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Background  MRI is attractive for guiding and monitoring interventional procedures due to its high intrinsic soft tissue contrast and the possibility to measure flow and cardiac function. Methods  Technical solutions have been developed for all procedural steps including imaging guidance, MR-safe catheters and instruments and patient monitoring. This has led to widening of the clinical applications. Interventional MRI is becoming increasingly important for the treatment of patients suffering from malignant diseases. The detectability of masses and consequently their accessibility for biopsy is higher, compared to other modalities, due to the high intrinsic soft tissue contrast of MRI. Temperature-dependent sequences allow for minimally invasive and tissue-sparing ablation (A-0 ablation). Conclusion  Interventional MRI has become established in the clinical routine for a variety of indications, including biopsies and tumor ablation. Since the economic requirement of covering costs by reimbursement is met and interventional MRI decreases the mortality and morbidity of interventional procedures, broader application of interventional MRI can be expected in the clinical routine in the future. Key points   · Particularly for the treatment of oncological patients, interventional MRI is superior to other methods with respect to minimal invasiveness and tissue protection due to the ability to exactly determine tumor borders and to visualize and control the size of the ablation area on the basis of MR temperature measurement.. · Due to the better visualization of targets and the effects of ablation in tissue, interventional MRI can lower the mortality and morbidity associated with these interventions for many indications.. · The complex comparison of costs and reimbursement shows that this application can be performed in a cost-covering manner and broader application can be expected in the future.. Citation Format · Barkhausen J, Kahn T, Krombach GA et al. White Paper: Interventional MRI: Current Status and Potential for Development Considering Economic Perspectives, Part 2: Liver and Other Applications in Oncology. Fortschr Röntgenstr 2017; 189: 1047 - 1054., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (© Georg Thieme Verlag KG Stuttgart · New York.)

Introduction: The "future of work" refers to workplace modifications projected to occur following large scale events impacting workforce health and safety such as pandemics, technological advancements, or economic shifts. This paper examines the influence of the COVID-19 pandemic on the future of work., Methods: The electronic databases PubMed, Google Scholar, and Scopus were explored using search terms such as "post-pandemic," "working force," and "future of work" to retrieve papers published between 2000 and 2023., Results: The main characteristics of the future of work modification revealed are blurring of work-home boundaries, increase in telework and the gig economy, demand for artificial intelligence, smart working, quiet hiring, diversity and inclusion, and algocratic governance. Needed skills include virtual collaboration capability, cross-cultural competency, computational thinking, news media literacy, transdisciplinarity, sense making, and focusing on mental health and well-being., Conclusions: Organizations are remodeling workforces to meet employee, employer, regulatory, and market expectations to adapt to future workplace needs., Competing Interests: Conflict of interest: None declared., (Copyright © 2024 American College of Occupational and Environmental Medicine.)

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making., (© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)

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]

In response to the current era of rapid evolution of health care delivery and financing, radiologists are increasingly considering, as well as confronting, new practice models. Hospital employment is one such opportunity. Within this report to the ACR membership, the potential advantages and risks for radiologists considering hospital employment are examined., (Published by Elsevier Inc.)

Accountable care organizations have received considerable attention as a component of health care reform and have been specifically addressed in recent national legislation and demonstration projects by CMS. The role or roles of radiologists in such organizations are currently unclear, as are changes to the ways in which imaging services will be delivered. The authors review concepts fundamental to accountable care organizations and describe roles for radiologists that may facilitate their success in such health care delivery systems., (Copyright © 2011 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

The language of radiology has gradually evolved from "the film" (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to "the image," an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatial alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the "image" as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.

Brachytherapy began at the turn of the 20th century, contemporary with external-beam radiotherapy. Physicists and physicians together developed the field. There has not been a period since the beginning that has not witnessed innovations and progress in brachytherapy. At the time of this article, the pace of change in the field has never been more rapid, particularly in image-guided brachytherapy and the development of unconventional sources and treatment techniques.

The Health Advisory Board of the German Federal Association for Rehabilitation (BAR) describes future trends and challenges in rehabilitation as deriving from the socio-demographic development in Germany and the structural characteristics of its Social and Health Care Insurance System. The focus is on elder employees to sustain and regenerate their capacity for employment, on people which are no longer employed to activate their autonomy and ability for self-support, and on rehabilitation as a holistic and integrative process extending through the social security and health insurances. There is an urgent need and a real chance to benefit from already existing scientific findings more frequently and to integrate them effectively into adequate further education and training programmes for professionals. Finally the conclusion summarises 8 theses to facilitate rehabilitation as an integral and essential part of the German social security and health sector. This paper was fully accredited by the members of the BAR Managing Board.

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]

Radiological imaging is a crucial diagnostic tool for the pediatric population. However, it is associated with several unique challenges in this age group compared to adults. These challenges mainly come from the fact that children are not small-sized adults and differ in development, anatomy, physiology, and pathology compared to adults. This paper reviews relevant articles published between January 2015 and October 2023 to analyze challenges associated with imaging technologies currently used in pediatric radiology, emerging technologies, and their role in resolving the challenges and future prospects of pediatric radiology. In recent decades, imaging technologies have advanced rapidly, developing advanced ultrasound, computed tomography, magnetic resonance, nuclear imaging, teleradiology, artificial intelligence, machine learning, three-dimensional printing, radiomics, and radiogenomics, among many others. By prioritizing the unique needs of pediatric patients while developing such technologies, we can significantly alleviate the challenges faced in pediatric radiology., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables., Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R 2  = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82)., Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

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.

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]

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6 ∘ C to almost zero, while the daily mean absolute error decreased from 5 ∘ C to 3 ∘ C for the neural network and 3.5 ∘ C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day., (© 2024. The Author(s).)

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]

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]

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]

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)

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]

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]

We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical risks for forecasting the future realized volatility of oil-price (WTI) returns over the monthly period from 1985:01 to 2021:08. Using machine-learning techniques, we find that adding the disaggregated metrics to the array of predictors improves the accuracy of forecasts at intermediate and long forecast horizons, and mainly when we use random forests to estimate our forecasting model., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper deals with the present position of a specialized scientific library, the increase in the number of such libraries, and their development in conjunction with that of the institutes and laboratories to which they belong. It describes the danger for these libraries of too great expansion and the need to restrict themselves to a well-defined area. The different categories of specialized scientific libraries are reviewed, as well as the role of each of these categories vis-à-vis overburdened university libraries. There is an account of the assistance given [by specialized scientific libraries] to university libraries now and in the near future.

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]

The article compares the accuracy of forecasted and actual supply chain cost and price factors in a Nordic paper mill's sales to customers in two large European countries. Empirical longitudinal research data covering the years between 2002 and 2008 were obtained from a large integrated Finnish paper mill, and consisted of sales volumes, paper prices, variable costs, and transport costs. It was expected that the mill would be able to forecast demand, prices and costs accurately, but the empirical findings showed that the forecasts for paper price, demand, and cost varied from one market to another and were not highly accurate. In addition, the forecasting of gross margins seemed to be very inexact. It is concluded that there is a need for more reliable forecasting methods in the paper industry to anticipate economic development as paper demand and costs change. [ABSTRACT FROM AUTHOR]

Abstract: Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model''s architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. The minimum mean absolute percentage errors of 1.003% and 0.5161% for CODeff and SSeff could be achieved using ANFIS. The maximum correlation coefficient values for CODeff and SSeff were 0.9912 and 0.9882, respectively. The minimum mean square errors of 1.2883 and 0.0342, and the minimum RMSEs of 1.135 and 0.1849 for CODeff and SSeff could also be achieved. [Copyright &y& Elsevier]

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]