Uncertainty In Efficiency Research Articles

Should a firm bring a supplier into the boardroom?

Endowed with significant firm‐specific knowledge, inside directors can contribute to the decision‐making processes of the boardroom. However, regulatory changes, focusing primarily on the monitoring function of the boards, have driven inside directors out of the boardroom. This article argues that suppliers with firm‐ and industry‐specific knowledge are uniquely positioned to fill a critical void in boardrooms. It also suggests that the value of having a supplier on the board (SOTB) is influenced by environmental contingencies faced by a firm: operational efficiency, diversification, and demand uncertainty. Using an objective measure of supplier presence in the boardroom, the authors find that a supplier's presence enhances firm performance. They also find that the value of an SOTB in enhancing performance is greater in firms with lower operational efficiency and higher demand uncertainty and, is lower in firms with higher diversification. These results are robust to potential endogeneity issues, alternative estimation methods, and measures of moderator and outcome variables.

Management efficiency uncertainty and its implications for bondholders

We empirically examine whether and how management efficiency uncertainty (MEU) affects the loss risks of bondholders with a theoretical application model. We find that a greater MEU increases the loss risks of bondholders by increasing a firm's assessed asset value volatility and incomplete information while management efficiency has the opposite effect by increasing the firm's profitability and decreasing financial leverage. Moreover, the MEU effect becomes weaker for firms with greater profitability. Finally, we further find that managers' state-contingent payoffs increase MEU and thus the loss risks of bondholders.

Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability

Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model.It is possible to calibrate the model using pile self-weight penetration, with an improved estimate of the probabilistic predictions. This is done with Markov Chain Monte Carlo simulations, to quantify the posterior probability of the parameters and finally to create a posterior predictive contour of the self-penetration model. This approach is used to repeat the back-analysis of pile driving into London Clay, with prior accounting of the inherent variability.

Compressor geometric uncertainty quantification under conditions from near choke to near stall

Geometric and working condition uncertainties are inevitable in a compressor, deviating the compressor performance from the design value. It’s necessary to explore the influence of geometric uncertainty on performance deviation under different working conditions. In this paper, the geometric uncertainty influences at near stall, peak efficiency, and near choke conditions under design speed and low speed are investigated. Firstly, manufacturing geometric uncertainties are analyzed. Next, correlation models between geometry and performance under different working conditions are constructed based on a neural network. Then the Shapley additive explanations (SHAP) method is introduced to explain the output of the neural network. Results show that under real manufacturing uncertainty, the efficiency deviation range is small under the near stall and peak efficiency conditions. However, under the near choke conditions, efficiency is highly sensitive to flow capacity changes caused by geometric uncertainty, leading to a significant increase in the efficiency deviation amplitude, up to a magnitude of −3.6%. Moreover, the tip leading-edge radius and tip thickness are two main factors affecting efficiency deviation. Therefore, to reduce efficiency uncertainty, a compressor should be avoided working near the choke condition, and the tolerances of the tip leading-edge radius and tip thickness should be strictly controlled.

Application of machine learning and Box-Behnken design in optimizing engine characteristics operated with a dual-fuel mode of algal biodiesel and waste-derived biogas

Waste-derived biogas and third-generation algal biodiesel are attractive alternative fuels to substitute fossil diesel in a diesel engine. However, using biodiesel as a pilot liquid fuel and biogas as the main fuel in a diesel engine is a complicated and highly non-linear process. The current study seeks to predict and optimize the combustion and exhaust emission characteristics of a variable compression dual-fuel combustion engine. Data from experiments were obtained at a variety of engine loads, compression ratios, pilot fuel injection pressures, and timings. A multi-layer perceptron network was employed to develop an Artificial Neural Network (ANN) based prognostic model using the experimental data. The developed prognostic model was used to estimate brake thermal efficiency, biogas flow rates, peak in-cylinder pressure, carbon dioxide, unburned hydrocarbons, oxides of nitrogen, and carbon monoxide. The predictive model's robustness is demonstrated by statistical metrics such as R (0.9723–0.988) and R2 (0.9453–0.9761), Nash-Sutcliffe model efficiency (94–97%), and mean absolute percentage error (0.013–0.128%), Kling-Gupta efficiency (0.9548–0.9836), and Theil's U2 model uncertainty (0.162–0.368). To optimize the parameters of dual-fuel combustion, the Multi-Output Response Surface Methodology (RSM) was employed. The trade-off assessment between emission and efficiency using the desirability approach revealed that 84% engine load, 244 bar of fuel injection pressure, 28 °BTDC of injection timing, and 17.5 compression ratio are the best-operating conditions for the test engine. An experimental investigation was used to corroborate the RSM research findings, and errors were less than 9%. It was revealed that ANN-linked RSM is a good hybrid technique for modeling, prediction, and optimization of the performance of a dual-fuel engine.

Bridging the knowledge gap between technology and business: An innovation strategy perspective.

Decision-makers (DMs) are not sufficiently exposed to concepts such as efficiency and risk in innovative activities from the perspective of organizational strategy. The challenges become even greater when these DMs lack expertise in technology and deal with uncertain circumstances. In this sense, exchanging expert knowledge between DMs and technical teams will strengthen the link between technology planning and strategic management. The purpose of this study is to bridge the knowledge gap between these two groups. It introduces a framework to translate the organization’s strategy into technological decisions at an acceptable innovation risk level. This framework considers aspects such as knowledge, type of innovation, and innovation process. This study focuses on determining whether activities should be accepted or rejected by examining the uncertainty and efficiency of innovation. It also introduces a novel perspective on the hybrid "success-failure" uncertainty of innovation, and a new measure called "efficiency probability," which DMs and technology developers can use to intuitively engage in the innovation process. This paper seeks to propose a practical strategy map for new product development under uncertain conditions. To achieve this goal, the Fuzzy Front-End (FFE) concept, fuzzy data envelopment analysis (FDEA) model, and adjustable possibilistic programming (APP) approach are applied. The results of this study indicate that innovative activities typically have low efficiency and high uncertainty. Therefore, the decision to implement or abandon them requires reviewing and balancing the goals and strategic approach of the organization with technological and business features.

Research on Optimal Operation Model of Virtual Electric Power Plant Considering Net-Zero Carbon Emission

“World Energy Outlook 2021” has mentioned the fact that with the current pledges announced, the 2050 net-zero carbon emission target would not be realized. To further improve energy efficiency, energy integration will be important. Therefore, this paper introduced virtual power plant (VPP) and power to gas (P2G) technology to analyze the improvement of energy integration. Firstly, the structure of VPP connected with P2G is proposed, and the physical output model is constructed. Secondly, combined with carbon emission and economic operation objectives, a multi-objective operation optimization model of VPP considering electrical interconnection is constructed, and the solution idea of the model is put forward. Finally, through the case study, the contribution of P2G, DR and GST is proven. With DR, P2G involved in VPP, the goal of carbon emission reduction can be achieved. In addition, the example also proves that carbon trading has a positive effect on energy efficiency and generation uncertainty improving.

Covariance analysis of efficiency calibration of NaI(Tl) detector

In this work, the efficiency of NaI(Tl) detector is analyzed using standard γ-ray sources namely 22Na, 137Cs, 60Co and 133Ba. The error in experimental observations, obtained by gamma ray spectrometry, are estimated by considering partial uncertainties in attributes such as γ-ray abundance, γ-ray counting of source, half-life of the radioactive source nuclide and source activity, that influence the detector's efficiency towards specific gamma energy. Information of these attributes are accounted for in the propagation of error and the covariance matrix is generated by considering the correlations among the attributes and their micro-correlations. Covariance matrices corresponding to each source, provide information about net uncertainty in detector efficiency towards specific photon energy. The detector efficiencies for the sources 22Na (1.274 MeV), 137Cs (0.662 MeV), 60Co (1.173 MeV) and 133Ba (0.356 MeV) are evaluated to be 0.02822 ± 0.00031, 0.00149 ± 0.00015, 0.00197 ± 0.00012 and 0.00068 ± 0.00002 respectively. Precise estimation of detector efficiency is essential in measurements that involve several observed variables. Hence, proper analysis of covariance among attributes and evaluation of uncertainty in detector efficiency is necessary to assess the quality of results.

Trade policy uncertainty and improvement in energy efficiency: Empirical evidence from prefecture-level cities in China

In the face of the shocks from trade policy uncertainty (TPU), China needs to improve urban energy efficiency (EE) to achieve its “carbon neutrality” goals as scheduled, but the existing literature has no systematic research on this topic. This paper develops and estimates TPU and EE indexes by adopting the Bartik instrument's methods and dynamic slacks-based measure (DSBM). Furthermore, the authors conduct empirical tests on the influencing effect and transmission mechanism of falling TPU on urban EE after the US granted China permanent normal trade relations (PNTR) status in 2001 with a difference-in-difference (DID) model. The results of the empirical study show that urban EE improves by around 1.9% when TPU falls by 1%. A heterogeneity analysis also shows that a falling TPU has a more significant impact on EE in provincial capitals, open cities, “hygienic cities,” highly marketized cities, and cities with a higher share of manufacturing. Falling TPU reduces the cost of foreign capital inflow into China. It drives progress in green technologies, thus improving urban EE by replacing energy factors with innovation-driven non-energy factors. Further, export expansion and export technology complexity significantly raise the stimulating impact of falling TPU on improvement in EE, but growing processing trade impedes this impact. The findings in this study offer a new perspective on estimating the effect of China's policies to respond to global climate change against the shock from TPU and provides new evidence for strengthening global energy cooperation.

Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

The lithofacies classification is essential for oil and gas reservoir exploration and development. The traditional method of lithofacies classification is based on “core calibration logging” and the experience of geologists. This approach has strong subjectivity, low efficiency, and high uncertainty. This uncertainty may be one of the key factors affecting the results of 3D modeling of tight sandstone reservoirs. In recent years, deep learning, which is a cutting-edge artificial intelligence technology, has attracted attention from various fields. However, the study of deep-learning techniques in the field of lithofacies classification has not been sufficient. Therefore, this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks (CNN) and the excellent ability to describe time-dependent features of long short-term memory networks (LSTM) to conduct lithological facies-classification experiments. The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3% and the best classification effect compared to the CNN, LSTM or the three commonly used machine learning models (Support vector machine, random forest, and gradient boosting decision tree). In addition, the borderline synthetic minority oversampling technique (BSMOTE) is introduced to address the class-imbalance issue of raw data. The results show that processed data balance can significantly improve the accuracy of lithofacies classification. Beside that, based on the fine lithofacies constraints, the sequential indicator simulation method is used to establish a three-dimensional lithofacies model, which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area. According to this comprehensive analysis, the proposed CNN-LSTM model, which eliminates class imbalance, can be effectively applied to lithofacies classification, and is expected to improve the reality of the geological model for the tight sandstone reservoirs.

Design diversity for efficiency improvement and uncertainty management in multiple wells stimulation

The history of oil and gas well stimulation through hydraulic fracturing is characterized by a pursuit of optimal designs tailored to reservoir properties. However, as with many engineering systems, the impact of variability and uncertainty (in this case of reservoir properties) is not accounted in these deterministic approaches. We propose an expansion of the principle of design diversity wherein the focus becomes developing optimal portfolio combinations of multiple designs rather than repeated application of a single design. This approach can substantially increase return and decrease risk when designing systems where outcome desirability is definable as a smoothly-varying return and where risk is related to the variance of outcomes tied to uncertain spatio-temporal variability of the design environment. For the case of oil/gas wells, diversified designs of portfolios can improve 60% more stimulation efficiency and halve the corresponding risk. Hydraulic fracturing optimization is therefore fundamentally dependent upon uncertainty, and a change of approach toward the pursuit of design diversity can have a profound impact on efficiency and risk, and in turn cost and environmental impacts, in oil and gas development.

Uncertainty assessment for the airborne nanoparticle collection efficiency of a TEM grid-equipped sampling system by Monte-Carlo calculation

A mini particle sampler (MPS) equipped with a transmission electron microscopy (TEM) grid enables convenient particle sampling to subsequent analysis. However, its sampling efficiency involves uncertainties, and accurate sampling efficiency is required for particle collection applications. In this study, the sampling efficiency uncertainties from measured data and models are quantified using Monte-Carlo methods. The Sobol variance-based sensitivity analysis is used to determine the contributions of parameters to the sampling efficiency uncertainties. The results reveal that the sampling efficiency uncertainties from experimental dispersion calibration and theoretical models are generally less than 1% and 9%, respectively. Most sampling efficiency measured data are covered by the efficiency uncertainty range simulated by theoretical models. Pore size and flowrate contribute significantly to the sampling efficiency uncertainties and require control to improve the sampling efficiency precision. Besides, the Cunningham correction factor is also a sensitivity parameter. The utilization of proper models is crucial to support simulations for further process optimization. This study offers a quantitative method for nanoparticle collection efficiency analysis, which will help assess nanomaterials’ workplace exposure.

Combining Sequential Gaussian Simulation and Fractal Analysis for Mapping of Cu Concentration in Haftcheshmeh Porphyry Copper Deposit

In this paper a hybrid approach of Sequential Gaussian Simulation (SGSIM) and Concentration-Volume (C-V) fractal method was applied to boreholes data for classification of major geochemical parameters and associated features with alteration zones of the Haftcheshmeh Cu deposit in NW of Iran. Thereupon, Cu parameters were detected with higher efficiency and lower uncertainty. The purpose is also extended to delineate the alteration zones pertinent to Cu mineralization. Firstly, the most straightforward simulation (SGSIM) was utilized for projecting different lithogeochemical parameters of Cu. Then the C-V fractal model was used to discriminate these parameters by thresholds, obtained from the C-V Log-Log plot. The Fractal based resulting maps of 10 realizations and their E-type indicate their association with potassic alteration that has imposed on porphyry granodiorite. Moreover, these maps illustrate that the boreholes (1, 9, 23, and 31) at about longitudes of 643400 to 643800 are more promising than others. This fact explicitly had been correlated with reality of the studied area and denoted in its primary surface map. The results based on SGSIM, C-V confirmed enhanced mineralization in three-dimensional maps of the Haftcheshmeh deposit as a powerful combined method that can be used to detect the similar ore zones in continuation of the ore roots in adjacent areas.

Uncertainty and risks in the economic efficiency of protected ground vegetable production

The development of protected soil is one of the priorities of state policy in the agricultural sector of the economy. By the end of 2020, it was planned to meet the needs of the national market in cucumbers and 70-80% in tomatoes. Growing vegetables is associated with a huge number of factors that have both positive and negative effects in the form of economic, political, natural, demographic, organizational, technical and other risks. At the planning stage, so-called preparatory risks are possible, followed by construction, environmental and production risks that lead to higher project costs, increased capital investment and operating costs. In modern conditions, production risks are particularly high. Therefore, production activities have become the most risky. The description of the most characteristic signs of risks and possible ways to manage them is given. It is important to identify risks in a timely manner and take appropriate measures. Risk management is possible at the state and enterprise levels.

Long-Term Sustainability Approach in Road Traffic Noise Wall Design

Despite the long-term experience in the application of noise walls, the uncertainty in wall panel service life efficiency is almost equal between panels built from established and new materials, which are—because of the desire to increase the sustainability of noise walls—developing at an ever-faster pace. The presented meta-analysis of data collected during a systematic review of concrete, metal, and wood panels’ acoustic and non-acoustic characteristics, long term performance, and cradle-to-gate sustainability aims to reduce this uncertainty and support the process of noise wall design and management by shifting the emphasis in decision making from construction costs to the long-term sustainability of the road traffic noise mitigation project. The multi-criterial analysis showed that when choosing a panel, preference should be given to those using lightweight concrete materials. A further comprehensive cradle-to-grave assessment of lightweight concrete panels with expanded clay and recycled tire rubber aggregates, which was performed to fill a knowledge gap observed in the literature and identify opportunities for the improvement of lightweight concrete sustainability, showed that the main environmental impacts of these panels are due to their production processes and that the way to reduce such impacts is to use panels made with aggregates from secondary raw materials.

Optimum Utilisation of CuO Nanofluid in Flat Plate Solar Collector

The optimum utilisation of CuO-nanofluid in flat plate solar collector has been investigated under Malaysian climatic condition. To determine the optimum nanoparticle concentration required in the base fluid, a simulation was carried out using MATLAB program. From the simulation, it was found that, 0.5 vol.% of CuO nanoparticles in the base fluid yielded maximum collector efficiency. The test was conducted over six months following the ASHRAE standard with nanofluid in the flat plate collector to ascertain its efficiency. The maximum average solar radiation incident on the collector, collector outlet and ambient temperatures were observed about 1000 W/m2, 50 ºC and 38 ºC respectively. From the efficiency curve, the absorbed and removed energy parameters were found to be 0.501 and 24.23 respectively. At a mass flow rate of one litre per minute, the maximum average instantaneous efficiency was 51%. The result of experimental efficiency was compared with the result of simulation and the efficiency values were within 4% of each other. CuO nanofluid base collector increases the efficiency compared to water as the collector fluid. The experimental results revealed that the efficiency of FPSC with CuO nanofluid was 4.78% higher than water base collector at the same mass flow rate of 1 L/min. The uncertainty analysis of result has shown that instantaneous efficiency uncertainty was about 3.3%. The simulation result has indeed minimised number of experiments required to determine the optimum concentration of nanofluid for maximum efficiency.

Absorption Efficiency Assessment and Uncertainty Measurement of the Sodium Arsenite Method for Ambient NO2 Determination

The absorption efficiency and uncertainty involved in the sodium arsenite method (modified Jacobs and Hochheiser method) of NO2 measurement were studied using two standard gas concentrations (4.86 ± 0.10 µmol mol–1 (ppm) and 1.95 ± 0.10 µmol mol–1 (ppm)). An average of 71 ± 3% and 71 ± 6% absorption efficiencies, respectively were observed against reported efficiency of 82% in the published standard (IS 5182 (Part 6): 2006, BIS, 2006a) in 1-impinger system at sampling flow rate of 1 L min–1 and absorbing solution temperature of 10–15°C during gas absorption in impinger. The difference is accounted for by the variation in flow rate used in present study (1 L min–1) and that in standard method (0.2 L min–1). Uncertainty involved in the method was calculated considering a range of factors directly or indirectly affecting the measurement and was found to be 15%. Based on the comparison study performed for NO2 ambient measurements using an equivalent method (chemiluminescence continuous method) and 1-impinger system reference method, the present study highlights the importance of accurate determination of absorption efficiency factor in the NO2 pollutant measurement at laboratory level. Variations observed in the absorption efficiency and the data from inter-comparison study between manual and real-time method warranted this study to be taken up to provide solutions for improvement of the method. The use of at least three absorbers/impingers (connected in series) instead of one to obtain absorption efficiency close to unity is proposed to make the reference method independent from the use of absorption efficiency factor thus ensuring the improved data quality and comparability.

Research on Testing Equipment for Anti-impact Performance of Building Guardrails

The detection status and existing problems of the anti-soft and heavy object impact performance of glass and metal guardrails for buildings were briefly described in the article. A set of detection equipment was designed for the shortcomings, which mainly included guardrail installation devices, height positioning devices, and impact control systems. It has the advantages of small size, low cost, simple operation, high efficiency and low uncertainty, etc. In addition, the influence of factors such as installation quality and test environment on the test results can be excluded, which greatly improves the accuracy and fairness of the test results.

Uncertainty and the marginal revenue product–wage gap

The presence of uncertainty and risk aversion compounds the effects of labor market imperfections and thereby widens the marginal revenue product of labor (MRPL) and wage gap. This paper analyzes the case of uncertainty in labor efficiency or job separation and its implications for managerial decisions. We show that when labor efficiency is subject to stochastic fluctuations, risk aversion leads to a gap between the MRPL and the wage even under conditions of perfect competition. If job separation costs are stochastic, the gap is due to monopsony power and the influence of risk aversion.

Uncertainty evaluation of efficiency measurement in laboratory conditions

Uncertainty is a parameter associated with the result of a measurement, which characterizes the dispersion of the values that could reasonably be attributed to the measurand. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as standard deviation or uncertainty band.The identification of uncertainty of individual sensors and standardization in calibration process is under progress in Turbine Testing Lab (TTL) at Kathmandu University (KU). The lab aims to establish a Francis turbine test rig and procedures that meet the standards set by International Electrotechnical Commission (IEC). This needs a major investment on calibration systems and development of procedures to estimate the uncertainty in measurement more precisely than ever before.This study aims to identify the sources of uncertainty and their quantification for efficiency measurement systems at laboratory condition. The study is done on the 14 kW Francis turbine project at TTL, KU. Based on different published papers regarding uncertainties, type A and type B uncertainties were evaluated separately and then combined to get combined uncertainty of each components. The uncertainty in efficiency was then calculated using the rules of uncertainty calculation.In this paper, the standard method is illustrated to evaluate the uncertainties in sensor-based measurement system. The same method is used in investigation of uncertainty to the extent it was possible and feasible in the lab i.e. study could be mainly focused only on torque transducer. The value of efficiency at 1300 rpm is calculated to be 66.7% ± 0.35%, where ± 0.35% represents the uncertainty in efficiency for the same rpm value. The results indicate uncertainty level unforeseen close to meet the IEC standards which could be result of including only parts of uncertainties. All uncertainties should be evaluated for future research papers.