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Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures. [ABSTRACT FROM AUTHOR]

With growing demand of paper products, the electric consumption in pulp and paper industry is increasing globally where a share of 30 % is attributed to the pulp pumping system. The efficient designing and running of pumping system are critical for energy efficiency. The incomplete information on pulp flows and the oversized designs are the major causes of low efficiency. In the first part of this research, the fiber level models and rheological properties covering a series of unit applications are examined. A loss model of pulp pump is set up by considering different factors influencing pumping performance. The efficiency improvement opportunities in pulp pumping are systematically investigated. The drag-reducing feature at low consistency pulp pumping is highlighted which will be further applied in energy optimized sizing and dimensioning of a pulp pump in the second part of this research. [ABSTRACT FROM AUTHOR]

Nowadays, reliability of power system is of great importance. Therefore, the condition of transformers as the expensive and critical components of a power system should be monitored and assessed. The dominant factor that causes limitation in transformers lifetime is the paper insulation. This paper aims to investigate the conspicuous influence of moisture content, especially in low oxygen and temperature on lifetime estimation under constant conditions, based on the results of previous experiments. Degree of polymerization (DP) is employed to quantify the effects of detrimental parameters with respect to their time-varying nature. This paper proposes a novel lifetime estimation-approach considering a yearly moisture growth for transformers. The results indicate different trend of DP profiles, which lead to an accurate lifetime estimation in comparison with DP obtained through ideal conditions. Furthermore, oscillations in the hot-spot temperature of transformers are demonstrated to have a negative effect on the lifetime. [ABSTRACT FROM AUTHOR]

A multi responsive Schiff base sensor L with thiophenol moiety was synthesized by the single-step condensation method. The structural analysis of the sensor was proposed by 1H-NMR, UV- Visible absorption, HRMS and FT-IR spectroscopy. Sensor L exhibited an instant appearance of yellow color on the addition of copper ions. Since copper gave an intense color change, therefore detailed studies were performed for copper sensing w.r.t. designed sensor L. Selectivity and sensitivity of sensor concerning copper metal ions were established by performing various UV-Visible spectroscopic and colorimetric studies. The 1:1 binding framework of the L-Cu2+ complex is confirmed by Job’s Plot and further supported by DFT studies. With the help of Benesi-Hildebrand equation, the detection limit and the association constants were found to be $3.15 \times 10^{-6}$ M and $1.16 \times 10^{4}$ M−1, respectively. In addition, sensor L was transferred onto solid substrates including silica and paper strips for establishing its utility in the onsite detection of copper ions. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 16(4) (2021). The first paper adopts a higher order spatial autoregressive model with endogenous spatial weight matrices. The second paper investigates the existence of the law of one price using regional observations over time. The third paper develops an economic-theoretical model that goes against the common belief that the most productive individuals and firms agglomerate at the core. The fourth paper provides empirical evidence that merger and acquisition deals are more likely to occur between firms in culturally than in geographically contiguous countries. The fifth paper develops a spatial econometric estimator based on the indirect inference principle. The sixth paper examines the investment behaviour of First Nation governments through joint ventures. The seventh paper employs a spatial econometric model with an endogenous spatial weight matrix to construct intraregional input-output models. [ABSTRACT FROM AUTHOR]

Reactor experiments were conducted to study aerobic sulfide oxidation in pulp and paper wastewaters under typical operating conditions. Data produced from these experiments were used to develop a set of sequential rate equations that describe the significant sulfide and oxygen processes in the reactors. Model parameters were estimated for three different pulp and paper wastewaters using a multiresponse procedure. The sulfide oxidation rate coefficients for the wastewaters ranged from 0.108 to 0.155 min-1 at 35°C and approximately neutral pH. The reaction order with respect to oxygen could not be precisely estimated but appeared to be near the lower boundary for the fitting procedure (essentially zero order). Modeling of the oxygen depletion data provided evidence that the first sulfide oxidation reaction product consumed 0.5 mg of oxygen per mg of sulfide oxidized, suggesting polysulfides and elemental sulfur as the initial reaction products. The oxygen depletion modeling also suggested oxidation of the initial reaction product to a more stable intermediate which is postulated to be thiosulfate. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 17(1) (2022). This issue begins with a second editorial calling on researchers to publish replication results from previous studies. The first paper applies a spatiotemporal Bayesian hierarchical model for understanding the dynamics of second home ownership in Corsica. The second paper determines the optimal time to invest in a new airport using real options analysis. The third paper employs unit root tests to provide empirical evidence that environmental policy changes have not been effective up to now. The fourth paper provides empirical evidence that regional spillover effects should play a crucial role in the policy discussion about climate change. The fifth paper forecasts the direct impact of climate change on crop yields in the agricultural sector and the indirect impacts on other sectors of the Brazilian economy up to 2100. The sixth paper investigates whether the percentage of women in national parliaments positively affects public expenditures on social needs both internally and in neighbouring countries. The seventh paper sets out a general framework for store sales evaluation and prediction. [ABSTRACT FROM AUTHOR]

When adequately maintained, the mechanical system of rewinders and similar machines in paper industry may have a significantly longer service life than its electrical and control systems. Over the lifetime of the facility, a rewinder may experience several major maintenance cycles, and these generally include significant upgrades to the electrical drive and control system. These upgrades present an opportunity to modify the design of the control system to eliminate some of the sensors or to provide signal redundancy by using state estimation. During such major reconstruction of a paper-board rewinder, it was noticed that the shipping-roll radius and the web tension force can be accurately estimated from the measurements available within the drive's control system. This paper presents the implementation of the two estimators on a commercial production rewinder. Test results from the equipment in service show that the measured signals can be supplemented with the estimated ones to provide an alternative in the event of sensor malfunction or to enable trending of accuracy of calibration. Both applications have an entitlement to reduce the downtime of the rewinder, as the replacement of the faulted sensor can be scheduled for the next planned idle period of the machine. [ABSTRACT FROM AUTHOR]

Online sensing of sheet modulus of elasticity in the dry-end spans of printing grade paper machines (PMs) will permit the machine operators to control the mechanical properties of the produced paper. This paper develops an integrated sensorless tension control and sheet modulus of elasticity estimation algorithm, which is tested using field-recorded signals from a production PM and using a full model of the dry-end sections of this machine. Results show the ability of the proposed estimation algorithm to predict the sheet tension (tensile force) and sheet modulus of elasticity even in the presence of rapid changes in the speed and/or in other process operating conditions. The developed algorithms offer the papermaker new insights into the papermaking process. The ability to understand how modulus changes in real time provides a pathway to optimizing the operation of the PM drive system. [ABSTRACT FROM PUBLISHER]

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted a growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this paper, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research. [ABSTRACT FROM AUTHOR]

This paper investigates the practicality of an autonomous and resilient positional navigation using corner cube reflectors on the lunar surface. The proposed approach stands as an efficient navigation solution when the current system, e.g. GPS, is unavailable due to being attacked, jammed or spoofed by a third party. The paper provides a complete setup of the mathematical models using a nonlinear least squares algorithm and covariance analysis. A study is done that determines how the blockage of the Earth affects the proposed solution’s ability to perform positional navigation. Numerical simulations are presented to show the accuracy, performance and practicality of the proposed solution. The results of the proposed solution are compared with the results of an Earth-based ground station system that uses radio frequency to track the given satellite. The results show that the lunar reflector-based system generates more accurate results than the Earth-based ground station system. [ABSTRACT FROM AUTHOR]

Building and other construction assets constitute a significant part of a country's physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries' specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980-2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country's construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of process faults in the Lorenz system that can affect any of the system parameters and cause the system to exhibit various behaviors. In this paper, the homoclinic orbits in the Lorenz system are described and then the occurrence of process faults in the system is investigated that can cause a homoclinic explosion, bifurcation, change of fixed point, or even instability in the system. In such systems, where a small change in one of the parameters causes large changes in the behavior of the system, to prevent disaster in industrial systems and also to stop the propagation of faults in the system, the faults must be identified as soon as possible. In this paper, the states in the system are estimated by using a reduced-order observer, and the faults are detected. The purpose of this article is to recognize the change in behavior of this system in the face of this type of fault and to express the importance of timely detection and identification of faults in the system so as not to lead to failure and disaster in industrial systems. Finally, the effect of process fault, disturbance, and sensor fault are investigated simultaneously and the states and faults in the system are estimated. [ABSTRACT FROM AUTHOR]

Currently, SLAM (simultaneous localization and mapping) systems based on monocular cameras cannot directly obtain depth information, and most of them have problems with scale uncertainty and need to be initialized. In some application scenarios that require navigation and obstacle avoidance, the inability to achieve dense mapping is also a defect of monocular SLAM. In response to the above problems, this paper proposes a method which learns depth estimation by DenseNet and CNN for a monocular SLAM system. We use an encoder-decoder architecture based on transfer learning and convolutional neural networks to estimate the depth information of monocular RGB images. At the same time, through the front-end ORB feature extraction and the back-end direct RGB-D Bundle Adjustment optimization method, it is possible to obtain accurate camera poses and achieve dense indoor mapping when using estimated depth information. The experimental results show that the monocular depth estimation model used in this paper can achieve good results, and it is also competitive in comparison with the current popular methods. On this basis, the error of camera pose estimation is also smaller than traditional monocular SLAM solutions and can complete the dense indoor reconstruction task. It is a complete SLAM system based on monocular camera. [ABSTRACT FROM AUTHOR]