Choice Of Inputs Research Articles

Communicating Nitrogen Loss Mechanisms for Improving Nitrogen Use Efficiency Management, Focused on Global Wheat

Nitrogen (N) losses are a major environmental issue. Globally, crop N fertilizer applications are excessive, and N use efficiency (NUE) is low. N loss represents a significant economic loss to the farmer. NUE is difficult to quantify in real time because of the multiple chemical–biological–physical factors interacting. While there is much scientific understanding of N interactions in the plant–soil system, there is little formal expression of scientific knowledge in farm practice. The objective of this study was to clearly define the factors controlling NUE in wheat production, focusing on N inputs, flows, transformations, and outputs from the plant–soil system. A series of focus groups were conducted with professional agronomists and industry experts, and their technical information was considered alongside a structured literature review. To express this understanding, clear graphical representations are provided in the text. The analysis of the NUE processes revealed 16 management interventions which could be prioritized to increase farm nitrogen use efficiency. These management interventions were grouped into three categories—inputs, flow between pools, and outputs—and include management options through the range of application errors, fertilizer input choice, root development, pests and disease, soil structure, harvesting and storage errors, and soil resources of water, micronutrients, carbon, nitrogen, and pH. It was noted that technical solutions such as fertilizer formulation and managing organic matter require significant supply chain upgrades. It was also noted that farm-scale decision support would be best managed using a risk/probability-based recommender system rather than generic guidelines.

A Generalized Predictive Controller for a Wind Turbine Providing Frequency Support for a Microgrid

The power system is moving away from the centralized generation paradigm. One of the current trends is the microgrid concept, where loads, small generators and renewable energy resources (RERs) that are in close proximity are controlled as one entity. Microgrids also allow for an increase in power availability as they can continue to supply electric power to loads even in the absence of a connection to the main grid. During the transition to islanded operation, microgrids may be subject to frequency disturbances caused by the power imbalance between load and generation. When microgrids contain high shares of renewable energy, the challenge is significantly higher due to the control strategies that aim to maximize power production, which are typically applied to RERs and render them insensitive to grid changes. Therefore, new control strategies need to be developed to enable the participation of RERs in the support of the frequency response. This work proposes a predictive control strategy that is based on a generalized predictive controller (GPC) being applied to the grid side converter of a doubly fed induction generator (DFIG) wind turbine to enable frequency support capabilities. The control objective was to track a time varying power reference signal that was generated according to the deviation from the nominal frequency, thereby enabling the energy storage device to inject power into the microgrid without a communication system. The GPC is a controller belonging to the family of model predictive controllers (MPCs), the main principles of which are the use of a system model to predict future states and the choice of an optimal input to ensure that the reference values are followed. To validate the proposed control strategy, a microgrid was simulated in MATLAB Simscape Electrical. The frequency response using the proposed GPC strategy was compared to another MPC-based strategy, known as finite control set, and a scenario in which the DFIG was not equipped with frequency support capabilities. The results show that the proposed strategy was able to improve the frequency response of the microgrid, reduce frequency oscillations and increase the value of the frequency nadir.

Ambiguities from Nuclear Interactions in the \(^{12}\)C(p,2p)\(^{11}\)B Reaction

We investigate the impact of ambiguities coming from the choice of optical potentials and nucleon-nucleon scattering cross sections on the spectroscopic factors extracted from the 12C(p,2p)11B reaction. These ambiguities are evaluated by analyzing the cross sections of the 12C(p,2p)11B reaction at 100 and 200 MeV within the framework of the distorted-wave impulse approximation with realistic choices of nuclear inputs. The results show that the studied ambiguities are considerably large in this energy region and careful choices of nuclear inputs used in the reaction calculations are required to extract reliable structure information.

Asynchronous Sessions with Input Races

We propose a calculus for asynchronous multiparty sessions where input choices with different senders are allowed in processes. We present a type system that accepts such input races provided they do not hinder lock-freedom.

Influence of Flow and Pressure of Carburising Mixture on Low-Pressure Carburising Process Efficiency

Low-pressure carburising (LPC) of steel is an industrially accepted method for improving the properties of a steel surface. LPC is environmentally friendly, does not cause intergranular oxidation and consumes less energy. Its effectiveness depends on the correct choice of process inputs. This paper aims to determine the effect of this type of carboniferous gas, pressure and flow rate on the efficiency of carbon transfer to the surface layer under low-pressure carburisation. A total of 40 disks of 16MnCr5 steel were carburised using pure acetylene or a mixture of acetylene, ethylene and hydrogen as a carboniferous gas, pressures of 2 or 6 hPa and two gas flow rates. The specimens were gravimetrically tested for the increase in the mass of carbon in the carburised layer. The results were analysed with U Mann–Whitney analysis and t-Student test. It was evidenced that carburising with pure acetylene resulted in a higher increase in carbon mass than carburising with the mixture (p < 0.05). Pressure and gas flow rates are important for carburising efficiency (p < 0.05).

Comparative life cycle assessment of natural gas and coal-based directly reduced iron (DRI) production: A case study for India

The energy-intensive steel industry is responsible for >7% of global greenhouse gas (GHG) emissions, of which the process of ironmaking contributes a large majority. Direct Reduced Iron (DRI) manufacturing, a growing technology in ironmaking, offers the potential to reduce emissions. India, as the largest manufacturer of DRI, is characterized by technological diversity, while simultaneously dominated by coal-based DRI processes. This study develops a comprehensive cradle-to-gate (raw material to iron product) life cycle assessment of three distinct technological pathways for producing DRI: direct coal reduction in a rotary kiln (rotary kiln DRI process), coal gasifier and shaft furnace (coal gasifier DRI process), and reformed natural gas and shaft furnace (NG reformer DRI process). A hybrid life cycle inventory approach is employed, involving process modeling, literature data, and industry consultation to establish baseline process metrics, followed by a sensitivity analysis to address the impact of variabilities and uncertainties. Depending on the supply chain and process assumptions, emissions attribution approach to multi-product systems, and the specific choice of inputs and outputs, the life cycle GHG emissions associated with DRI production range from 1391.1 to 1880.0 kgCO2e/t-DRI for the rotary kiln DRI process, 1565.5 to 1969.1 kgCO2e/t-DRI for the coal gasifier DRI process, and 815.3 to 1160.1 kgCO2e/t-DRI for the NG reformer DRI process. While the research considers India as a case study, the methodology and results can be directionally and broadly applicable to other geographies and process conditions.

Convex non-parametric least squares, causal structures and productivity

In this paper we consider Convex Nonparametric Least Squares (CNLS) when productivity is introduced. In modern treatments of production function estimation, the issue has gained great importance as when productivity shocks are known to the producers, input choices are endogenous and estimators of production function parameters become inconsistent. As CNLS has excellent properties in terms of approximating arbitrary monotone concave functions, we use it, along with flexible formulations of productivity, to estimate inefficiency and productivity growth in Chilean manufacturing plants. Inefficiency and productivity dynamics are explored in some detail along with marginal effects of contextual variables on productivity growth, inputs, and output. Additionally, we examine the causal structure between inefficiency and productivity as well as model validity based on a causal deconfounding approach. Unlike the Cobb-Douglas and translog production functions, the CNLS system is found to admit a causal interpretation.

Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs

In hydrological modelling, artificial neural network (ANN) models have been popular in the scientific community for at least two decades. The current paper focuses on short-term streamflow forecasting, 1 to 7 days ahead, using an ANN model in two northeastern American watersheds, the Androscoggin and Susquehanna. A virtual modelling environment is implemented, where data used to train and validate the ANN model were generated using a deterministic distributed model over 16 summers (2000–2015). To examine how input variables affect forecast accuracy, we compared streamflow forecasts from the ANN model using four different sets of inputs characterizing the watershed state—surface soil moisture, deep soil moisture, observed streamflow the day before the forecast, and surface soil moisture along with antecedent observed streamflow. We found that the best choice of inputs consists of combining surface soil moisture with observed streamflow for the two watersheds under study. Moreover, to examine how the spatial distribution of input variables affects forecast accuracy, we compared streamflow forecasts from the ANN using surface soil moisture at three spatial distributions—global, fully distributed, and single pixel-based—for the Androscoggin watershed. We show that model performance was similar for both the global and fully distributed representation of soil moisture; however, both models surpass the single pixel-based models. Future work includes evaluating the developed ANN model with real observations, quantified in situ or remotely sensed.

Multimodel quantification of green and blue water components and its error propagations through parameter transferability approach across input choices

A robust quantitative assessment of water security through efficient water accounting concepts like blue and green water footprints has gained a lot of attention for water resources management at river basin scale. However, such water accounting tools and models have dependencies to the choice of precipitation data used to drive the model, as the input bias propagate into simulated streamflow through interaction with different hydrological processes. This study performs a holistic investigation of sensitivity of water accounting fractions like blue water flow (BWF), green water flow (GWF), green water storage (GWS), and their dependencies on secondary precipitation datasets (SPDs) choices using a lumped conceptual rainfall-runoff model [Hydrological Simulation model (HYSIM)] and a physically based semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] in the Damodar River basin, India. These models were driven by seven gridded precipitation datasets, including India Meteorological Department (IMD) data, Indian Monsoon Data Assimilation and Analysis (IMDAA) data, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, Watch forcing ERA-Interim data, Climate Hazard Group Infrared Precipitation with Station data (CHIRPS), PRINCETON precipitation data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Records (PERSIANN-CDR) data. The reference IMD based multimodel results revealed that the annual average blue and green water components of the Damoder River were in the range of 558–656 mm/year, 574–728 mm/year and 34–112 mm/year for BWF, GWF, and GWS respectively during 1994–2010 period. The results obtained after applying parameter transferability approach across the input space have revealed exact quantification of choice dependent annual variations in spatial and temporal characteristics of BWF, GWF, and GWS. A further evaluation based on error propagation ratios (γCC, γrRMSE, γB) has revealed a quantification of input choice induced dampening/magnification effects on flow estimations of both the HYSIM and SWAT and these quantifications were free from the compensation effects of recalibration. The findings in this study demonstrate that the change in annual average BWF varies between −46.97% to +41.38% in comparison to benchmark IMD simulations, and the corresponding changes for GWF is between −19.63% and +17.06% and for GWS is between −29.97% to +35.5% with different choices of SPD scenarios. The systematic (γB) and random (γrRMSE) error propagation factors during monsoon season are larger for SWAT model ([0.21–0.78], [0.69–0.89] respectively) than that of HYSIM model ([0.18–0.74], [0.66–0.81], respectively) and it is varying with choice of different SPDs.

Sourcing decision under interconnected risks: an application of mean–variance preferences approach

Supply chains are customarily associated with multiple interconnected risks originated from supply side, demand side, or from the unanticipated background uncertainties faced by a firm. Also, effective functioning of supply chain hinges on sourcing decisions of inputs (raw materials). Therefore, there is a striking need to analyse the risk preference of the decision maker while going for optimal sourcing decision under varying degree of interconnected supply chain risks. This study addresses this issue by analysing the comparative static effects under interconnected supply chain risks for a risk averse decision-maker, manufacturing and selling products in a regulated market under perfect competition. The decision-maker faces not only supply-side risk (due to random input material prices) but also interconnected risks arising out of background risk (setup costs risk) and demand-side risk (output prices risk). With preferences defined over the mean and standard deviation of the uncertain final profit, this study illustrates the effects of the changes in the pairwise correlations between the three above mentioned risks on the optimum input choice of the manufacturer. To contextualise this study, an India-based generic drug manufacturer cum seller has been considered as a case in the parametric example of our model. Adaptation of the mean–variance framework helps obtaining all the results in terms of the relative trade-off between risk and return, with simple yet intuitive interpretations.

Структура оценки качества менеджмента российских банков

The profitability and sustainability of the bank in the long term is largely determined by the quality of its management. Recognition of the importance of this factor is, for example, its inclusion as one of the components in the CAMELS rating system, which has not lost its popularity so far. At the same time, a direct quantitative assessment of the quality of management is poorly formalized and still represents a serious creative task. In this regard, an indirect assessment of this factor based on data envelopment analysis (DEA) has become popular. It allows you to build a production boundary for organizations implementing a technological process, the input of which receives several types of resources, and the product is also a multidimensional quantity. The distance from the point representing the organization to the production boundary determines the magnitude of its inefficiency and can be considered as a metric characterizing the quali­ty of management. The specification of the DEA model - the choice of input and output indicators - reflects the definition by the expert of the concept of the effectiveness of the organization and significantly affects the value of its assessment. Different specifications can lead to conflicting estimates of efficiency and, consequently, the quality of management.In this study, for a fixed set of potential inputs and outputs, an assessment of the overall efficiency of banks is constructed, which accumulates the properties of estimates of partial efficiencies obtained for specific specifications of DEA models. An increase in the value of this estimate is associated with an increase in the values of estimates of partial efficiencies and vice versa. Thus, the resulting metric can act as an assessment of the quality of management of banks for a variety of possible definitions of their effectiveness. The overall efficiency metric allows banks to be ranked regardless of the specific specification of the DEA model, even at the production boundary, where all banks have the same maximum efficiency value. In addition, the study obtained additional metrics that allow analyzing the strategy for improving the efficiency (quality of management) for each bank. Each metric corresponds to a unique strategy. The number of variants of potential specifications of DEA models and the number of additional metrics introduced determines the depth of analysis. Using these metrics, you can get an answer to the question of exactly how a particular bank has reached the current level of overall efficiency. The authors also propose a method for comparing different DEA models based on the analysis of the relationship of the corresponding private efficiency with the metrics of overall efficiency and metrics of strategies to improve it. Quantitative results were obtained for a sample of open statements of medium-sized Russian banks that completed the reporting period without losses.

Investment Tax Credits and the Response of Firms

This paper studies the effects of investment tax credits on firms' input choices by exploitinga sudden shift in the tax credit rate by firm size for manufacturing firms in Germany in 1999. I find that more generous tax credits lead to a significant increase in both investment and employment, with implied elasticities with respect to capital costs of 2.8 and 1.1, respectively. Local spillovers between firms generate an additional positive effect. The employment effect is due to the increased hiring of new employees rather than a decrease in separations, with direct flows out of unemployment constituting about half of the inflow of workers. A heterogeneity analysis reveals that firms with larger capital cost shares are more responsive to tax credits and that spillovers tend to be stronger for firms operating in the same industry. While there is little evidence that the average firm adjusts its skill mix or occupational structure, firms in industries with higher investment shares into information and communications technology (ICT) are more likely to shift towards highly educated labor and high-skilled occupations.

Robust input redundancy for uncertain systems

For input redundant linear systems, it is possible to construct distinct inputs producing identical outputs, from the same initial state. Thus selecting an output does not determine uniquely the input, i.e., there exist degrees of freedom related to the choice of inputs to generate a given output. These degrees of freedom can be used to enhance the system performances without altering the output. However their determination might depend on system parameters. When some of them are not known or uncertain, it may be difficult or impossible to design distinct inputs leading to the same output. Considering uncertainties in linear systems, this paper determines if such degrees of freedom exist independently from the uncertain parameters and, in this case, provides a methodology to compute these inputs. This corresponds to the new concept of robust input redundancy, which is defined and characterized in this paper.

Can One Hear a Matrix? Recovering a Real Symmetric Matrix from Its Spectral Data

The spectrum of a real and symmetric Ntimes N matrix determines the matrix up to unitary equivalence. More spectral data is needed together with some sign indicators to remove the unitary ambiguities. In the first part of this work, we specify the spectral and sign information required for a unique reconstruction of general matrices. More specifically, the spectral information consists of the spectra of the N nested main minors of the original matrix of the sizes 1,2,ldots ,N. However, due to the complicated nature of the required sign data, improvements are needed in order to make the reconstruction procedure feasible. With this in mind, the second part is restricted to banded matrices where the amount of spectral data exceeds the number of the unknown matrix entries. It is shown that one can take advantage of this redundancy to guarantee unique reconstruction of generic matrices; in other words, this subset of matrices is open, dense and of full measure in the set of real, symmetric and banded matrices. It is shown that one can optimize the ratio between redundancy and genericity by using the freedom of choice of the spectral information input. We demonstrate our constructions in detail for pentadiagonal matrices.

Stit-LOGIC FOR IMAGINATION EPISODES WITH VOLUNTARY INPUT

AbstractFrancesco Berto proposed a logic for imaginative episodes. The logic establishes certain (in)validities concerning episodic imagination. They are not all equally plausible as principles of episodic imagination. The logic also does not model that the initial input of an imaginative episode is deliberately chosen.Stit-imagination logic models the imagining agent’s deliberate choice of the content of their imagining. However, the logic does not model the episodic nature of imagination. The present paper combines the two logics, thereby modelling imaginative episodes with deliberately chosen initial input. We use a combination ofstit-imagination logic and a content-sensitive variably strict conditional à la Berto, for which we give a Chellas–Segerberg semantics. The proposed semantics has the following advantages over Berto’s: (i) we model thedeliberate choiceof initial input of imaginative episodes (in a multi-agent setting), (ii) we showframe correspondencesfor axiomatic analogues of Berto’s validities, which (iii) allows thepossibility to disregard axiomsthat might be considered not plausible as principles concerning imaginative episodes. We do not take a definite stance on whether theseshouldbe disregarded but give reasons for why one might want to disregard them. Finally, we compare our semantics briefly with recent work, which aims to model voluntary imagination, and argue that our semantics models different aspects.

Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities

The share of photovoltaic energy is more and more increasing in the World energy mix; the intermittence of this production makes difficult to maintain the stability of the electricity grid and the balance production-consumption. Predicting in advance the solar production facilitates the operation of the grid manager. This paper aims to forecast hourly global solar irradiation for time horizons from h+1 to h+6, using two approaches: multiple linear regression (MLR) and artificial neural network (ANN) models. The choice of inputs in these models is crucial for a good prediction and is investigated here; generally, only endogenous data are used (global solar irradiation), the addition of exogenous ones often improves the accuracy (ambient temperature, humidity, pressure and differential pressure) but they are not always available; the introduction of ordinal variables is studied: four ordinal variables allow to introduce the double seasonality of solar irradiation. The performances of two forecasting models (linear and nonlinear models) with combinations of endogenous, exogenous and ordinal variables are compared on two Algerian sites with different meteorological variabilities. It appears that adding ordinal variables to endogenous data decreases the nRMSE values and enables to reach the same level of reliability than adding exogenous variables while simplifying the implementation. This addition as inputs in ANN models decreased nRMSE by 0.45–1.65% points (2.6–6.2%) for Algiers and by 0.2–0.3% point (1–3%) for Ghardaïa according to the forecasting horizon.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis.

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Forecasting Bitcoin with technical analysis: A not-so-random forest?

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.

DISPERSION IN INPUT AND OUTPUT PRICES: A FIRM-LEVEL ANALYSIS

Analyzing representative and rich data on the Indian formal manufacturing sector, this paper tries to establish an empirical relationship between prices of input, output and firm size of the plant. The firm-level data reflect tremendous dispersion in prices that firms pay to purchase material input even within an industry. Price heterogeneity is also observed for prices that firms charge for their output even for narrowly defined products. The paper constructs a model using information on output and input choices by firms to analyze the observed patterns. The paper finds that on average, bigger plants not only pay a premium for the inputs used in the production process, but also charge a premium for their outputs. After documenting the empirical relationship, the paper discusses the possible sources for price dispersion. The paper highlights sectoral variations in the correlation between plant size and prices, which are consistent with the findings in the literature related to the scope for quality differentiation in output as well as inputs. The empirical pattern observed supports the hypothesis that correlation between plant size and price can be driven by market power.

Estimating the Production Function Under Input Market Frictions

Abstract Behind many production function estimators lies a crucial assumption that the firm's choice of intermediate inputs depends only on observed choices of other inputs and on unobserved productivity. This assumption fails when market frictions distort the firm's input choices. I derive a test for the assumption, which is rejected in several industries. Using weak identification asymptotics, I show that when the assumption fails, a simplified dynamic panel estimator can be used instead of choice-based methods because it requires choices to be distorted. I propose criteria for choosing between estimators, which in simulations yield lower error than either estimator alone.