Performance Forecasting Research Articles

Integrated CO2, cost, and schedule management system for building construction projects using the earned value management theory

Although the construction phase in a building's life cycle is relatively very short, the density of the CO2 emissions in the construction phase is very higher than that in the other phases (i.e., operations and maintenance phase). However, there was little research on scheduling, monitoring, evaluating and forecasting CO2 emissions based on the construction schedule. To address this challenge, this research aimed to develop an integrated CO2, cost and schedule management (ICCSM) system for building construction projects using the earned value management theory. The proposed ICCSM system consisted of five steps: (i) definition of the project scope and organization; (ii) project planning and scheduling; (iii) estimation and budgeting of the CO2 emissions and construction costs; (iv) establishment and weighting of the project performance measurement baseline; and (v) monitoring and forecasting of the project performance. The case study showed that the ICCSM system can evaluate and forecast the project performance earlier and more accurately, based on the construction schedule, (i) when integrated weighting was implemented, the project performance can be estimated faster (i.e., one day earlier); and (ii) when 0.8 × (cost performance index) + 0.2 × (schedule performance index) was implemented as the index for calculating an estimate at completion, the project performance can be more accurately predicted (i.e., error rate = −0.24%). The ICCSM system could allow a project manager to monitor and forecast CO2 emissions and costs based on the construction schedule, simultaneously.

Predicting Financial Stability of Select BSE Companies Revisiting Altman Z Score

In the era of globalization, prediction of financial distress is of interest not only to managers but also to external stakeholders of a company. The stakeholders are continuously seeking the optimal solution for performance forecasting, as a way to rationalize the decision-making process. The recent past shows that financial stability of companies is at the stake. Stockholders, Managers, Creditor and employees of the business are always concerned about financial stability of the companies. The most frequently tool for financial analysis is financial ratios. However, financial ratios are no-longer proved appropriate for „Stockholders‟ equity position and creditors‟ claims. Stakeholder‟s have concerns about the consequences of financial distress for companies, and controls of capital adequacy through the regulatory capital requirement (Mingo, 2000). This shared interest creates persistent investigations and continuing attempts to answer an incessant question that how financial distress can be predicted, or what reveals the credit risk of firms. For this purpose most commonly used tool is Altman Z score, but due to nature of the explanatory variables, financial distress prediction research has not reached an unequivocal conclusion. The primary goal of this paper is to analyze and reexamine the Altman Z score. In order to facilitate the current research, various ratios were taken from Altman‟s Z score. To fulfill our objective Z score ratios were used to divide sample firms into healthy and unstable among BSE-30 companies. First the Z score is calculated for 10 companies selected for this purpose for a period of 5 years each. And then it is divided as per z scores, later the significant in the changes in the ratio is calculated with the help of One sample Komogrov-Smirnow test, which resulted that the change in the z scores is not significant in case of all the companies.

Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

Containing data noise in unconventional-reservoir-performance forecasting

Many decline-curve analysis methods have emerged for forecasting the future performance of unconventional reservoirs. However, severe noise in field data, coupled with the low-frequency rate in monitoring/reporting and the unknown behavior of many completion and reservoir parameters, collectively present serious challenges in obtaining correct model parameters in many settings.To address the complexity of this multitude of issues, performance forecasting is approached in two steps. First, we attempt to circumvent the data noise and frequency issues with a global cumulative production profile for a group of wells exhibiting similar performance characters, leading to the estimation of global model parameters. Second, we compare error trends amongst all methods for a basis of selecting well groups. Finally, a simple rule-of-thumb is developed to get an estimate of the allowable time for extrapolating performance prediction within 10% error.

Security Analysts and Strategic Decision Making: The Disparate Effects of Loss & Adaptation-Pressure

This paper adopts a legitimacy perspective to study the influence of security analysts on firms’ strategic decisions. In particular, we question how security analysts’ earnings pressure influences firms’ acquisition decisions. In line with prior work, we rely on legitimacy arguments to elucidate the ‘security analysts’ – ‘acquisition decision’ relationship, yet differentiated among two disparate mechanisms: Loss-pressure is defined as firms’ actual shortfall from analysts’ previous performance forecasts. It implies a loss in legitimacy and, thereby, suspends strategic decision making (i.e. acquisition decisions). Adaptation-pressure arises as analysts’ forecasts fall short of firms’ current or future profitability. It implies a mismatch in firms’ and analysts’ future expectations, inducing firms’ strategic decision making in order to realign with analysts’ believes and, hence, regain legitimacy. Studying panel data on S&P 1500 firms over a period of 21 years we find support for both mechanisms. We contribute by demonstrating the influence of security analysts on acquisition decisions, as well as by scrutinizing the mechanisms behind this influence. We introduce two possibilities to lose legitimacy and show their disparate effects on firms’ strategic decision making.

Optimum operating performance based online fault-tolerant control strategy for sensor faults in air conditioning systems

This paper presents an online fault-tolerant control strategy. By correcting the faulty measurements with a final correcting factor, the strategy covers five steps: fault detection, construction of correcting alternatives, performance forecasting, alternatives outranking, and fault correction. System energy, indoor air quality, human thermal comfort, and control efficiency are considered as a whole to evaluate the operating performance. Taking the supply air temperature sensor faults as testing examples, the strategy is tested in a virtual air conditioning system and simulated on TRNSYS platform. The testing results show that a large fault correcting factor is obtained in the first hour, an adjusting phase presents in the next several hours, and the correction factor keeps unchanged finally. High level of calculating accuracy is required for the performance prediction model. Also, for the outranking evaluation model, the thresholds and weight coefficients should be assigned appropriately to meet the requirements of building functions and/or owners.

Trend Analysis of Sales of Marble Product in Glister Success (Nig.) Ltd., Ilorin, Nigeria using Exponential Smoothing Method

Marketing models are important in analyses and forecasting of performance of mineral markets. The sale trend of the past can be extrapolated into the future. This study sourced the sales of marble chipping in tons in Glister Success Ltd, Ilorin and modeled its performance in order to evaluate the performance of marble market and make future forecast. Record of sales of the company for months was consulted; exponential smoothing method of trend analysis and forecasting was used for creation of sales model and forecasting. The calculations and modeling were done with the aid of Microsoft excel program. The resulting model shows sales performance and useful in forecasting sales. The forecast model shows that the forecast value is not over bloated against the actual value. The resulting model of the ‘forecast sales’ is hereby recommend for planning production for future sales in the establishment. Keywords: mineral, model, marble, exponential, forecasting Nigerian Journal of Technological Research, 8(2), 2013

New Time/Rate Relations for Decline-Curve Analysis of Unconventional Reservoirs

This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 162910, ’Practical Considerations for Decline-Curve Analysis in Unconventional Reservoirs - Application of Recently Developed Time/Rate Relations,’ by V. Okouma, SPE, Shell Canada Energy; D. Symmons, Consultant; N. Hosseinpour-Zonoozi, SPE, and D. Ilk, SPE, DeGolyer and MacNaughton; and T.A. Blasingame, SPE, Texas A&M University, prepared for the 2012 SPE Hydrocarbon Economics and Evaluation Symposium, Calgary, 24-25 September. The paper has not been peer reviewed. This work presents a workflow that can be used to analyze and forecast time/rate data of wells in low- and ultralow-permeability reservoirs. The key component of the workflow is the application of diagnostic plots to guide the analysis and obtain model parameters for a given time/rate relation. Once model parameters are obtained, the production profile is extrapolated to yield the estimated ultimate recovery (EUR) at a specified time limit or abandonment rate. Introduction The starting point for any discussion of decline-curve analysis for unconventional reservoirs must be an understanding that no simplified time/rate model can accurately capture all elements of the performance behavior. In addition, no time/rate model can be expected to pro-vide a completely unique forecast of future performance or prediction of EUR. It is important to be both realistic and practical when attempting to characterize production performance from systems where the permeability is on the order of 10–500 nd, the reservoir flow system is complex, and, although the induced-hydraulic-fracture system enables (and dominates) the production performance, there is only the most rudimentary understanding of the flow structure in the fracture systems. It is essential that these conditions be established as a starting point. Not doing so will inevitably lead the analyst to interpretations based on incorrect assumptions as well as significant bias. The authors assert that reasonable production forecasts and predictions of EUR can be made, but not in isolation, not solely looking at the data and the selected time/rate model. The analyst must consider the nature of the resource and the significant uncertainty in the ability to apply simple time/rate relations to a very complex reservoir system. As an attempt to better represent the general character of time/rate production data for a multistage-fractured horizontal well in an ultralow-permeability reservoir, numerous authors have developed time/rate relations using certain specific bases to represent a particular scenario. These developments include the following time/rate relations: Power-law exponential model Stretched exponential model Logistic growth model Duong model

Performance Forecast of Solar Spectrum Selective Absorbing Coating Based on RBF Neural Network

The paper researches on the coating performance prepared by sputtering stainless steel particles to Cu substrate, analyzes the effect of the six magnetron sputtering process parameters on absorption rate, such as Ar pressure, oxygen flow, sputtering vacuum degree, sputtering time, target voltage and target current with the artificial neural network technology, and forecasts the performance of the coating prepared by specific process parameters. The results show that during the process of magnetron sputtering when Ar pressure is within the range of 0.2 ~ 0.5 Pa, the oxygen flow is within the range of 0 ~ 20 sccm, and the target current is within the range of 380~ 430 A, the higher the sputtering vacuum degree and the target voltage, the superior the performance of the coating can be obtained. It is also found that the sputtering time has little effect on the coating performance, in the actual preparation process, on the condition that the performance can be guaranteed, the sputtering time should be as short as possible.

Non-parametric probabilistic forecasting of academic performance in Spanish high school using an epidemiological modelling approach

Academic underachievement is a concern of paramount importance in Europe, and particularly in Spain, where around of 30% of the students in the last two courses in high school do not achieve the minimum knowledge academic requirement. In order to analyse this problem, we propose a mathematical model via a system of ordinary differential equations to study the dynamics of the academic performance in Spain. Our approach is based on the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. Moreover, in order to consider the uncertainty in the estimation of model parameters, a bootstrapping approach is employed. This technique permits to forecast model trends in the next few years using confidence intervals. Unfortunately, the obtained results do not suggest improvement in academic performance for the coming years.

Integrated data envelopment analysis and neural network model for forecasting performance of wafer fabrication operations

With the rapid change of manufacturing environments, semiconductor fabricators are forced to make continuous investments in advanced technology to maintain their competitive advantage. Wafer fabrication (fab) performance evaluations are important for examining past operations such as the capacity and resource allocation. In addition, forecasting provides useful information for what-if analyses to anticipate useful strategies early to avoid potential losses. However, the integration of performance evaluation and forecast based on a consideration of the relative performance along the time horizon has rarely been addressed. In particular, the part of performance evaluation is to generate the relative performance in a period with rolling data. Then, the forecast part is to build a model for performance prediction based on the result of present performance. This study aimed to construct a performance forecast model by integrating data envelopment analysis and a back-propagation neural network for performance evaluation and forecast, respectively. Empirical data from a leading semiconductor company in Taiwan was used to test the proposed model. The results provide basic information and early alarms to adjust the resource allocation before the future performance declines. The empirical analysis demonstrated the practical feasibility of the proposed approach.

A Reformative Simulation Method of Motor Pole Core Vibration Characteristics

As to directing motor design, it is very important to make sure that the motor’s forecast of vibration performance has reference value. So, it must need motor’s parts vibration characteristics simulation results are so close to their characteristics. This paper puts forward a new simulation method of motor pole core vibration characteristics, this method includes setting anisotropy material attributes multipartite, getting attributes parameters values which are based on recommended fitting curves, modeling and equating windings, equating dipping lacquer and so on. Combining with experiments, the new method is validated its availability.

Impact of Various Parameters on the Production of Coalbed Methane

Summary Coalbed-methane (CBM) reservoirs are naturally fractured formations, comprising both permeable fractures and matrix blocks. The interaction between fractures and matrix presents a great challenge for the forecast of CBM reservoir performance. In this work, a dual-permeability model was applied to study the parameter sensitivity on the CBM production, because the dual-permeability model incorporates not only the influence from matrix and fractures but also that between adjacent matrix blocks. The mass exchange between two systems is defined as a function of desorption time constant at the standard condition, coal matrix porosity, and the difference of gas pressure between two systems. Correspondingly, gas diffusivity in matrix is considered as a variable and represented by a function of shape factor, gas desorption time, and reservoir pressure. These relations are integrated into a fully coupled numerical model of coal geomechanical deformation and gas desorption/gas flow in both systems. This numerical approach demonstrates the important nonlinear effects of the complex interaction between matrix and fractures on CBM production behaviors that cannot be recovered without rigorously incorporating geomechanical influences. This model was then used to investigate the sensitivity of CBM extraction behavior to different controlling factors, including gas desorption time constant, initial fracture permeability, fracture spacing, swelling capacity, desorption capacity, production pressure, and fracture and matrix porosities. Modeling results show that the peak magnitudes of gas-production rate increase with initial fracture permeability, sorption and swelling capacities, and matrix porosity, and decrease with gas desorption time constant and production pressure. These results also show dramatic increase in gas-production efficiency with decreasing magnitudes of fracture spacing. The comparison of the transient contributions of the desorbed gas and the free phase gas from the matrix system to gas production shows that the free phase gas plays the dominant role at the early stage, but diminishes when the adsorption phase gas takes over the dominant role, indicating the necessity of incorporating free phase gas impact in simulation models. The numerical model was also applied to match the history data from a gas-production well. A better matching result than that for the single-permeability model demonstrates the potential capability of the dual-permeability model for the forecast of CBM production.

Probabilistic forecasting of ionospheric scintillation and GNSS receiver signal tracking performance at high latitudes

<p>At high latitudes, phase scintillation occurs predominantly on the dayside in the ionospheric footprint of the magnetospheric cusp, and in the nightside auroral oval. A new technique of probabilistic forecasting of phase scintillation occurrence relative to the arrival time of high-speed solar wind from coronal holes and interplanetary coronal mass ejections has recently been proposed [Prikryl et al. 2012]. Cumulative probability distribution functions for the phase-scintillation occurrence that are obtained can be specified for low (below-median) and high (above-median) values of various solar wind plasma parameters. Recent advances in modeling of high-speed solar wind and coronal mass ejections, combined with the probabilistic forecasting of scintillation, will lead to improved operational space weather forecasting applications. Scintillation forecasting and mitigation techniques need to be developed to avoid potential costly failures of technology-based Global Navigation Satellite Systems in the near future, in particular during the upcoming solar maximum. The Global Navigation Satellite Systems receiver-tracking performance during severe scintillation conditions can be assessed by the analysis of receiver phase-locked-loop jitter. Tracking jitter maps [Sreeja et al. 2011] offers a potentially useful tool to provide users with expected tracking conditions, if based on scintillation predictions as proposed above. Scintillation indices are obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network. Combined with high rate amplitude and phase data, they can be used as input to receiver tracking models to develop scintillation mitigation techniques.</p>

What to expect when you are consolidating: effective prediction models of application performance on multicores

Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant. As opportunities for better system usage become ample, so are opportunities to degrade individual application performances due to unregulated performance interference between applications and system resources. Can we predict a performance region within which application performance is expected to lie under different consolidations? Alternatively, can we maximize resource utilization while maintaining individual application performance targets? In this work we provide a methodology that offers answers to the above difficult questions by constructing a queueing-theory based tool that can be used to accurately predict application scalability on multicores. The tool can also provide the optimal consolidation suggestions to maximize system resource utilization while meeting application performance targets. The proposed methodology is based on asymptotic analysis that can quickly provide a range of performance values that the user should expect under various consolidation scenarios. In addition, when more accurate performance forecasting is needed, the methodology can provide more accurate predictions using approximate mean value analysis. The methodology is light-weight as it relies on capturing application resource demands using standard system monitoring, via non-intrusive low-level measurements. We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm2008 benchmark suites. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of collocated applications with an average error below 9 per cent. Experimental and analytical results are in excellent agreement, confirming the robustness of the proposed methodology in suggesting the best consolidations that meet given performance objectives of individual applications while maximizing system resource utilization.

Study on Motion Simulation of HOV

It will be to bear several forces when Human Occupied Vehicle (HOV) moves underwater, So the movement performance forecast and research becomes more difficult when HOV under environment disturbances,this paper regards a certain HOV as the research object,researches the HOV actual situation, the comprehensive consideration of the hydrodynamic force, gravity, propeller thrust, environmental interference force for HOV motor effects, establishes the six degrees of freedom equations of motion for HOV, several typical motion states are studied , finally the simulation work is carried out, the simulation results can reflect the HOV motion characteristics , provide important basis for HOV motion control system design.

Modeling Style Rotation: Switching and Re-switching

Abstract: The purpose of this paper is to investigate the dynamics and statistics of style rotation based on the Barberis–Shleifer model of style switching. Investors in stocks regard the forecasting of style-relative performance, especially style rotation, as highly desirable but difficult to achieve in practice. Whilst we do not claim to be able to do this in an empirical sense, we do provide a theoretical framework for addressing these issues. We develop some new results from the Barberis–Shleifer model which allows us to understand some of the time series properties of styles’ relative performance and determine the statistical properties of the time until a switch between styles. In conclusion, we discuss potential applications of our findings to empirical data.

Risk-adjusted performance forecasting of future key technology

A new way of identifying future key technologies based on a risk-adjusted technology performance measure is suggested for policymakers. Current key technologies are identified by patent citation network analysis. Employing growth curve fitting, we forecast the future growth path of these technologies, and evaluate their risk-adjusted future performance. Overcoming the weaknesses of return-only and current-focused key technology identification, our method can better inform policymakers and experts about both the return and risk of future key technologies, helping them to make better decisions.

Thermal Performance Analyses of Heat Pipe of All-Glass Evacuated Tubular Solar Collectors with Properties of Pipe Materials

In this paper, the abio-superconductor heat pipe is used as heat transfer tube of all-glass of evacuated tubular solar collectors. Based on thermal analysis, the overall heat loss coefficient , the efficiency factor and the heat removal factor of superconductor heat pipe evacuated tubular collector are presented. They were compared with instantaneous efficiency between theory value of example and measure value of experiment. As a result, their opposite error less than 5.3%, it explained related calculation expression that it reflected preferably transmit heat function and characteristics of vacuum tube solar collector .It discussed how to prove collector performance and used for that type of vacuum tube of the design and the performance forecast. The heat pipe catheter contains a small amount of powder of the working medium, these media consists of a variety of inorganic elements. The properties of pipe materials can be in a very short time to achieve isothermal heat transfer, as its radial heat transfer capacity of 44 kW/m2, axial heat transfer capacity is 8600 kW/m2,heat pipe can transfer more compared with the normal.

An Efficient Framework to Derive Transaction Slicing with Performance Forecast for Enterprise Modernization

During legacy application to Enterprise Modernization, where the Web logs are not available and deriving Transaction slicing for the ‘n’ number of applications to forecast the Performance is extremely difficult. This involves proper well defined process flows to derive the characteristics of heterogeneous applications. The performance forecast devised has to be approved and stamped by key stakeholders during the Non Functional requirements (NFR) sign off phase itself in any Enterprise Modernization [1]. This Paper emphasizes on the process flows to derive Transaction slicing with performance forecast for Enterprise Modernization. General Terms Transaction Slicing, Performance Forecast, Enterprise Modernization.