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With the rapid development of industrialization, the problem of heavy metal wastewater treatment has become increasingly serious, posing a serious threat to the environment and human health. Biochar shows great potential for application in the field of wastewater treatment; however, biochars prepared from different biomass sources and experimental conditions have different physicochemical properties, resulting in differences in their adsorption capacity for uranium, which limits their wide application in wastewater treatment. Therefore, there is an urgent need to deeply explore and optimize the key parameter settings of biochar to significantly improve its adsorption capacity. This paper combines the nonlinear mapping capability of SCN and the ensemble learning advantage of the Adaboost algorithm based on existing experimental data on wastewater treatment. The accuracy of the model is evaluated by metrics such as coefficient of determination (R2) and error rate. It was found that the Adaboost–SCN model showed significant advantages in terms of prediction accuracy, precision, model stability and generalization ability compared to the SCN model alone. In order to further improve the performance of the model, this paper combined Adaboost–SCN with maximum information coefficient (MIC), random forest (RF) and energy valley optimizer (EVO) feature selection methods to construct three models, namely, MIC-Adaboost–SCN, RF-Adaboost–SCN and EVO-Adaboost–SCN. The results show that the prediction model with added feature selection is significantly better than the Adaboost–SCN model without feature selection in each evaluation index, and EVO has the most significant effect on feature selection. Finally, the correlation between biochar adsorption properties and production parameters was discussed through the inversion study of key parameters, and optimal parameter intervals were proposed to improve the adsorption properties. Providing strong support for the wide application of biochar in the field of wastewater treatment helps to solve the urgent environmental problem of heavy metal wastewater treatment. [ABSTRACT FROM AUTHOR]

Employing molecular dynamics software, three models—vacuum–cellulose, nitrogen–cellulose, and air–cellulose—were built to clarify, via a microscopic perspective, the macroscopic changes in single-chain cellulose undergoing vacuum, nitrogen, and air heat treatments. Kinetic simulations were run following model equilibrium within the NPT system of 423, 443, 463, 483, and 503 K. The energy variations, cell parameters, densities, mean square displacements, hydrogen bonding numbers, and mechanical parameters were analyzed for the three models. The findings demonstrate that as the temperature climbed, the cellular characteristics among two models—the nitrogen and vacuum models—decreased and subsequently increased. The nitrogen model reached its lowest value at 443 K. In contrast, the vacuum model reached its minimum value at 463 K. The vacuum heat treatment may enhance the structural stability of the single-chain cellulose more effectively than the nitrogen and air treatments because it increases the number of hydrogen bonds within the cellulose chain and stabilizes the mean square displacement. Furthermore, the temperature has an impact on the mechanical characteristics of the cellulose amorphous zone; the maximum values of E and G for the vacuum and nitrogen models are found at 463 and 443 K, respectively. The Young's modulus and shear modulus were consistently more significant for the vacuum model at either temperature, and the Poisson's ratio was the opposite. Therefore, the vacuum heat treatment can better maintain wood stiffness and deformation resistance, thus improving wood utilization. These findings provide an essential theoretical basis for wood processing and modification, which can help optimize the heat treatment and enhance wood's utilization and added value. [ABSTRACT FROM AUTHOR]

In this paper, an Improved Pelican Optimization Algorithm (IPOA) was proposed to optimize a BP neural network model to predict the dielectric loss factor of wood in the RF heating and drying process. The neural network model was trained and optimized using MATLAB 2022b software, and the prediction results of the BP neural network with POA-BP and IPOA-BP models were compared. The results show that the IPOA-optimized BP neural network model is more accurate than the traditional BP neural network model. After the BP neural network model with IPOA optimization was used to predict the dielectric loss factor of wood, the value increased by 4.3%, the MAE decreased by 68%, and the RMSE decreased by 67%. The results provided by the study using the IPOA-BP model show that the prediction of the dielectric loss factor of wood under different macroscopic conditions in radio frequency heating and drying of wood can be realized without the need for highly costly and prolonged experimental studies. [ABSTRACT FROM AUTHOR]

Recently, Japan's discharge of wastewater from the Fukushima nuclear disaster into the ocean has attracted widespread attention. To effectively address the challenge of separating uranium, the focus is on finding a healthy and environmentally friendly way to adsorb uranium using biochar. In this paper, a BP neural network is combined with each of the four meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), Differential Evolution (DE), Cheetah Optimization (CO) and Fick's Law Algorithm (FLA), to construct four prediction models for the uranium adsorption capacity in the treatment of radioactive wastewater with biochar: PSO-BP, DE-BP, CO-BP, FLA-BP. The coefficient of certainty (R2), error rate and CEC test set are used to judge the accuracy of the model based on the BP neural network. The results show that the Fick's Law Algorithm (FLA) has a better search ability and convergence speed than the other algorithms. The importance of the input parameters is quantitatively assessed and ranked using XGBoost in order to analyze which parameters have a greater impact on the predictions of the model, which indicates that the parameters with the greatest impact are the initial concentration of uranium (C0, mg/L) and the mass percentage of total carbon (C, %). To sum up, four prediction models can be applied to study the adsorption of uranium by biochar materials during actual experiments, and the advantage of Fick's Law Algorithm (FLA) is more obvious. The method of model prediction can significantly reduce the radiation risk caused by uranium to human health during the actual experiment and provide some reference for the efficient treatment of uranium wastewater by biochar. [ABSTRACT FROM AUTHOR]

A paraffin–cellulose composite model was created using Materials Studio software, establishing a cellulose chain with a polymerization degree of 20 and paraffin molecules. A theoretical foundation for the research of wood heat treatment was established by explaining the changes in macroscopic qualities, such as mechanical properties, water absorption, etc., from a microscopic point of view. The model tended to a stable state with lower energy through geometric optimization and kinetic relaxation. The dynamics simulation was run based on this condition at a total of five different temperatures: 100 °C, 150 °C, 170 °C, 190 °C, and 210 °C. The energy balance, paraffin molecular diffusion coefficients, cell parameters and densities, hydrogen bonding numbers and mechanical parameters of the paraffin–cellulose composite model were analyzed. The results demonstrated that the paraffin diffusion range increased with temperature, and the paraffin diffusion coefficient was greatest at 210 °C, which also resulted in the maximum cell volume, the lowest density, and the lowest water absorption at this temperature. On the other hand, the paraffin–cellulose hybrid model had the most hydrogen bonds and the most stable system at 100 °C. The mechanical properties of the amorphous cellulose region of wood are influenced by temperature. According to the calculation of Lamé constants, with the system's temperature rise, Young's modulus (E) and shear modulus (G) were maximum at 100 °C, indicating the wood's optimal toughness and plasticity. Poisson's ratio and K/G values were largest at 210 °C, indicating the optimal toughness and plasticity of wood. The thermal modification of wood under different conditions can not only retain its natural advantages, but also improve its own performance, expand the application range of wood, and increase the utilization rate of wood. Therefore, the appropriate temperature and other conditions can be selected according to the actual needs of the wood heat treatment, which has significant practical significance for the study of wood heat treatment. [ABSTRACT FROM AUTHOR]

Visualizations often appear in multiples, either in a single display (e.g., small multiples, dashboard) or across time or space (e.g., slideshow, set of dashboards). However, existing visualization design guidelines typically focus on single rather than multiple views. Solely following these guidelines can lead to effective yet inconsistent views (e.g., the same field has different axes domains across charts), making interpretation slow and error-prone. Moreover, little is known how consistency balances with other design considerations, making it difficult to incorporate consistency mechanisms in visualization authoring software. We present a wizard-of-oz study in which we observed how Tableau users achieve and sacrifice consistency in an exploration-to-presentation visualization design scenario. We extend (from our prior work) a set of encoding-specific constraints defining consistency across multiple views. Using the constraints as a checklist in our study, we observed cases where participants spontaneously maintained consistent encodings and warned cases where consistency was overlooked. In response to the warnings, participants either revised views for consistency or stated why they thought consistency should be overwritten. We categorize participants' actions and responses as constraint validations and exceptions, depicting the relative importance of consistency and other design considerations under various circumstances (e.g., data cardinality, available encoding resources, chart layout). We discuss automatic consistency checking as a constraint-satisfaction problem and provide design implications for communicating inconsistencies to users. [ABSTRACT FROM PUBLISHER]