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This technical note describes in detail a method for associating individual sheets of blank A4 white paper from the same ream by the physical fit of machine‐cut edges. A large‐scale laboratory trial involving ~700 sheets of paper from 24 different reams (plus one spoiled sample), and more than 20,000 potential physical fits, correctly associated and sequenced 219 pairs of sheets together with a 100% empirical success rate and no false associations. The edge profile of each short machine‐cut end of a sheet of A4 paper allows us to physically fit sheets of paper from the same ream to each other and use this to predict the sequence of sheets in a set of documents. In a real‐life scenario, it may now be possible to detect the substitution or addition of a sheet in a multipage document, link documents from different sources to each other or to a common source of paper (e.g. to paper from a seized printer or from an accused's address) or to date documents. The study provides data for the application of this method in forensic casework and supports the practitioner when forming conclusions in this type of case. [ABSTRACT FROM AUTHOR]

Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems. [ABSTRACT FROM AUTHOR]

The objective of the research is to forecast the trend of the printing and writing paper consumption in Iran for a five-year period using both modern and classical methods. In order to do the forecasting, predictability of time series was primarily studied using Durbin-Watson and Runs tests. Then, artificial neural network model (multilayer perceptrons (MLP)) and univariate and multivariate classical forecasting models such as univariate single exponential smoothing (SES), double exponential smoothing (DES), holt-winters exponential smoothing (HWES) and Box- Jenkins (ARIMA) models, and multivariate econometric model all together were compared in terms of the standard statistical measures. Finally, the consumption of printing and writing paper in Iran was forecasted up to the year 2017 using the most appropriate model. The results of both the parametric test of Durbin-Watson and non-parametric test of Runs show that, the printing and writing consumption series is non-random and predictable. The results of comparing different forecast methods showed that the artificial neural network model has higher forecasting accuracy than the classical models and it is more appropriate for the five-year forecast period. Also, the results of forecasting by using neural network model (MLP), revealed that the printing and writing paper consumption in Iran is forecasted to increase by 5.3%, from around 375 thousand tons in 2012 to 420 thousand tons in 2013, but it falls over the five-year forecast period, from 5.3% in 2013 to 0.07% in 2017.

Mathematical modelling is a powerful tool in science. Causal mathematical models based on a clear picture of how key variables interact enable a deeper understanding of a given situation and provide reliable predictions. This is a classic approach in science. Unfortunately, this approach is declining in pulp and paper-related research in favour of simply reporting experimental data. The lack of a framework provided by a model diminishes the value of much experimental work. Therefore, the increased use of mathematical models is encouraged, and this approach is illustrated via several practical examples from our work. [ABSTRACT FROM AUTHOR]

The article focuses on the Institute for Supply Management's quarterly forecast, which predicts spending increases for printing and paper manufacturing. It mentions the forecast indicates that manufacturing and services sectors are expected to experience revenue growth, while capital expenditures and prices paid for raw materials are projected to increase.

This study proposes a novel method to forecast the emerging patenting area with Main Path Analysis and Word Cloud Analysis. To test the methods, we used Wind Power Energy as an example to illustrate the method's usefulness. Firstly, we used "wind power" and "wind energy" to collect 40,827 related journal papers in Scopus and 72,979 related patents in Derwent Innovation databases. Main Path Analysis was conducted to explore the development trajectory. The results of the Main Path Analysis for the papers and patents were visualized with Pajek software. Secondly, we used VOSviewer to extract the technological areas (i.e., keywords) of the collected academic papers and patents. Then, we calculated the average time lag between the first paper published and the first patent filed for each technological area (keyword). Finally, we forecasted the trend of patenting for wind power energy based on the average lag time and academic research themes in recent years. [ABSTRACT FROM AUTHOR]

Paper folding is a common activity in East Asian kindergartens, but its potential value to early spatial skills have not been empirically explored. This study aims to investigate whether and how paper folding skills can predict spatial ability (SA) in the early years. Altogether 101 preschoolers (Ngirl = 45, M age = 4.54, SD = 0.75) were randomly sampled from two Hong Kong kindergartens and invited to complete the map-use and the paper folding tasks. The paper folding task taps two levels of children's paper folding skills: Basic Folding Skill (BFS) and Advanced Folding Skill (AFS). The parents reported the demographic information and their involvement in spatial activities at home. The results indicated the following: (1) there was a significant age-related increase in the paper folding performance; (2) child age could significantly predict both BFS (β = 0.551, p < 0.001) and AFS (β = 0.627, p < 0.001), while parental involvement could only predict BFS (β = 0.246, p < 0.001); (3) after controlling for confounders, paper folding skills could significantly predict SA as measured by the map-use task; (4) BFS was found to mediate the relationship between parental involvement and SA. The educational implications of these findings are also discussed. [ABSTRACT FROM AUTHOR]

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid's operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. [ABSTRACT FROM AUTHOR]

Paper, a porous-fibrous network, is made up of hydrophilic fibres, which are notably susceptible to deformations due to variations in moisture content. The response of paper sheets to full or partial wetting, based on the water-induced fibre swelling, has mostly been predicted in the past using highly idealised two-dimensional network geometries. The purpose of the present paper is to establish how precisely the two-stage process of determining effective hygro-expansivity under uniform wetting by a two-dimensional model followed by bending analysis using a continuum model predicts the curl of a fully three-dimensional network. In the present multi-scale modelling work, the moisture-induced homogenised sheet scale curl, derived from hygro-elastic deformations using non-uniform moisture profiles, is analysed based on an idealised non-woven three-dimensional fibrous network finite element model. First, the effective paper sheet swelling properties, derived computationally for the uniform wetting condition, are compared with analytically homogenised hygro-elastic properties. Whereas the analytically predicted elastic modulus has an inaccuracy of only ∼ 5%, the hygro-expansivity coefficient is over-predicted by roughly a third for a network with straight fibres. The difference is smaller if waviness of the fibres and wrap-around in the fibre bonds are taken into account. Fibre alignment turns out to have little influence. Subsequently, paper sheet curl predictions made by the analytical model are compared with that computed from the three-dimensional network model. The inaccuracy of the fully analytical model, based on the two-dimensional network description, relative to the fully detailed three-dimensional computational model with straight fibres is on the order of 40%–50%. The fidelity of the simple model is primarily constrained by the inaccuracy of the effective hygro-expansion coefficient. If the expansivity extracted from the three-dimensional simulations under uniform wetting is inserted, the inaccuracy drops to less than 10%. The continuum bending description hence performs adequately, provided it is based on a reliable estimation of the uniform expansion behaviour of the material. • Hygroscopic curl of paper sheets may be predicted using homogenised models. • We assess the accuracy of such predictions. • As a reference simulations of an idealised three-dimensional fibre network are used. • The predictions are accurate provided an accurate hygro-expansivity is used. • Two-dimensional network models may not be sufficient for this. [ABSTRACT FROM AUTHOR]

Accurate monitoring of insulation aging of oil-paper insulation power equipment such as oil-immersed transformers is a key and difficult point in the field of high voltage research. In this paper, a method based on Raman spectroscopy to diagnose the aging degree of oil-paper insulation is discussed. Raman detections of the samples were carried out on a self-built Raman detection platform. The partial least squares method was used to extract and analyze the spectral features. The aging time of the sample was used to supervise the feature extraction of oil-paper insulation Raman data, and the intrinsic mathematical relationship between the Raman features of oil-paper insulation and the aging was excavated. Finally, a quantitative aging diagnostic model based on Raman spectral features of oil-paper insulation to predict its aging state was built with the assistance of the support vector regression method. The results of aging time prediction for 30 test samples show that the mean square error is 0.0123 and the square of correlation coefficient is 0.987. The proposed method provides a new idea for Raman aging diagnosis of oil-paper insulation. [ABSTRACT FROM AUTHOR]

The thin-walled tubular deployable composite boom (DCB) can realize folding and deploying functions, and it has a good application prospect in space field. This paper investigates the folding behaviour of the tubular DCB by analytical modeling. Based on the Archimedes' helix, the geometrical model of the DCB was established. By combining equilibrium equation and energy principle, an analytical model to predicted the folding moment versus rotational displacement of the DCB was presented. The failure indices in the equal-sense and opposite-sense folding processes were calculated utilizing Tsai‐Hill and maximum stress criteria. Analytical results agreed well with experimental and numerical results. At last, the influence of geometric parameters (i.e., radius, central angle and thickness of cross-section) on the folding behaviour of the DCB was further studied using the analytical model. [Display omitted] • An analytical model is presented to predict the folding behaviour of the deployable composite boom (DCB). • Failure indices are calculated using two criteria in equal-sense and opposite-sense folding processes. • The analytical model is validated against experimental and numerical results, showing good prediction accuracy. • Geometric parameters (radius, central angle, and thickness) significantly affect the folding behavior of the DCB. [ABSTRACT FROM AUTHOR]

This study examined the effect of different parameters on the collapsibility of reconstituted sandy soils. The effect of related parameters including silt content, type of fines, initial water content, drying and wetting process, and wetting pressure have been studied. Collapse potential and matric suction were measured via the single oedometer collapse test and the ASTM filter paper method, respectively. The current study revealed that matric suction has a major role in determining the collapsibility of sandy soils. The effect of parameters related to soil index properties were reflected on the initial matric suction. In addition, an equation was proposed to predict collapse potential as a function of the two stress-state variables that control the behavior of an unsaturated soil, i.e. net normal stress and matric suction before wetting. The matric suction measurements were also used to generate soil water characteristic curves (SWCCs) based on the most widely used models. Finally, a simplified relationship between initial matric suction and the degree of collapse based was established. [ABSTRACT FROM AUTHOR]

The problem of emission control in chemical fiber industry and construction of multifactor model of pollution index prediction is studied. A neural network method that makes it possible to derive regularities of a unidimensional series for PM 2.5 and simultaneously determine the influence of a set of factors of weather conditions is described. To reduce the complexity of the model, instead of the range of factors of influence, its main components are used. The interconnection of the number of factors of influence, depth of retrospective sampling, type and scale of neural network, and accuracy of prediction is studied. [ABSTRACT FROM AUTHOR]

The condition assessment of oil–paper insulation is a vital issue in transformer management. To investigate the effect of moisture content on the accuracy of the prediction model, accelerated ageing experiments were performed with different initial moisture content (IMC) in laboratory condition. Two types of multi-parameter lifetime prediction models, e.g. with and without consideration of IMC, were presented for comparison purpose. The authors found that a better accuracy for its prediction results could be achieved by establishing the prediction models with the consideration of IMC. Besides, models considering IMC have better economic feasibility than that without considering IMC(non-IMC) as its total cost is cheaper according to life cycle cost (LCC) calculations. In addition, failure probability based on the proposed prediction models was also calculated as a direct indicator for evaluating the oil–paper insulation status. [ABSTRACT FROM AUTHOR]

Rainfall is one of the remarkable hydrologic variables that is directly connected to the sustainable environment for any region over the globe. The present study aims to review different research papers on rainfall forecasting using artificial intelligence (AI) models including a bibliographic assessment of the most popular AI models and a comparison of the results based on the accuracy parameters. 39 journal papers, published in renowned international journals from 2000 to 2023, were studied extensively to categorize modeling techniques, best models, characteristics of input data, the period for the input variables, data division, and so forth. Although certain drawbacks still exist, the results of reviewed studies suggest that AI models may help simulate rainfall in various geographic locations. In some cases, the data splitting mechanism was delivered to the model itself so that the model accuracy gets improved. The recommendations from the reviewed papers will help future researchers fill the research gaps, especially tuning the hyperparameters while building the training models. Hybrid models were advised in some cases to minimize the gap between the simulated and the observed data. All recommendations from reviewed papers aimed to achieve a resilient rainfall forecasting model in the era of climate change. [ABSTRACT FROM AUTHOR]

The power transformer is an important part of the power grid. The aging of oil-paper insulation is affected by many factors such as temperature and moisture. In order to accurately evaluate the service life of transformer and ensure the stable operation in power system, a method for predicting the lifetime of oil-immersed pressboard under different temperatures and moisture content was proposed. This method is mainly realized by the chemical reaction kinetic equations and transformation factor. Firstly, the effect of temperature and moisture content on the frequency dielectric response of oil-paper insulation was studied by frequency domain dielectric spectroscopy method. The results show that the dielectric loss of oil-immersed pressboard increases with the increase of moisture content and temperature. The activation energy of oil-immersed pressboard also increases exponentially with the increase of moisture content. Then, we analyzed the trend of polymerization degree of insulation pressboard in aging process and calculated the insulation life and the remain lifetime by combining the cellulose degradation kinetic models and translation factor. When the moisture content of the oil-immersed at 70 °C, the life expectancy of oil-paper insulation is about 19.26 years. [ABSTRACT FROM AUTHOR]

Nests of independent-founding primitively eusocial paper wasps are founded by one or a few females. Before worker emergence, foundresses must forage, sometimes leaving the nest unattended. Chemical defence - the application of ant repellent secretions of the Van der Vecht organ (VdVO) on the nest surface - allows brood protection when active defence is weak or not possible. After worker emergence, it is more likely that some wasps stay in the nest, so active nest defence may be more common. Given this constraint on the nest defence, selection for increasing the cuticular secretory area of the VdVO, to improve chemical defence, may be stronger in foundresses (queens) than in workers - morphological castes are hypothesized to evolve. We investigated the occurrence of morphological castes in the Neotropical paper wasp Mischocyttarus. Confirming our prediction, we found that in some (but not all) species, foundresses have a distinct increase in the secretory area of the VdVO in relation to workers; even though there is a strong overlap between caste phenotypes (incipient morphological castes). Implications of these findings for the evolution of morphological castes in primitively eusocial wasps are discussed. [ABSTRACT FROM AUTHOR]

Effluent from paper industry was oxygenated till saturation using Lark Hygiene Fermenter at 25°C under real time environment. Time, speed, and feed concentration were selected as dominant input factors to determine the dissolved oxygen (DO) under response surface methodology (RSM) based Box–Behnken design to provide 17 experimental runs. The experimental results were optimized using RSM and artificial neural networks (ANN). From RSM analysis, a second-order quadratic representation for single objective optimization was successfully fitted which produced R² = 99.07 and R² adj = 97.87. Analysis of variance results shows that time and feed concentration were the most significant parameters than the speed, influencing the DO. Another single objective optimization tool ANN produced R² = 96.27 and R² adj = 94.06. Validation analysis provided the predicted values by RSM and ANN were close to the validation values, whereas RSM showed a better prediction than the ANN with the lowest deviation. Hence, the optimal condition predicted by RSM was taken to conduct the confirmatory experiment. The confirmatory experimental condition of time at 6 min, speed of 142 rpm with 90% concentration produced the minimal DO of 5.38 ppm. The error percentage of the predicted with confirmatory experimental and theoretical equations results were 1.11% and 0.035%, respectively, which validates the predicted model. [ABSTRACT FROM AUTHOR]

Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

We present a combined experimental and theoretical investigation of the dynamics of water imbibition through paper with swelling. The Washburn equation has been widely used to describe the dynamics of the liquid absorption in paper, but its prediction of liquid imbibition speed has been reported to be inaccurate. Our recent study (Chang et al., J. Fluid Mech., vol. 845, 2018, pp. 36–50) demonstrated that the internal cavity of cellulose fibres composing the paper is partially responsible for the limited accuracy of the Washburn equation based on oil imbibition experiments. Here we extend the investigation to water absorption through paper with swelling. We demonstrate that the swelling of the cellulose fibre network in addition to the internal voids of the cellulose fibres crucially affects the imbibition dynamics. Based on the microscopic observation that paper swelling is caused by the expansion of inter-fibre space, we suggest a mathematical model for water imbibition which considers both intra-fibre voids and swelling. By introducing parameters that characterize the swelling speed and volume of paper, our model markedly improves prediction of the water imbibition speed. The results provide not only a theoretical background for designing paper-based microfluidic systems, but also new insights into capillary flow through expandable porous media. [ABSTRACT FROM AUTHOR]

We enjoyed reading Professor Efron's (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. Interestingly, Efron puts the pure predictive algorithms to the test in a scenario where the sample size is extremely moderate by today's standards ( I n i = 102). In the knockoff framework, one can use I any i black-box (any predictive model) to identify any kind possible association - linear or not - between a large set of explanatory variables HT ht and an outcome I Y i . This framework rigorously tests whether the conditional distribution HT ht actually depends on any of the variables I X SB j sb i without assuming any model for how I X i and I Y i are connected (Candès et al. 2018). [Extracted from the article]

Here we study the physical problem in which a thin bar, such as a strip of paper, is bent under several types of loads and boundary conditions. Initially, we consider lengthwise compression of the strip due to concentrated forces applied on its ends. We present this problem for the three boundary conditions: doubly-clamped, hinged–clamped, and doubly-hinged ends constrained on a flat surface. We also consider both analytically and experimentally the problems of distributed loading such as the heavy cantilever and the post-buckling heavy column. We find the equations that govern the shapes of the bent strips via a minimization flow of the bending energy. In all cases, we find that photographically-captured profiles agree very well with the theoretical predictions. We also validate the theory with measurements of the deflection of the middle or the tip of the strip as its length is varied. [ABSTRACT FROM AUTHOR]

With a sample of 228 psychology papers that failed to replicate, we tested whether the trajectory of citation patterns changes following the publication of a failure to replicate. Across models, we found consistent evidence that failing to replicate predicted lower future citations and that the size of this reduction increased over time. In a 14-y postpublication period, we estimated that the publication of a failed replication was associated with an average citation decline of 14% for original papers. These findings suggest that the publication of failed replications may contribute to a self-correcting science by decreasing scholars’ reliance on unreplicable original findings. [ABSTRACT FROM AUTHOR]

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions. [ABSTRACT FROM AUTHOR]

In view of the problem of fuzziness of rating and tag in traditional collaborative filtering algorithms,this paper used trapezoidal fuzzy number to describe the mapping relationship between rating and satisfaction.The algorithm considered the impact of sparseness of the rating,constructed a new trapezoidal fuzzy rating model to determine the similarity based on fuzzy rating,analyzed the degree of membership between the tag and the item,and constructed a fuzzy item-tag matrix to measure the similarity based on the degree of tag membership.Finally,it used the improved scoring prediction strategy to estimate the score.The experimental results on the MovieLens dataset show that the proposed algorithm improves the prediction accuracy while suppressing the cold start of the project,alleviating the problems of fuzziness and sparseness,which indicates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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With the ever-increasing number of submissions in top-tier conferences and journals, finding good reviewers and meta-reviewers is becoming increasingly difficult. Writing a meta-review is not straightforward as it involves a series of sub-tasks, including making a decision on the paper based on the reviewer's recommendation and their confidence in the recommendation, mitigating disagreements among the reviewers, and other such similar tasks. In this work, we develop a novel approach to automatically generate meta-reviews that are decision-aware and which also take into account a set of relevant sub-tasks in the peer-review process. More specifically, we first predict the recommendation scores and confidence scores for the reviews, using which we then predict the decision on a particular manuscript. Finally, we utilize the decision signals for generating the meta-reviews using a transformer-based seq2seq architecture. Our proposed pipelined approach for automatic decision-aware meta-review generation achieves significant performance improvement over the standard summarization baselines as well as relevant prior works on this problem. We make our codes available at https://github.com/saprativa/seq-to-seq-decision-aware-mrg. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]