of flexibility, or coarser grain solutions, which are usually more suitable for a particular type of data flow and applications. In this paper, we present the MORPHEUS architecture, a versatile reconfigurable heterogeneous System-on-Chip targeting streaming applications. The presented architecture exploits different reconfigurable technologies at several computation granularities that efficiently address the different applications needs. In order to efficiently exploit the presented architecture, we implemented a complete software solution to map C applications to the reconfigurable architecture. In this paper, we describe the complete toolset and provide concrete use cases of the architecture.     

A new method for evaluating antiarrhythmic drug efficacy

To develop standards for distinguishing antiarrhythmic drug effect from spontaneous variability of premature ventricular complexes (PVCs), 21 males (mean age 56 +/- 8 years) with chronic ischemic heart disease and PVCs underwent symptom-limited treadmill exercise testing and 24-hour ambulatory monitoring before and after 2 weeks of placebo medication. Linear regression analysis was used to describe the relationship between baseline and placebo PVC frequency for various indexes of ventricular ectopic activity and to establish 95% and 99% one-tailed confidence intervals for this relationship within the group of 21 patients. The lower limit of baseline PVC frequency for which the procedure could distinguish a placebo from a true drug response, termed the "sensitivity threshold," was an average frequency of 2.2 PVCs/hour for ambulatory electrocardiographic monitoring and 1.2 PVCs/min for treadmill exercise testing. All patients exceeded the sensitivity threshold on baseline ambulatory ECGs, but only 38% of patients did so on baseline treadmill exercise tests. To establish antiarrhythmic efficacy with 95% confidence, the minimal percent reduction of PVCs between baseline and placebo visits was 68% for treadmill exercise testing and 65% for ambulatory electrocardiography. Although these standards were developed in patients with chronic ischemic heart disease, the model can be used to establish antiarrhythmic drug efficacy in any patient group.     

A Tractable Class of Maximal Hedging Policies in Multi-Part Manufacturing Systems

The flow control problem in multi-part failure prone manufacturing systems is considered. While computationnaly attractive, the near optimal controllers of Caramanis and Sharifnia, suffer from the drawback that the production capacity set must be approximated via a very restricted set of inscribed hypercubes, namely those for which a componentwise feasibility requirement is satisfied. Also, due to the completely decoupled nature of production along each component, utilization of the restricted capacity set is suboptimal. A class of capacity set incribed hypercube policies called simple maximal hedging (SMH) policies is introduced. In SMH policies production levels along the various components of the capacity set are coupled, the componentwise feasibility requirement is lifted, and there is no underutilization of production capacity if needed. In a p part types manufacturing system, for partwise additive cost functionals, it is shown that performance evaluation of a given SMH policy reduces to the analysis of p decoupled (fictitious) semi-Markovian machines. The machines are Markovianized via first passage-time analysis and a Padé approximants technique. Numerical optimization over the class of SMH policies in a sample manufacturing system indicates that their performance can come close to that of the optimal control.     

Conception of Stretchable Resistive Memory Devices Based on Nanostructure‐Controlled Carbohydrate‐block‐Polyisoprene Block Copolymers

It is discovered that the memory‐type behaviors of novel carbohydrate‐block ‐polyisoprene (MH‐b ‐PI) block copolymers‐based devices, including write‐once‐read‐many‐times, Flash, and dynamic‐random‐access‐memory, can be easily controlled by the self‐assembly nanostructures (vertical cylinder, horizontal cylinder, and order‐packed sphere), in which the MH and PI blocks, respectively, provide the charge‐trapping and stretchable function. With increasing the flexible PI block length, the stretchability of the designed copolymers can be significantly improved up to 100% without forming cracks. Thus, intrinsically stretchable resistive memory devices (polydimethylsiloxane(PDMS)/carbon nanotubes(CNTs)/MH‐b ‐PI thin film/Al) using the MH‐b ‐PI thin film as an active layer is successfully fabricated and that using the MH‐b ‐PI_12.6k under 100% strain exhibits an excellent ON/OFF current ratio of over 10⁶ (reading at ?1 V) with stable V _set around ?2 V. Furthermore, the endurance characteristics can be maintained over 500 cycles upon 40% strain. This work establishes and represents a novel avenue for the design of green carbohydrate‐derived and stretchable memory materials.     

Investigation of non-Newtonian flow characterization and rheology of heavy crude oil

The heavy crude oil exhibits a non-Newtonian shear thinning behavior over the examined shear rate. The viscosity of the heavy crude oil decreases about 15.6% when the temperature increased from 30 to 60°C. Heavy crude oil was blended with the aqueous solution of surfactant and saline water in different volumetric proportions of NaCl, and Na₂CO₃ solution mixtures. The addition of 50% of the mixture to the heavy crude oil causes a strong reduction in the viscosity, about 67.5% at 60°C. The heavy crude oil fits the Power law model since it has the lowest average absolute percent error of 0.0291. The flow behavior index of the heavy crude oil reaches a value of 0.9305 at a temperature of 30°C and it increases to 0.9373 when the temperature raises 60°C, while the consistence coefficient decreases from 2.8811 to 2.3558.