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The fiber of paper mulberry, which is mostly grown in the East Asian region, varies in both length and width—even if from the same species—depending on the country, soil, and climate where it is grown. This accounts for differences in the quality of handmade paper between domestic and imported products. Once handmade paper is manufactured as sheets, its origin cannot be determined from a morphological perspective, regardless of macroscopic and microscopic observations. Therefore, this study attempted to determine the origin of Korean and imported products using mulberry bast fiber and handmade paper from a chemometrics perspective. The inorganic components of wood are absorbed from the soil and exist in the bark of trees; thus, metal and rare-earth elements in the bast fibers and handmade papers were quantitatively analyzed using inductively coupled plasma-atomic emission spectroscopy/mass spectrometry (ICP-AES/MS). Following this, a prediction model of their origin was constructed by applying multivariate statistical analysis, i.e., a partial least squares-discriminant analysis (PLS-DA) loading plot was constructed, which helped in identifying the discrimination factors resulting from their origin. According to the ICP-AES results, Pb was only detected in the Gyeongbuk Mungyeong dak sample of the Korean bast fibers. The Chinese bast fiber sample had high contents of Ca, and B and Zn were detected only in the Chinese bast fiber samples. Bast fiber samples from China, Japan, and Thailand showed relatively high contents of Al, Ca, and Na. Meanwhile, Cu and Zn were detected in the handmade paper manufactured from the imported materials. According to the ICP-MS results, among the Korean handmade papers, the products from Gyeongbuk Mungyeong, Andong, and Chungbuk Goesan exhibited a high U content and that from Gapyeong had high Sc, Rb, Sr, and Ba contents. Thus, the elemental analysis employed in this study effectively identified the origin of the papers. The origin prediction by PLS-DA for the domestic and imported products revealed an accuracy of 86.4% and 72.7% for ICP-AES and ICP-MS methods, respectively, which is deemed acceptable to assess the origin. We believe this method significantly contributes to determining the origin of paper mulberry bast fiber and handmade paper manufactured in East Asia. [ABSTRACT FROM AUTHOR]

Abstract Citation prediction of scholarly papers is of great significance in guiding funding allocations, recruitment decisions, and rewards. However, little is known about how citation patterns evolve over time. By exploring the inherent involution property in scholarly paper citation, we introduce the Paper Potential Index (PPI) model based on four factors: inherent quality of scholarly paper, scholarly paper impact decaying over time, early citations, and early citers' impact. In addition, by analyzing factors that drive citation growth, we propose a multi-feature model for impact prediction. Experimental results demonstrate that the two models improve the accuracy in predicting scholarly paper citations. Compared to the multi-feature model, the PPI model yields superior predictive performance in terms of range-normalized RMSE. The PPI model better interprets the changes in citation, without the need to adjust parameters. Compared to the PPI model, the multi-feature model performs better prediction in terms of Mean Absolute Percentage Error and Accuracy; however, their predictive performance is more dependent on the parameter adjustment. [ABSTRACT FROM AUTHOR]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

Intervention into the present building industry is the main strategy of the European Commission in order to achieve one of its planned aims regarding the reduction of greenhouse gas emissions up to 20% up by 2020 and up to 80% by 2050. Therefore, it is necessary to develop not only energetically efficient but also more environmentally friendly materials. This work presents bio-based propylene glycol and paper waste sludge particles modified polyurethane foams, their physical properties and their prediction models. The conducted research has revealed that paper waste sludge particles negatively affect dimensional changes. In order to eliminate the negative impact of paper waste sludge, bio-based propylene glycol is used. The obtained results have shown that this raw material allows development of dimensionally and structurally stable polyurethane foams used as a thermal insulating layer having the following properties: density in the range of 40–50 kg/m 3 , compressive strength in the range of 193–243 kPa, thermal conductivity in the range of 0.0349–0.0359 W/(m·K), long-term water absorption in the range of 6–11 vol.% and water vapour transmission factor in the range of 26.2–40.9. On the basis of the results, prediction models are suggested for the most important physical properties of propylene glycol and paper production waste sludge particles modified polyurethane foams. [ABSTRACT FROM AUTHOR]

Abstract: The Rock–Paper–Scissors (RPS) game is a widely used model system in game theory. Evolutionary game theory predicts the existence of persistent cycles in the evolutionary trajectories of the RPS game, but experimental evidence has remained to be rather weak. In this work, we performed laboratory experiments on the RPS game and analyzed the social-state evolutionary trajectories of twelve populations of players. We found strong evidence supporting the existence of persistent cycles. The mean cycling frequency was measured to be period per experimental round. Our experimental observations can be quantitatively explained by a simple non-equilibrium model, namely the discrete-time logit dynamical process with a noise parameter. Our work therefore favors the evolutionary game theory over the classical game theory for describing the dynamical behavior of the RPS game. [Copyright &y& Elsevier]

Abstract: Scientific breakthroughs are rare events, and usually recognized retrospectively. We developed methods for early detection of candidate breakthroughs, based on dynamics of publication citations and used a quantitative approach to identify typical citation patterns of known breakthrough papers and a larger group of highly cited papers. Based on these analyses, we proposed two forecasting models that were validated using statistical methods to derive confidence levels. These findings can be used to inform research portfolio management practices. [Copyright &y& Elsevier]

The cross-directional model predictive control (CD-MPC) tuning problem is investigated for multiple-array paper-making process with parametric uncertainty. A systematic spatial tuning method is proposed to tune the penalty matrices such that the harmful high frequency elements in each actuator profile can be eliminated. A multivariable temporal filter is employed to adjust the output references, and a temporal tuning algorithm is developed for the filter parameters to achieve the pre-specified 2 σ performance indices. The computational time of the proposed methods is reduced via event-based sampling strategy. The effectiveness of the techniques is shown via a CD system from paper-making industry. [ABSTRACT FROM AUTHOR]

Abstract: As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

Stochastic cellular automata for rock-paper-scissors games are related to Lotka-Volterra model. Simulations are usually performed by two methods local and global interactions. It is well known that the population dynamics with local interaction is stable, where all species coexist. In contrast, global interaction leads to extinction. So far, theories such as mean-field theory and pair approximation have been presented, but they never explained the stable dynamics in local simulation. In the present article, we apply effective medium approximation (EMA) which has been developed in Physics. The effective medium is determined in a self-consistent way. The EMA theory well predicts the stability of population dynamics. Moreover, it fairly explains the aggregation of each species observed in the stationary state. [ABSTRACT FROM AUTHOR]

Highlights • Shear capacity of RC voided (V) slabs and TUBEDECK (TD) slabs is investigated. • Web-shear cracking did not affect the shear design of V and TD slabs. • Based on the real cross-sectional area, a shear strength prediction equation is proposed. • A moment–shear interaction design model for TD slabs is suggested. Abstract In order to improve structural efficiency, environmental performance, and constructability, a new type of one-way composite voided slab system (TUBEDECK) has been recently proposed which combines profiled steel decks with cast-in-situ RC slabs. Because eliminating concrete volume to optimize flexural strength can significantly reduce the shear strength of nonprestressed concrete or composite slabs without shear reinforcement, a clarification of the shear resistance capacity is required. In this study, shear tests on a total of 12 specimens were conducted with slab thickness, the presence or absence of voids and/or steel decks, and tension reinforcement ratio as variables. The results show that combined flexure and shear dominated the behavior of both voided (V) slabs and TUBEDECK (TD) slabs, and web-shear cracking did not affect strength. Predicted shear strength based on the minimum web width was too conservative. Conversely, the shear strength prediction equations, which were proposed on the basis of the real cross-sectional area of concrete, predicted the capacity of both V slabs and TD slabs from a reasonably conservative perspective. A discussion on the influence of moment–shear interaction is also included, and an interaction design model is proposed in a further investigation. [ABSTRACT FROM AUTHOR]

Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics, …) have triggered the need for an efficient screening strategy to prioritize the substances of highest concern. In this context, alternative methods such as in silico (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned ‘high priority’ as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. [ABSTRACT FROM AUTHOR]

Significant dimensional variations may occur in paper-based materials when subjected to changes in moisture content. Moisture induced deformations are governed by the swelling of individual fibres, which is transferred through inter-fibre bonds to the entire fibrous network. Complex interactions between mechanical and hygro-expansive properties take place in the bonding areas, affecting the overall material response. In most network models, the role of these inter-fibre bonds is not explicitly incorporated. This work presents a periodic meso-structural model for the discrete fibrous network, which considers the free-standing fibre segments and inter-fibre bonds. Despite its simplicity, the reference unit-cell enables the incorporation of relevant micro- and meso-structural features such as network structure, fibres and bond geometry and hygro-elastic properties. The proposed model is solved analytically through a proper homogenization strategy, allowing to recover the paper’s anisotropic hygro-mechanical response in terms of effective elastic constants and effective hygro-expansive coefficients, exploiting the coupling at the meso-structural level between hygroscopic and mechanical behavior. A comparison with experimental results obtained from the literature shows that the presented approach is quite accurate in predicting the overall paper response, thereby revealing the influence of several meso-scale parameters (e.g. fibre orientation, dimensions, mechanical strength). [ABSTRACT FROM AUTHOR]

Continuous assessment of soil heavy metal concentrations in agricultural soils is critical for ensuring ecosystem health and minimizing potential adverse effects. However, conventional laboratory determinations are time-consuming, expensive and involve hazardous chemicals. We evaluated the potential of visible near infrared reflectance spectroscopy (VNIRS) to rapidly and simultaneously predict total, Mehlich-3 (M3), DTPA, and water-soluble (H 2 O) soil heavy metal concentrations. Soil samples were collected from a loam soil ( n = 64) and a loamy sand ( n = 32) after repeated annual applications (3–9 years) of paper mill biosolids (PB) and liming by-products. Models were developed using modified partial least squares regression on 75% of the total sample set and also for each soil, after removal of the spectral outliers ( n = 3), with the remainder used for validation. The coefficient of determination in validation (R 2 V ) and the ratio of performance to deviation (RPD) were used to assess model accuracy. Predictions for the total set were excellent for total Zn and Cd, M3-Cu and -Cd, and DTPA-Cd (R 2 V > 0.90, RPD > 3.0), good for total Cu, Ni and Mo, M3-Ni, DTPA-Cu and -Ni, and H 2 O-Cu and -Ni (R 2 V = 0.70 to 0.90, RPD = 1.70 to 3.0), but poor for M3-Zn, DTPA-Zn, and H 2 O-Zn, -Cd and -Mo (R 2 V < 0.70, RPD < 1.70). Except for one case, site-specific models were either more accurate or provided similar results with the model of the total set in predicting site-specific concentrations. The relationship with soil organic matter (SOM) was the major predictive mechanism for Cd in our dataset, while the prediction of other soil heavy metals was due to the combined influence of SOM and Fe oxides. We conclude that VNIRS has the potential to predict total, M3, DTPA and to a lesser extent water-soluble soil heavy metals, especially when developed using site-specific samples. [ABSTRACT FROM AUTHOR]

• This study improved the citation prediction accuracy by applying the BP neural network. • The BP neural network significantly outperformed the other six baselines (XGBoost, RF, LR, SVR, KNN, and RNN). • Five essential features were determined for citation prediction. Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., 'citations in the first two years', 'first-cited age', 'paper length', 'month of publication', and 'self-citations of journals', and the other features contribute only slightly to the prediction. [ABSTRACT FROM AUTHOR]

• This study examines the effect of keyword popularity (KP) features on prediction of highly-cited papers. • We use article metadata to calculate KP feature values based on text mining techniques. • We empirically compared KP features of articles with journal-related and author-related features. • The results show that, with KP features, the performance of binary classification model can be improved. The number of received citations have been used as an indicator of the impact of academic publications. Developing tools to find papers that have the potential to become highly-cited has recently attracted increasing scientific attention. Topics of concern by scholars may change over time in accordance with research trends, resulting in changes in received citations. Author-defined keywords, title and abstract provide valuable information about a research article. This study performs a latent Dirichlet allocation technique to extract topics and keywords from articles; five keyword popularity (KP) features are defined as indicators of emerging trends of articles. Binary classification models are utilized to predict papers that were highly-cited or less highly-cited by a number of supervised learning techniques. We empirically compare KP features of articles with other commonly used journal-related and author-related features proposed in previous studies. The results show that, with KP features, the prediction models are more effective than those with journal and/or author features, especially in the management information system discipline. [ABSTRACT FROM AUTHOR]

Abstract: In the present study the interactions between the main constituents of the refuse derived fuel (plastics, paper, and wood) during pyrolysis were studied. Binary mixtures of polyethylene-paper and polyethylene/sawdust have been transformed into pellets and pyrolyzed. Various mixtures with different composition were analyzed and pyrolysis products (tar, gas, and char) were collected. The mixtures of wood/PE and paper/PE have a different behavior. The wood/PE mixtures showed a much reduced interaction of the various compounds because the yields of pyrolysis products of the mixture can be predicted as linear combination of those of the pure components. On the contrary, a strong char yield increase was found at a low heating rate for paper/PE mixtures. In order to explain the results, the ability of wood and paper char to adsorb and convert the products of PE pyrolysis into was studied. Adsorption and desorption tests were performed on the char obtained by paper and wood by using n-hexadecane as a model compound for the heavy products of PE pyrolysis. [Copyright &y& Elsevier]

Abstract: A simple alternative method to determine the degree of polymerization (DP) in transformer insulating papers, using near infrared spectroscopy (NIR), was developed to reduce the costs and the analysis time of the conventional method. Seventy-five samples of Kraft, crepe and cardboard types of paper, in different stages of degradation (DP varying between 200 and 900 units) were collected from transformers over a period of 3 years. The sample set was analyzed according to the conventional method to compare with the proposed alternative spectroscopic method. The spectra were obtained by diffuse reflectance using the spectral range between 1260 and 2500nm. Some pre-processing strategies of the spectral data were evaluated and the best results were obtained when the derivatives spectra were used (Savitzky–Golay algorithm employing a 21 point window). NIR spectroscopy combined with partial least squares regression method revealed a simple and fast technique to determine the DP of the papers. The prediction error of 83, obtained after external validation, is acceptable for evaluation of the remaining useful life of insulating paper. [Copyright &y& Elsevier]

There is growing momentum, as articulated through the visions of space agencies, to establish permanent settlements on planetary bodies such as the Moon and Mars. One key hazard that structures will be subjected to will be hypervelocity impacts of micrometeoroids and orbital debris. Shielding against such impacts is essential to ensure the safety of structures and their human inhabitants. There has, however, been limited research on such shields and no field applications to date. In order to inform the composition and design of protective shields for planetary structures, recourse can be made to protective shields utilised in spacecraft and space stations that have benefitted from years of research and development. This paper reviews the existing shielding practices for spacecraft and space stations as a starting point for the shielding of planetary structures. Shield configurations are firstly introduced. Both experimental tests and numerical simulations required to evaluate the performance of the shields are then discussed. Analytical prediction models for shields are reviewed, and the predictions of these models are compared with test results of corresponding shields from the literature. Finally, the effect of parameters such as projectile diameter, impact velocity, impact angle, and shield geometry on the overall shield performance is analysed. This paper ultimately provides technical knowledge about the shielding systems for spacecraft and space stations that can be used as a point of reference for scientists and engineers to develop shields for structures on planetary bodies. • Review of shielding systems on spacecraft and ISS for MMOD protection. • Analysis of theoretical models for predicting protective shield performance. • Recommendations provided for shielding systems of Lunar and Martian structures. [ABSTRACT FROM AUTHOR]

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester. Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers. • The paper examines spatial transferability of mode choice models between eHUBS cities. • Model scaling resulted in improvement of disaggregate measures of transferability. • Updating ASC improves aggregate mode share predictions of transferred model. • Scaled mixed logit model with updated ASC explain 75% of overall behaviour. • Transferability of mixed logit model gives better results as compared to MNL model. [ABSTRACT FROM AUTHOR]

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI +3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI +3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production. [ABSTRACT FROM AUTHOR]

The dynamic behaviour of wildfires is mainly influenced by weather, fuel, and topography. Based on fundamental conservation laws involving numerous physical processes and large scales, atmospheric models require substantial computational resources. Therefore, coupling wildfire and atmospheric models is impractical for high resolutions. Instead, a static atmospheric wind field is typically input into the wildfire model, either as boundary conditions on the control surface or distributed over the control volume. Wildfire propagation models may be (i) data-driven ; (i i) theoretical ; or (i i i) mechanistic surrogates. Data-driven models are beyond the scope of this paper. Theoretical models are based on conservation laws (species, energy, mass, momentum) and are, therefore, computationally intensive; e.g. the Fire Dynamics Simulator (FDS). Mechanistic surrogate models do not closely follow fire dynamics laws, but related laws observed to make predictions more efficiently with sufficient accuracy; e.g. FARSITE, and FDS with the Level Set model (FDS-LS). Whether theoretical or mechanistic surrogate, these wildfire models may be coupled with or decoupled from wind models (e.g. Navier-Stokes equations). Only coupled models account for the effect of the fire on the wind field. In this paper, a series of simulations of wildfire propagation on grassland are performed using FDS-LS to study the impact of the fire-induced wind on the fire propagation dynamics. Results show that coupling leads to higher Rates of Spread (RoS), closer to those reported from field experiments, with increasing wind speeds and higher terrain slopes strengthening this trend. Aiming to capture the fire–wind interaction without the hefty cost of solving Navier-Stokes equations, a conceptual framework is proposed: 1) a swarm of unmanned aerial vehicles measure wind velocities at flight height; 2) the wind field is constructed with the acquired data; 3) the high-altitude wind field is mapped to near-surface, and 4) the near-surface wind field is fed into the wildfire model periodically. A series of simulations are performed using an in-house decoupled physics-based reduced-order fire propagation model (FireProM-F) enhanced by wind field "measurements". In this proof of concept, wind velocities are not measured but extracted from physics-based Large Eddy Simulations taken as ground truth. Unsurprisingly, higher measurement frequencies lead to more accurate and realistic predictions of the propagating fire front. An initial attempt is made to study the effect of wind measurement uncertainty on the model predictions by adding Gaussian noise. Preliminary results show that higher noise leads to the fire front displaying more irregular shapes and slower propagation. • Higher wildfire model prediction accuracy normally achieved by coupling it with wind model such as Navier-Stokes equations. • Coupled fire-wind models often too computationally expensive for real-time use in operational contexts. • Prediction accuracy of wildfire model uncoupled from wind model enhanced by periodic inputs of updated wind field. • Wind field to be constructed from UAV swarm measurements, surrogated here to velocities obtained from full CFD simulations. • Proposed method to enhance wildfire model predictions robust to Gaussian noise added to represent measurement uncertainty. [ABSTRACT FROM AUTHOR]

A specific fluid dynamics related feature associated with application of corrugated sheet structured packings in distillation columns operated under deep vacuum conditions is that the vapour ascends through a sparsely irrigated packed bed under laminar flow conditions. In the present paper the well-known Delft Model (DM) and two ad-hoc arranged fully appropriate predictive models have been evaluated in this respect using experimental evidence obtained in total reflux vacuum distillation tests carried out with conventional and advanced geometries of a well-established wire-gauze corrugated sheet structured packing. Shortcomings and strong points of compared predictive models, which have exhibited large discrepancies with respect to relative magnitude of governing flow resistances, have been addressed, and upon a tedious elaboration the way paved for upgrading DM accordingly. As shown in present paper, a simple fundamentally sound correction of existing expressions for gas-gas interaction under laminar and turbulent flow conditions, and implementation of empirically founded expressions to account for previously unrecognized form drag related losses, occurring in case of conventional packing due to a rather sharp disrupted change in gas flow direction at transitions between packing layers, resulted in a remarkable advancement in performance of the upgraded Delft model (DM) in all cases. Yet, a further in-depth research effort is necessary to unravel true nature and scale of governing flow resistances. • In deep vacuum distillations vapour ascends under laminar flow conditions. • Preferred contactor is a corrugated sheet structured packing made of wire gauze. • Unrecognized sources of pressure loss identified and quantified. • Special modelling features related to laminar vapour flow conditions revealed. • Delft model upgraded accordingly reproduces experimental data amazingly well. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%. • The economy and durability of vehicular fuel cell system are both considered. • The LSTM neural network is imported into MPC theory for vehicular power prediction. • The key parameters of MPC and LSTM are determined by referring to the greedy algorithm. • The computational load in MPC optimisation is reduced by simplifying DP algorithm. • The real-time performance of the supposed EMS on-board is validated. [ABSTRACT FROM AUTHOR]

This paper explores the characteristics of a two-step procedure (dimension reduction and function approximation) in discrete choice modeling with high-dimension data. This study proposes the SS-GAM procedure which is an extension of the Super Sparse Principal Component Analysis (SSPCA) where the results are further processed with the Generalized Additive Model (GAM) in a classification problem. Moreover, the Orthogonal Sparse Principal Component Analysis with GAM (OS-GAM) is also proposed. For baseline comparison, the General Adaptive Sparse-PCA with GAM (GAS-GAM) is considered in this paper. The performance of these three sparse PCA methods are investigated with varied underlying correlation structure. In the simulation study, it is demonstrated that with varied degree of dimensionality, and levels of correlation structure, SS-GAM performed better compared to OS-GAM and GAS-GAM in terms of its predictive rate, on the average. It was observed that the OS-GAM performed best when data exhibits low correlation structure. However, with high correlation structure, OS-GAM and GAS-GAM obtained comparable result. Moreover, in terms of computational time, OS-GAM seems to be not affected by the increase of feature dimension. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

Against the background of growing concern about climate change, this study attempts to examine the impact of US climate policy uncertainty on exchange rate volatility. Specifically, this paper explores whether climate policy changes and their extreme observations, as well as the short-term asymmetry of exchange rate volatility, can help predict future exchange rate volatilities. The in-sample estimation results show that the uncertainty of US climate policy significantly affects the volatility of the US exchange rate, and extreme positive shocks of climate policy uncertainty have a greater impact on the exchange rate than extreme negative shocks of the same magnitude. In addition, we find significant asymmetries in the short-term components of exchange rate volatility, especially in the USD-CNY exchange rate and the USD-EUR exchange rate. Multiple out-of-sample forecasting tests show that models incorporating extreme values of climate policy uncertainty exhibit superior forecasting performance. Notably, the predictive performance of these models tends to be stronger during periods of low volatility and relatively weak during periods of high volatility. This paper provides valuable insights for stakeholders to make informed decisions and optimize strategies in the face of uncertain climate policies. [ABSTRACT FROM AUTHOR]

• To solve the forecasting problems of time-lead and nonlinearity in a system, a novel grey multivariable model is proposed. • To reduce the morbidity of the model, the solution of the proposed model is deduced through a derivation method. • The grey prediction evolution algorithm is employed to find the optimal value of parameters. • The validity of the new model was verified by three examples and applied to house price forecasting. Time lags or leads and non-linearity of complex systems are critical to address in accurate scientific prediction. However, few studies have focused on time-lead and nonlinear models with limited information. Therefore, this paper aims to develop a novel grey multivariate model with time-lead operator τ and nonlinear factor r from the perspective of this problem. Next, the solution of the proposed model is deduced through a derivation method to reduce the morbidity of the model. Then, the parameter identification of the model is given on the basis of the least-squares method. The grey prediction evolution algorithm is employed to find the optimal value of parameters τ and r to meet the minimum errors. Finally, house prices of Wuhan, Beijing and Shanghai in China are performed to verify the validity and reliability of the new model. The results demonstrate the model we proposed has satisfactory prediction precision, demonstrating that time-lead and nonlinear factor in the model is reasonable. At the end of the paper, the house prices of these three cities from 2022 to 2028 are also predicted. The average annual growth rate in the three regions is 2.49%, 3.57% and 2.97%, respectively. These results are useful to policy analysis regarding house price correlations. They provide insights for market investors and policy makers. [ABSTRACT FROM AUTHOR]

• A novel combined interval prediction algorithm is proposed in this paper. • Hybrid feature selection strategy is designed in the proposed system. • Multi-objective optimization mechanism reflects strong search capabilities. • Four interval prediction models cover the inherent modes of sequence. Traditional point prediction approaches can not reflect the uncertainty, which brings greater risks to decision-makers. To fill this gap, this paper extends a feature selection strategy that relies solely on correlation and redundant feature judgment, proposes a novel combined interval prediction algorithm, 3-Mcip (Combined Interval Prediction Based on Maximize Relevancy, Minimize Redundancy and Maximize Synergy) system, and solves the tradeoff between prediction accuracy and interval width. This system first designs a hybrid feature selection strategy to optimally select candidate variables and reduce model input redundancy. Secondly, the structure of the four ANN models is improved to accommodate the results of feature selection, and an optimization mechanism is introduced to search for the Pareto optimal solution set. In order to measure the comprehensive performance of the 3-Mcip system, hourly power load data and related candidate variables from Pittsburgh and Washington, D.C are considered. The numerical results show that the 3-Mcip system has coverage rates of 53.3333, 90.1667, and 99.4479 for Site 1 at different levels of interval width coefficients, which not only achieves perfect prediction of power load but also analyzes uncertainty. It is also helpful for power system managers to better capture the fluctuation range of future load and improve the flexibility of smart grid dispatching. [ABSTRACT FROM AUTHOR]

Post-Traumatic Stress Disorder (PTSD) is a prevalent and disabling disorder with a high degree of comorbidity. Clinical studies have focused on hallucinations, which could be associated with the severity of the disorder and treatment resistance. Auditory illusions have received little attention so far, possibly because they are particularly difficult to assess. However, they may impact functioning, and underdiagnosis could impair prognosis. To provide a clearer understanding of PTSD psychopathology, this paper proposes to focus on these auditory illusions. A monocentric case-control study was conducted on 30 subjects with PTSD and 30 controls, based on an original design. False recognitions were estimated during a sound test created with a vocoder. Additionally, differences in emotional valence, dissociation, hyperarousal, and reliving were assessed. The study found that individuals with PTSD experience a higher frequency of auditory illusions compared to healthy controls (65% versus 20%, p < 0.001). Additionally, the emotional valence of these illusions was more negative in individuals with PTSD than in controls. The study also identified a correlation between dissociation symptoms, hyperarousal and reliving with auditory illusions. These findings are in line with neurobiological studies of PTSD, as well as cognitive predictive models, and support the hypothesis of a significant prevalence of auditory illusions in PTSD. Like hallucinations, auditory illusions may be influenced by dissociation. Although these results are preliminary, they suggest a need for further investigation into auditory illusions in PTSD and their effect on prognosis. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed model predictive control (DMPC) for heterogeneous connected vehicle platoon (CVP) under denial-of-service (DoS) attacks. Firstly, a dynamic event-triggering mechanism (DETM) based on the information interaction between vehicles is proposed to reduce the communication and computational burdens. Due to the fact that the triggering moment for each vehicle cannot be synchronized and DoS attacks can break the communication between vehicles, a packet replenishment mechanism is designed to ensure the integrity and effectiveness of information interaction. Then, the effect of external disturbance is handled by adding robustness constraints to the DMPC algorithm. In addition, the recursive feasibility of the DMPC algorithm and input-to-state practical stability (ISPS) of the CVP control system are demonstrated. Finally, the effectiveness of the algorithm is verified by simulation and comparison results. • A general model for the longitudinal dynamics of heterogeneous vehicles with external disturbances is considered. • A secure platooning control protocol is designed for automated vehicles under DoS attacks. • A distributed model predictive control strategy is designed for each follower vehicle. • A dynamic event-triggering mechanism is designed to reduce the communication and computational burdens. [ABSTRACT FROM AUTHOR]

Accurate estimation of natural gas consumption plays a crucial role in energy planning, economic stability, environmental protection, and investment decisions. Despite numerous predictive studies related to natural gas consumption, most of these studies focus on univariate prediction tasks, lacking a reasonable and efficient multivariate prediction approach. To obtain precise forecasting results, this paper constructs a new adaptive grey multivariate prediction model called AGMPM(r,N) based on the fractional-order accumulation operation and grey system theory. It is found that AGMPM(r,N) can degenerate into some grey multivariable prediction models by replacing its own hyperparameters, which reflects its uniformity. In addition, the new model is unbiased for some special time series. In particular, the grey wolf optimizer is used to facilitate the model solution process. To validate the effectiveness of AGMPM(r,N), AGMPM(r,N) and 27 competing algorithms (9 natural gas consumption prediction methods, 10 multivariate prediction models, 7 machine learning algorithms and a statistical predictive model) are used to study the natural gas consumption in China. In addition, ablation experiments are executed. The results of experimental analysis show that the MAPE and MAE of AGMPM(r,N) are 2.795% and 89.880, respectively, which are superior to all competing methods and ablation models. [ABSTRACT FROM AUTHOR]

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction. [ABSTRACT FROM AUTHOR]

To accurately, efficiently and stably predict the thermal displacement of the spindle, a prediction model based on the beluga whale algorithm (BWO) optimized bi-directional long and short-term memory neural network (BiLSTM) is introduced in this paper. Firstly, the thermal characterization and simulation analysis of the spindle are carried out, and the temperature and thermal displacement change characteristics of the spindle are obtained. Then the thermal deformation experiment of the spindle is carried out, and the temperature and displacement sensors are set up reasonably according to the temperature and thermal displacement change characteristics of the spindle, and the experimental data are collected and analyzed. The adaptive and globally convergent BWO is selected to optimize network parameters of BiLSTM, and the BWO-BiLSTM prediction model is constructed by learning the nonlinear correlation characteristics between spindle temperature and axial thermal displacement. The constructed BWO-BiLSTM prediction model is compared with other prediction models, and it is found through analysis that the prediction results output from the BWO-BiLSTM model have better accuracy and stability. The results of the study can provide a certain theoretical basis and technical support in predicting the spindle thermal displacement, which can help to promote the precision machining production of electric spindles. • Spindle thermal characterization and simulation studies are carried out. • Significant changes in axial temperature and thermal displacement are found. • Sensors are set up according to the results of studies. • A BWO-BiLSTM model is built to predict the spindle thermal displacement. [ABSTRACT FROM AUTHOR]

This paper examines the predictions of expectations-based reference-dependent models for risk-apportionment tasks that elicit higher-order risk attitudes. We consider some of the most commonly used specifications of Kőszegi and Rabin (2006, 2007) and disappointment aversion models. Our analysis reveals that higher order risky choices exhibited by decision makers defined by those model specifications depend on whether risks to be apportioned in these tasks are symmetric or asymmetric, whether they include small probability outcomes, and on the level of loss aversion. We highlight that some of the predicted choice behaviour in the risk-apportionment tasks differs from the ones in alternative models of decision under risk. We employ experimental data to examine whether choice patterns in the risk apportionment tasks are in line with the predictions of the model specifications described here. We find that only a small proportion of them are consistent with those predictions. [ABSTRACT FROM AUTHOR]

Accurate prediction of crack extension is crucial for ensuring the structural integrity of metal components exposed to diverse loads. While the Gurson model and its extensions are widely accepted for delineating ductile fracture stages, especially in porous materials with a rigid-perfectly plastic matrix, their efficacy diminishes in the presence of strain-hardening matrices. To address this limitation, our study introduces a robust numerical implementation and assessment of Perrin's model. In contrast to Gurson's approach, Perrin's model incorporates two distinct strain-hardening parameters derived from an intricate analysis of a strain-hardening hollow sphere subjected to axisymmetric loading. Moving beyond theoretical discussions, this paper actively engages in the rigorous evaluation of Perrin's model under various scenarios, encompassing isotropic, kinematic, and mixed isotropic-kinematic hardening conditions. The model's effectiveness is convincingly demonstrated through meticulous comparisons between numerical simulations and real-world experimental observations conducted on pre-cracked specimens. Facing challenges related to achieving global elastic-plastic convergence in large-scale simulations, our research introduces a sophisticated approach. Leveraging stiffness tangent moduli ensure the maintenance of quadratic convergence in global Newton method iterations. This not only enhances the reliability of our simulations but also underscores the practical applicability of our findings. The study also focuses on the necking and crack propagation of a cylindrical specimen through meticulous computational modeling. Our mesh captures intricate details and addresses stress gradients, revealing coplanar crack propagation contrary to prior beliefs. The confirmation of the cup-cone fracture effect challenges previous interpretations, emphasizing the critical role of void nucleation. This reinforces the credibility and applicability of our approach in advancing the understanding of material fracture behavior. In summary, this study significantly contributes to existing knowledge in fracture prediction models, offering a robust framework for predicting ductile fracture under substantial deformations. The presented findings pave the way for advancements in structural engineering, providing invaluable insights for optimizing the performance and resilience of metal structures in real-world applications. • We present a numerical implementation and assessment of a model for ductile fracture, considering three hardening scenarios. • We discuss the numerical implementation of the model and address the projection problem algorithm for three hardenings • We introduce a numerical integration strategy for handling significant deformations and rotations in complex structures. • We demonstrate the model effectiveness by comparing numerical simulations and experiments in pre-cracked specimens. [ABSTRACT FROM AUTHOR]

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader's speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations. • A hierarchical distributed model predictive control for multi-linear motor traction systems is introduced. • The nonlinear disturbance observers are adopted to improve the system's robustness. • A strict proof is constructed for the Lyapunov criterion of asymptotic stability. [ABSTRACT FROM AUTHOR]

With increasing quality requirements, especially for safety-critical products such as in medical technology, manufacturing companies face the challenge, that the quality inspection of component characteristics may become a crucial factor for economic production. For increasing the ratio of correctly identified scrap without increasing the amount of physical inspection, prediction models pose a valuable tool. Prediction models in manufacturing build process models through process data collected during the manufacturing process. The prediction models can for example be used to predict the product quality from the process data, which enables the substitution of physical quality inspections through virtual inspections. Before selecting suitable prediction models, checkpoints in the manufacturing process and inspection sequence (MPIS) for integrating the prediction models profitably have to be identified. Therefore, the paper aims to provide a methodology to generate integrated quality strategies for MPIS that combine physical inspection with the prediction of quality characteristics. Furthermore, a methodology for the economic evaluation of the quality strategies as decision support is provided. Since the number of prediction models available for predicting quality based on process data is manifold, the explicit recommendation for the implementation of a certain prediction model is not the focus of this paper. The methodology presented rather focuses on the identification of suitable checkpoints in the MPIS for prediction model integration. For generating quality strategies suitable checkpoints in the manufacturing process sequence have to be identified. Therefore, three aspects are considered, the influence of the manufacturing process on the change in a characteristic and the overall importance of a characteristic for the component function as well as the ability of the characteristic and process for being predicted. A prediction recommendation is calculated that enables the derivation of quality strategies as a combination of physical and virtual inspection for different characteristics in different checkpoints. For the economic evaluation of the derived quality strategies two cases for supplementing physical inspection with prediction models to either compensate for the alpha- or beta-error rate are presented. The most reasonable quality strategy is chosen according to its economic potential. For validation, the applicability of the methodology for integrating prediction models in manufacturing process and inspection sequences to a use case of medical technology is demonstrated successfully. It is shown that the methodology supports users in identifying possible quality strategies and their economic evaluation for manufacturing process and inspection sequences as a basis for the decision regarding quality strategies to be implemented. [ABSTRACT FROM AUTHOR]

Flexible dynamic operation of ammonia synthesis is a key enabling factor of green ammonia production in the face of intermittency in renewable energy sources. A nonlinear model predictive control scheme is presented in this paper for the Haber–Bosch ammonia synthesis process with a varying feed flowrate. The considered process consists of three interstage-cooled reactor beds and a flash separator, with a flexible reactor load varying between 50% and 100% of its nominal capacity. The proposed control scheme aims to control the reactor temperatures, separation pressure, and the liquid volume in the flash tank during feed transitions. A simulation study is performed given an assumed 5-hour feed schedule. The results indicate that all controlled variables are maintained in a safe operating range and tracked with small offsets in the nominal case as well as under disturbances in the reaction rate. It takes approximately 10 min for the reactor temperatures and 40 min for the separation pressure to reach steady state after a large step change in the reactor load. In a comparison with a PID control scheme, the investigated NMPC scheme tracks feed temperatures and the separation pressure significantly faster with less oscillations. This study therefore demonstrates the feasibility and effectiveness of the proposed control scheme in enabling flexible operation of the process. [ABSTRACT FROM AUTHOR]

High precision ionospheric Total Electron Content(TEC) prediction is of great significance for improving the accuracy of Global Navigation Satellite System(GNSS), preventing natural disasters, and ensuring wireless communication. Given the varying frequencies of TEC signals, a hybrid CEEMDAN-BiLSTM-PSO-LSSVM-FE model for predicting ionospheric TEC content is proposed in this paper. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the extracted hourly TEC sequence and calculate the fuzzy entropy (FE) of the subsequences. Then, the signal is divided into high-frequency and low-frequency parts based on the fuzzy entropy value, for the high-frequency component, Bidirectional Long Short-Term Memory network (BiLSTM) is used for prediction; for the low-frequency component, Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) is used for prediction. The hourly TEC values collected from six evenly distributed GPS stations in China are used as the main input variable for the proposed model, with solar and geomagnetic activity data used as auxiliary data, using the TEC data from the previous 48 h to forecast the TEC content for the next hour. The performance of the model is evaluated by comparing it with other models under different geographical locations, seasons, solar and geomagnetic activity conditions. Experimental results show that the proposed hybrid model outperforms other models, with a correlation coefficient R2 and root mean square error (RMSE) of 0.99 and 0.20 TECU, respectively. The model effectively overcomes the problem of low TEC prediction accuracy and can provide more precise ionospheric delay correction services. [ABSTRACT FROM AUTHOR]

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control—cross-coupling control with deep neural network feedforward (NMPC-CCC-DNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPC-CCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller. • The nonlinear model of an asymmetric multi-hydraulic system is established. • The entirety model-type solution is proposed with nonlinear model predictive control and cross-coupling control algorithm. • A novel disturbance compensator based on the inverse deep neural network model is proposed. • Results showed the proposed controller can decrease 60.8% and up to 91.1% sysnchronization root mean square error. [ABSTRACT FROM AUTHOR]