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Highlights: [•] The waste generated by the suppliers of the Spanish paper industry is estimated. [•] An Economic Input Output Life Cycle Assessment Models (IO-LCA) is employed. [•] No direct relationship between impact on output and on waste generation was found. [•] The major contributors to waste generation were mining and forestry. [ABSTRACT FROM AUTHOR]

Abstract: Carbon capture and storage (CCS) is one of a suite of technology options that might play a significant role in reducing greenhouse gas emissions. However, outside of traditional enhanced oil and gas recovery operations with a well established business model, CCS project deployments are struggling with adoption of a federal or international climate policy driver appearing unlikely for the foreseeable future. As part of a feasibility study for an industrial CCS project at the Boise White Paper mill in Washington State, a business model and CCS system design was developed that provided financial surety for the project developers while recognizing that uncertainty in revenue forecasts for CO2 storage would persist for the foreseeable future. Key to the business model was installation of a new 37MWe biomass-fueled power island at the plant that would replace antiquated boilers and that could still supply the necessary steam to run pulp and paper production operations when capturing CO2 emissions under favorable market conditions for monetizing CO2 storage credits. Under unfavorable market conditions, CO2 capture would be suspended and excess power generated from the plant would reduce external electrical energy purchases required for paper mill operations. The net CO2 reduction for the project versus current operations is 1.0MMT of CO2 per year with the CCS system online, and 139ktons per year with the CCS system offline due to reduced natural gas co-firing. So, both operational modes offered a significant net reduction in CO2 emissions. Based on assumptions regarding electricity and natural gas pricing, and CO2 storage revenue generated through an open market mechanism priced at $15 per metric ton CO2, the payback period for the project capturing 0.5MMT/yr (62% of total CO2 production) was estimated at 6.7 years versus 7.4 years with the CCS system offline. Geologic storage was evaluated in the deep flood basalt formations that dominate the storage capacity in Eastern Washington. Sub-basalt sediments were also examined at the site to provide alternative or supplemental storage capacity in the event that storage in the basalt sequences did not prove technically or economically feasible. [Copyright &y& Elsevier]

Pulp and paper industries have provided great research opportunities to control systems. The objective of this study was to investigate the relationships between 80 process variables of CMP tower and stock preparation, and 17 newsprint quality properties in Mazandaran Wood and Paper Industries (MWPI). After the preparation of two suitable data series considering the time needed for pulp to paper, the relations between process dependent and newsprint independent variables were determined using partial least squares (PLS) regression. As a result, two PLS models were developed. The first model with 4 latent vectors categorized and related CMP tower variables and the second one, through 8 latent vectors connected stock preparation variables with paper properties. PLS regression coefficients determined how much the most influencing process variables impact each paper properties [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to estimate the energy saving potential in China's paper industry by determining energy intensity under different scenarios. Cointegration model and stability test are applied to formulate the equilibrium equation. Results show that energy price, industry structure, profit margin and technology have significant impact as they are negatively correlated to changes in energy intensity. Three scenarios (BAU (business as usual), intermediate and active) are designed to estimate the future trend of energy intensity in paper industry. In 2010, the energy intensity (energy consumption per unit of value added) was 4 tce/10,000 RMB, and by the scenarios it is expected to considerably decline. According to the BAU (business as usual) scenario, the energy intensity will decrease to 2.56, 1.43 and 0.70 tce/10,000 RMB by 2015, 2020 and 2025, respectively. With respect to the intermediate scenario it is expected to drop further to 0.44 tce/10,000 RMB. Yet by the active scenario, the energy intensity is expected to considerably decline to 0.36 tce/10,000 RMB by 2025. Using the BAU forecast as a baseline, the quantity of energy savings is estimated to be 185.6 billion tce by 2025. Considering this important potential, we also provided some policy suggestions. [Copyright &y& Elsevier]

This article reports on my attempt to replicate Sachs and Warner’s 1995 and 1997 resource curse working papers. The 1995 paper is not replicable for lack of a data archive. Pure replication of the 1997 paper is achieved. Statistical replication determines that the proposed institutional causes of the resource curse are not robust to country sample. Scientific replication shows that findings of a resource curse are not sensitive to different measures of resource intensiveness, though they are sensitive to estimation technique. Typographical errors in the published paper reveal the value of researchers making both their data and code available. [ABSTRACT FROM PUBLISHER]

Moisture is one of the most harmful agents for transformer solid insulation. The catalyzing effect of moisture on the degradation process of paper and pressboard has been studied by several authors, who developed models which they used to estimate how the aging process would be accelerated in the presence of water [1]?[5]. In [2] and [3] Lelekakis et al. reported the results of an experimental study in which samples with different moisture contents were aged in the presence of mineral oil (MO). In [3] an equation to determine the reduction of service lifetime of insulation subjected to increased temperature and with increased moisture content is proposed. [ABSTRACT FROM PUBLISHER]

Abstract: The transport of electrons through the gas diffusion layer (GDL) of polymer electrolyte membrane (PEM) fuel cells has a significant impact on the optimal design and operation of PEM fuel cells and is directly affected by the anisotropic nature of the carbon paper material. In this study, a three-dimensional reconstruction of the GDL is used to numerically estimate the directional dependent effective electrical conductivity of the layer for various porosity values. The distribution of the fibers in the through-plane direction results in high electrical resistivity; hence, decreasing the overall effective electrical conductivity in this direction. This finding is in agreement with measured experimental data. Further, using the numerical results of this study, two mathematical expressions were proposed for the calculation of the effective electrical conductivity of the carbon paper GDL. Finally, the tortuosity factor was evaluated as 1.7 and 3.4 in the in- and through-plane directions, respectively. [Copyright &y& Elsevier]

Accurate estimation of paper-moisture in transformer insulation is necessary for ensuring their satisfactory operation. Paper-moisture in transformer insulation can be estimated from their corresponding transfer function zero obtained from insulation model that is parameterised using polarisation depolarisation current data. However, paper-moisture estimated using transfer function zero is less reliable for a new unit. Considering the fact, a method is proposed in this work that can estimate the paper-moisture with greater accuracy. Instead of using only transfer function zero, tand has also been employed for estimating the paper-moisture. In order to investigate the effectiveness, the proposed technique has first been applied on several test samples in the laboratory. The proposed technique is then applied on few power transformers to estimate their paper-moisture. Results obtained from the experiment show that this technique can estimate the paper-moisture content within test samples and power transformer insulation with better accuracy. [ABSTRACT FROM AUTHOR]

The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. [ABSTRACT FROM AUTHOR]

Abstract: Heat management is crucial to polymer electrolyte membrane (PEM) fuel cell commercialization. Numerical modeling is often used to simulate heat transfer in the various components of the cell and specifically the gas diffusion layer (GDL). Due to the porous nature of the gas diffusion layer and its complexity of anisotropy, the effect of the structure on the thermal conductivity is usually taken into account by introducing an effective thermal conductivity. In this study, the effective thermal conductivity of carbon paper diffusion media was estimated numerically. Carbon paper is often used as the GDL in PEM fuel cells due to its ability to efficiently transport electrons, heat and gaseous species. Using the GeoDict code, a realistic three-dimensional pore morphology of carbon paper was used as the modeling domain and the governing mathematical equations were solved using the commercial software package Fluent (6.3.26) and the ThermoDict solver. The geometrical effects on the effective thermal conductivity were investigated for different geometries. It was found that the effective thermal conductivity is highly sensitive to the geometry of the porous material under investigation. The effective thermal conductivity is much larger in the in-plane direction when compared with the value in the through-plane direction. Further, the change of the effective thermal conductivity due to porosity and compression was studied. Finally, correlations for the through-plane and in-plane effective thermal conductivity were developed. [Copyright &y& Elsevier]

This study examines the causal relations between exports and domestic production in the pulp and paper industries. The issue is whether exports are the engine of growth, or whether exports follow growth. The data were time-series of the 15 main exporting countries between 1961 and 1995. The method was Granger-causality analysis with error correction, based on models estimated in three ways: ordinary least squares by country, least squares with dummy variables (LSDV), and seemingly unrelated regression. Regardless of method, the strongest relation was an instantaneous (within a year) feedback between exports and production. The LSDV results implied average multipliers across countries of 1.2 to 1.4 from exports to production, and 0.20 to 0.25 from production to exports, in both industries. Experiments with monthly data on the pulp industries of Canada and the USA showed that temporal aggregation could affect the Granger-causality test results. [ABSTRACT FROM AUTHOR]

This discussion provides our reaction to the article by Greven and Scheipl. It contains an overview of their article and a description of the many areas of research that remain open and could benefit from further methodological and computational development. [ABSTRACT FROM AUTHOR]

Multilevel regression and post-stratification (MRP) is a popular use of hierarchical models in political science. Multiple papers have suggested that relying on machine learning methods can provide substantially better performance than traditional approaches that use hierarchical models. However, these comparisons are often unfair to traditional techniques as they omit possibly important interactions or nonlinear effects. I show that complex ("deep") hierarchical models that include interactions can nearly match or outperform state-of-the-art machine learning methods. Combining multiple models into an ensemble can improve performance, although deep hierarchical models are themselves given considerable weight in these ensembles. The main limitation to using deep hierarchical models is speed. This paper derives new techniques to further accelerate estimation using variational approximations. I provide software that uses weakly informative priors and can estimate nonlinear effects using splines. This allows flexible and complex hierarchical models to be fit as quickly as many comparable machine learning techniques. [ABSTRACT FROM AUTHOR]

In the paper the second order half-linear delay differential equation ( r ( t ) | u ′ ( t ) | α - 1 u ′ ( t ) ) ′ + p ( t ) | u ( τ ( t ) ) | α - 1 u ( τ ( t ) ) = 0 , α > 1 is studied under the condition ∫ ∞ r - 1 / α ( t ) d t < ∞ . The oscillation criterion proved by Sun and Meng (2006, Theorem 2.2) is carefully examined and improved by simplifying and sharpening the estimates used in the original proof and removing unnecessary assumptions. Among others, using elementary arguments we show that an arbitrary positive nondecreasing function ρ from this criterion can be safely and with no loss of generality replaced by a constant function. An extension to other related equations, such as neutral equations is also provided. [ABSTRACT FROM AUTHOR]

This paper uses estimated model parameters as inputs into multivariate quality control charts. The thickness of paper leaving a paper mill is measured at a high sampling rate, and these data are grouped into successive data segments. A stochastic model for paper is fitted to each data segment, leading to parameter estimates and information-based standard errors for these estimates. The estimated model parameters vary by more than one can be explained by the information-based standard errors, suggesting that the ‘true’ underlying parameters are not constant over time. A model is formulated for the true parameters in which the information matrix dictates the distribution for the observed parameters given the true parameters. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

It is argued that model selection and robust estimation should be handled jointly. Impulse indicator saturation makes that possible, but leads to the situation where there are more variables than observations. This is illustrated by revisiting the analysis of Tobin's food data. [ABSTRACT FROM AUTHOR]

A dirty-paper channel is considered, where the transmitter knows the interference sequence up to a constant multiplicative factor, known only to the receiver. Lower bounds on the achievable rate of communication are derived by proposing a coding scheme that partially compensates for the imprecise channel knowledge. We focus on a communication scenario where the signal-to-noise ratio is high. Our approach is based on analyzing the performance achievable using lattice-based coding schemes. When the power of the interference is finite, we show that the achievable rate of this lattice-based coding scheme may be improved by a judicious choice of the scaling parameter at the receiver. We further show that the communication rate may be improved, for finite as well as infinite interference power, by allowing randomized scaling at the transmitter. [ABSTRACT FROM AUTHOR]

Respondents in self-administered, paper-based surveys answer open-ended frequency questions differently depending on the size of the response field provided. Longer fields increase the proportion of alphanumeric elaborations or answers explicitly designated as an estimate ("about 20," "60-70"). By contrast, shorter fields yield higher proportions of pure digit responses. It is assumed that respondents interpret the size of the response field as an instruction regarding the format of the expected answer. In this article, the authors assess the effect of the length of the response field in a web survey compared to a paper-and-pencil questionnaire. Results indicate that the effect of the length of the response field is more pronounced in the paper-and-pencil condition compared to the web survey condition. This raises the question as to what extent the underlying visual design language differs across self-administered survey modes. [ABSTRACT FROM AUTHOR]

One of the main issues of an agile software project is how to accurately estimate development effort. In 2014, a Systematic Literature Review (SLR) regarding this subject was published. The authors concluded that there were several gaps in the literature, such as the low level of accuracy of the techniques and little consensus on appropriate cost drivers. The goal of our work is to provide an updated review of the state of the art based on this reference SLR work. We applied a Forward Snowballing approach, in which our seed set included the former SLR and its selected papers. We identified a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD). We also identified that there is a gap in terms of agreement on suitable cost drivers. Thus, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation. This updated review of the state of the art resulted in 24 new relevant papers selected. [ABSTRACT FROM AUTHOR]

An introduction to papers on small area estimation featured in the current issue is presented, with topics including poverty mapping, time series models and measurement error models.

This paper proposes and evaluates two robust sheet tension estimation algorithms based on unwind and rewind variables, respectively. The proper sheet tension estimation is guaranteed by a continuous monitoring of the differences between the actual estimated values and the values predicted from the last calibration of the estimation parameters. The evaluation was performed with the aid of a developed winder emulator that allowed the offline testing in a real-time field environment, including the initial setting of the estimation constants, and evaluation of both four- and six-shipping-roll cycles. The sheet tension estimation results confirm the precise and stable operation of the proposed robust estimation algorithms, in tests conducted with signals from a winder model, and with tests involving actual field signals from an operational winder. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a methodology for the implementation of edge intelligence for predicting solar panel performances on wireless sensor nodes. The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper. [ABSTRACT FROM AUTHOR]