Use Of Prediction Methods Research Articles

Ecological assessment of pastures semi-deserts and dry steppes of Azerbaijan

The absence in the scientific literature of criteria for assessing the ecological status of pasture lands, insufficient knowledge of the use of predictive methods and technology for carrying out special agrochemical measures, as well as issues of permissible loads, served as the basis for choosing the topic of research work. For the first time, in the conditions of pastures in Azerbaijan, an environmental and energy assessment of soil-landscape complexes was carried out. A detailed and final quality assessment was drawn up on soil scale, and the coefficient of their comparative merit was determined. A scientifically based system of agrochemical measures for the superficial and fundamental improvement of pastures has been developed. The final bonitet scale, reflecting the level of fertility of soil varieties, showed that the soils of the Jeyranchol massif turned out to be the most fertile at 62 points, compared with them, the pasture soils of Ajinohur on average across the massif received 53 points, Gobustan 51 points, and the Kura-Araz lowland 55 points. On average, pasture lands in Azerbaijan are valued at 55 points, which indicates the need for agro-reclamation measures. The types of forage plants are distributed as follows: i) Cereals 116, 12%; ii) Asteraceae 109, 11.2%; iii) Legumes 82, 8.4%; iv) Brassicas 59, 6.2%; 6%; v) Cloves 50, 5.3%; vi) Lamiaceae 42, 4.6%; vii) Linear 40, 4.2%; viii) Goosefoot 6, ix) Gimletaceae 32 species, 3.4%; x) Umbrellas 38, 4.4%; xi) Other 339 types, 35.5%.

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot.

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra's algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies.

Kinematic Viscosity Prediction Guide: Reviewing and Evaluating Empirical Models for Diesel Fractions, and Biodiesel–Diesel Blends According to the Temperature and Feedstock

Experimental analysis of viscosity can be a straightforward and inexpensive analysis for few samples. However, in industrial processes that have high demands of properties measurements, the determination of viscosity and other properties involves time-consuming with sampling, analysis, and availability of results. Also in refineries, the sampling routines for experimental determination of the viscosity of streams are not enough to represent variations that occur in the process, such as the shift of an oil tank in distillation units. In addition, besides requiring cost of operating personnel and laboratory analyst, all of these steps can take up to one shift until the result is available. Therefore, as an alternative, the use of predictive methods of kinematic viscosity are essential. Empirical methods have been used in simulations and design calculations of streams and mixture at industries regarding kinematic viscosity (KV) of petroleum fractions and fuels at different temperatures. However, there are uncertainties about the most accurate method to use at specific condition (temperature, feedstock, volume fraction) which might affect the KV prediction of fuels with unknown composition. Therefore, we assembled and evaluated several methods to predict KV of different diesel systems. In addition, new methods for predicting KV of diesel fractions at several temperatures were also developed for improving the estimation accuracy. As a result, we developed a guide with suggestions of the most accurate models to be applied for diesel fraction from assays, diesel fractions S500 from blend system at several temperatures, and biodiesel–diesel blends at different temperatures, volume fractions, and feedstock.

Practical use of Method for Prediction of Welding Deformation Applicable for Real Scale Structures

Practical use of Method for Prediction of Welding Deformation Applicable for Real Scale Structures

Development of performance measurement system in the context of Industry 4: a case study

To manage and cope with significant, radical changes of business processes and activities in the context of Industry 4, it is necessary for organizations to have a rapid reactive ability. This requirement may be fulfilled by performance measurement system (PMS) which should be aligned with and could reflect the current digitalization and innovative trends. According to this PMS should be developed due to changes in technology, business environment and organizational processes. But the main uncertainty here is to understand what are the main changes and development trends of PMS needed in the context of Industry 4?To solve this problem, the research presents how PMS can be developed facing industrial revolution using qualitative research method - case study with multiple sources of evidence from semi-structured interviews and documents analysis. According to research results a comprehensive framework for changes in PMS due to the alterations of Industry 4 was developed. Case study results show that technological developments and digitalization processes lead to a more intensive use of predictive methods in planning processes as well as faster control and decision making processes. The main contribution of this paper is to highlight the scientific literature of performance measurement by providing a comprehensive view of developments in PMS in the context of Industry 4. From a practical perspective, the study provides specific practical insights in order to support the reaction processes within organization.

Enhancement of bearing capacity from consolidation: due to changing strength or failure mechanism?

Bearing capacity of shallow foundations is higher following preload (or self-weight)-induced consolidation because the soil strength changes, and perhaps because the failure mechanism changes. Previous studies have illustrated this effect by plotting or predicting changes in either bearing capacity factor or strength. In this study, the relative contribution of these two effects is explored. This is achieved by formalising a definition of bearing capacity factor, which is described in terms of the average strength mobilised in the deformation mechanism at failure. Using the alternative definition of bearing capacity factor, the gain in foundation capacity is shown to be almost entirely due to changes in soil strength, rather than bearing capacity factor, which remains largely unaffected by the strength gains. This observation should encourage future studies into consolidated bearing capacity to present gains in capacity in terms of changes in mobilised strength rather than changes in bearing capacity factors, and supports the use of prediction methods that focus on defining the change in soil strength.

Prediction of Hepatic Clearance of Stereoselective Metabolism of Carvedilol in Liver Microsomes and Hepatocytes of Sprague-Dawley and Cytochrome P450 2D-Deficient Dark Agouti Rats

Hepatic clearance (CLh) of carvedilol (CAR), which is eliminated via stereoselective metabolism by the CYP2D subfamily of cytochromes P450 (CYPs), was predicted using liver microsomes and hepatocytes from Sprague-Dawley (SD) rats and CYP2D-deficient Dark Agouti (DA) rats to determine the usefulness of prediction method. Plasma concentrations of CAR following intravenous injection to DA rats were higher than those in SD rats. The volume of distribution at steady state and total clearance (CLtot) of S-CAR were approximately two times greater than those of R-CAR in both strains. CLh predicted from in vitro studies using DA rat liver microsomes was different from that obtained from in vivo studies. In contrast, in vitro CLh prediction using DA rat hepatocytes was nearly identical to the CLh observed in DA rats in vivo, and was lower than that in SD rats. The predicted CLh in vitro using hepatocytes correlated well with the observed CLtot in vivo, which is expected to be nearly the same as CLh. These results suggest that in vitro metabolic studies using hepatocytes are more relevant with regard to stereoselectively predicting CLh of CAR than those using liver microsomes.

Spatiotemporal floodplain mapping and prediction using HEC-RAS - GIS tools: Case of the Mejerda river, Tunisia

The floods have become a scourge in recent years (Floods of, 2003, 2006, 2009, 2011, and 2012), increasingly frequent and devastating. Tunisia does not escape flooding problems, the flood management requires basically a better knowledge of the phenomenon (flood), and the use of predictive methods. In order to limit this risk, we became interested in hydrodynamics modeling of Medjerda basin.To reach this aim, rainfall distribution is studied and mapped using GIS tools. In addition, flood and return period estimation of rainfall are calculated using Hyfran. Also, Simulations of recent floods are calculated and mapped using HEC-RAS and HEC-GeoRAS for the most recent flood occurred in February–March 2015 in Medjerda basin.The analysis of the results shows a good correlation between simulated parameters and those measured. There is a flood of the river exceeding 240 m3/s (DGRE, 2015) and more flowing sections are observed in the future simulations; for return periods of 10yr, 20yr and 50yr.

ASSESSMENT OF THE ECONOMIC EFFICIENCY OF IT APPLICATION AT ENTERPRISES

The article covers theoretical aspects of the necessity of information technologies’ introduction and development at the enterprise. There was substantiated the importance of the introduction of modern information technologies and their influence on the effective activity of the enterprise. There were generalized the conditions and consequences of technologies introduction, the main trends of IT development at enterprises, and possibilities of modern technologies use. The composite analysis of the methods for assessing the efficiency of information technology use conducted made it possible to compile the methods into three groups (methods of financial analysis, methods of qualitative analysis, methods of predictive analysis) and to distinguish their characteristics and possibilities of use. There were considered the most popular structural modules of the corporate information and technological system (CIS) in the world. There was the generalized structural composition of the ERP-system with MRP, SRM, CRM, and CSRM modules. There were determined the main characteristics of IT systems, advantages and disadvantages of integrated CRM modules – customer relationship management system, BI data analysis systems (SAP, Microsoft Dynamics AX, Oracle BI systems), communication management system with SRM providers; 1C: Enterprise 8 – system of bookkeeping and management accounting use and the possibility of their association in CIS as: CSRP Enterprise Resource Planning System (Customer Synchronized Resource Planning). Resource planning technologies are functionally related in such a system, modules of CRM strategies are integrated, interaction with buyers and customers and bookkeeping analysis and accounting are synchronized. The method of economic efficiency determination with the improvement of calculation algorithm is proposed. The main stages of assessing the effectiveness of IT introduction and application at the enterprise can be summarized in three stages: preparation – analysis and assessment of the technology selection; introduction – analysis and assessment of direct costs and substantiation of expediency; implementation – assessment of economic efficiency of application and calculation of intellectual depreciation and possible stepwise modernization of information technology in future. The multiplicity of introduction goals and sources of IT payback requires a differentiated approach to the determination of the criteria for their effectiveness assessment. There was developed an algorithm of determination of the economic efficiency of IT introduction in the activity of the enterprise. The traditional way of profit assessing from the IT introduction does not ensure full consideration of qualitative and strategic efficiency, therefore, it requires additional use of predictive methods that allow supplementing quantitative calculations. The author proposed to calculate the efficiency of introduction, modernization, and use of IT projects, use of scenarios analysis methods, which include the Black-Scholes model and the binomial model for calculating the effectiveness of real options. The author considers the application of the Black-Scholes-Merton model to be the most expedient because, in contrast to the binomial one, it is easier to use but allows obtaining a result with minimal errors, without significant time costs close to the definitions of the binomial model. The use of such a model has already proven effective not only when calculating the effectiveness of real options, but there are also positive results of the study when using them in other areas of analysis. We consider it economically feasible to use methods determined by the author to assess economic efficiency of IT use.

Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures.

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development.

A Survey About Prediction-Based Data Reduction in Wireless Sensor Networks

One of the main characteristics of Wireless Sensor Networks (WSNs) is the constrained energy resources of their wireless sensor nodes. Although this issue has been addressed in several works and received much attention over the years, the most recent advances pointed out that the energy harvesting and wireless charging techniques may offer means to overcome such a limitation. Consequently, an issue that had been put in second place now emerges: the low availability of spectrum resources. Because of it, the incorporation of the WSNs into the Internet of Things and the exponential growth of the latter may be hindered if no control over the data generation is taken. Alternatively, part of the sensed data can be predicted without triggering transmissions that could congest the wireless medium. In this work, we analyze and categorize existing prediction-based data reduction mechanisms that have been designed for WSNs. Our main contribution is a systematic procedure for selecting a scheme to make predictions in WSNs, based on WSNs’ constraints, characteristics of prediction methods, and monitored data. Finally, we conclude the article with a discussion about future challenges and open research directions in the use of prediction methods to support the WSNs’ growth.

A two‐stage forecasting approach for short‐term intermodal freight prediction

AbstractThe forecasting of the freight transportation, especially the short‐term case, is an important topic in the daily supply chain management. Intermodal freight transportation is subject to multiple complex calendar effects arising in the port environment. The use of prediction methods provides information that may be helpful as a decision‐making tool in the management and planning of operations processes in ports. This work addresses the forecasting problem on a daily basis by a novel two‐stage scheme combination to offer reliable predictions of fresh freight weight on Ro‐Ro (roll‐on/roll‐off) transport for 7 and 14 days ahead. The study compares daily forecasting with a weekly forecasting approach. The applies database preprocessing and Bayesian regularization neural networks (BRNN) in Stage I. In Stage II, an ensemble framework of the best BRNN models is used to enhance the Stage I forecasting. The results show that the models assessed are a promising tool to predict freight time series for Ro‐Ro transport.

The Use of Equivalent Model and Numerical Simulation for EMC Analysis in Hospital Environments

This paper promotes the use of prediction methods as an alternative tool for evaluating and managing electromagnetic compatibility (EMC) in hospital environments. Radiated emissions originated from medical equipment are modeled based on open area test site measurements, and they are applied to numerical simulation using transmission line matrix method (TLM). TLM simulation is performed to include not only the multipath effects provided by obstacles, but also the interaction and coupling of all different sources/models operating simultaneously on the environment under test (a typical operating room). This approach provides a useful tool in order to assess electromagnetic profiles established within the room area, and it allows implementing optimization processes such as genetic algorithms (GA). GA is employed to evaluate many different placements of medical equipment within the environment under test. A fitness function is defined to minimize the average electric field inside the room, providing a better equipment disposal to develop a preliminary EMC management program.

Recurrent pattern matching based stereo image coding using linear predictors

The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based generic image encoding solution which has been investigated earlier for the compression of stereo images with successful results. While first MMP-based proposals for stereo image coding employed dictionary-based techniques for disparity compensation, posterior developments have demonstrated the advantage of using predictive methods. In this paper, we focus on recent investigations on the use of predictive methods in the MMP algorithm and propose a new prediction framework for efficient stereo image coding. This framework comprises an advanced intra directional prediction model and a new linear predictive scheme for efficient disparity compensation. The linear prediction model is the main novelty of this work, combining adaptive linear models estimated by least-squares algorithm with fixed linear models provided by the block-matching algorithm. The performance of the proposed intra prediction and disparity compensation methods when applied in an MMP encoder has been evaluated experimentally. Comparisons with the current stereo image coding standards showed that the proposed MMP algorithm significantly outperforms the Stereo High Profile of H.264/AVC standard. In addition, it presents a competitive performance relative to the MV-HEVC standard. These results also suggest that current stereo image coding standards may benefit from the proposed linear prediction scheme for disparity compensation, as an extension to the omnipresent block-matching solution.

CHETAH 10.0, the ASTM Computer Program for the Prevention of Reactive and Flammability Hazards

Abstract The uses of the ASTM computer program CHETAH were described. The recently released version 10 is capable of accomplishing five different kinds of calculations related to chemical thermodynamics. The program enables the prediction of chemical reactive hazards, heats of reaction for combustion and other types of reactions, and a variety of flammability parameters. It can accomplish these calculations making use of its large internal databases of thermochemical properties or by making use of predictive methods for chemicals for which properties have yet to be determined.

Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic Design

AbstractThe current paper indicates the systematic determination of the optimal conditions for the selected physical properties of the fava bean. The effects of varying moisture content of the Barkat fava bean grown in Golestan, Iran, in the range of 9.3–31.3% (Input) on the 15 selected physical properties of the crop, including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image; gravity and frictional parameters like the weight of 1000 seeds; true density; bulk density; volume and porosity as well as friction (filling and vacating angle stability) as the outputs were predicted. Afterwards, a model relying on fuzzy logic for the prediction of the 15 outputs had been presented. To build the model, training and testing using experimental results from the Barkat fava bean were conducted. The data used as the input of the fuzzy logic model are arranged in a format of one input parameter that covers the percentage of the moisture contents of the beans. In relation to the varying moisture content (input), the outcomes (15 physical parameters) were predicted. The correlation coefficients obtained between the experimental and predicted outputs as well as the Mean Standard Deviation indicated the competence of fuzzy logic design in predicting the selected physical properties of fava bean seeds.Practical ApplicationToday, because of the high demand for crops to be used extensively in the human diet, enhancements in the efficiency of the processing are getting more attention. In this way, finding and/or the determination of the optimal conditions for processing with minimum waste looks very substantial. Therefore, the use of prediction methods in food processing is considered to be a tool for improving the efficiency and the quality of the produced products. In this regard, the fuzzy logic design as a novel prediction tool, along with response surface methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Therefore Fuzzy Logic Design is optimized to predict the some of the selected physical properties of fava bean, as a function of seed's moisture content. Therefore predicting the behavior of this crop against different moisture contents can improve the quality and performance of the products with the minimum wastes during very short time.© 2016 Wiley Periodicals, Inc

Toward a predictive model for predicting viscosity of natural and hydrocarbon gases

Accurate knowledge of pure hydrocarbon and natural gas viscosity is essential for reliable reservoir characterization and simulation as well as economic design of natural gas processing and transport units. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. When there is no available experimental data for the required composition, pressure, and temperature conditions, the use of predictive methods becomes important. In this communication, a novel approach was proposed to develop for prediction of viscosity of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrogen sulfide, using over 3800 data sets. A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was proposed (CSA-LSSVM model). Moreover, comparative studies between the CSA-LSSVM model and fourteen empirical correlations were done. To this end, statistical and graphical error analyses have been used simultaneously. Obtained results illustrated that the “two-parameter” CSA-LSSVM model is more robust, reliable, and consistent than the existing correlations for the prediction of pure and natural gas viscosity. Moreover, the relevancy factor illustrated that the molecular weight of gas has the greatest impact on gas viscosity.

Laminar single phase flow distribution in a multi-tube mini-channel heat exchanger using fractal distribution

Mini-channel technology is a promising solution in a world where there is a growing demand for product miniaturization. In this paper a new geometry for a distributor of a multi-tube mini-channel shell and tube exchanger is proposed which should distribute the flow equally over the tubes. The tubes have an internal diameter of 0.5mm while the shell has a hydraulic diameter of 1.8mm. The flow in the heat exchanger is pure counter current flow; no baffles have been used in the shell. Experimental data for laminar single phase water in both the tube and shell side are reported. A mathematical model is developed which can predict the performance of the multi-tube mini-channel that uses the newly proposed distributor. This model considers both heat transfer and pressure drop and makes use of prediction methods proposed in the literature for similar geometries. It has been experimentally validated that the newly proposed distributor gives a good distribution for laminar single phase flows. The mathematical model can predict the experimental performance of the multi-tube mini-channel exchanger within 10% accuracy.

Use of prediction methods to assess laboratory bias and mean values associated with an interlaboratory study for method validation and/or proficiency testing.

Two methods of prediction of random variables, best predictor (BP) and best linear unbiased predictor (BLUP), are discussed as potential statistical methods to predict laboratory true mean and bias values using the sample laboratory mean (y(i)) from interlaboratory studies. The predictions developed here require that the interlaboratory and/or proficiency study be designed and conducted in a manner consistent with the assumptions of a one-way completely randomized model (CRM). Under the CRM the individual laboratory true mean and bias are not parameters but are defined to be random variables that are unobservable and considered as realized values that cannot be estimated but can be predicted using methods of "prediction." The BP method is applicable when all salient parameters are known, e.g., the consensus true overall mean (mu) and repeatability and reproducibility components (sigma2(r) and sigma2(R)), while the BLUP method is useful when sigma2(r) and sigma2(R) are known, but mu is estimated by the generalized least square estimator. Although the derivations of predictors are obtained by minimizing the mean-square error under the CRM assumptions, the predictors are the expected laboratory true mean and bias given the sample laboratory mean, i.e., conditional expectation.

Prediction in evolutionary algorithms for dynamic environments

Evolutionary algorithms have been widely used to solve dynamic optimization problems. Memory-based evolutionary algorithms are often used when the dynamics of the environment follow some repeated behavior. Over the last few years, the use of prediction mechanisms combined with memory has been explored. These prediction techniques are used to avoid the decrease of the algorithm's performance when a change occurs. This paper investigates the use of prediction methods in memory-based evolutionary algorithms for two distinct situations: to predict when the next change will happen and how the environment will change. For the first predictor two techniques are explored, one based on linear regression and another supported by nonlinear regression. For the second, a technique based on Markov chains is explored. Several experiments were carried out using different types of dynamics in two benchmark problems. Experimental results show that the incorporation of the proposed prediction techniques efficiently improves the performance of evolutionary algorithms in dynamic optimization problems.