Harmonious Concordance of Men, Women and Nature: A Study of Lawrence’s Ecological Philosophy in His Lady Chatterley’s Lover

D. H. Lawrence stands as a talented and unconventional writer in the twentieth century English Literature. Lady Chatterley’s Lover is his last novel which embodies his mature thought. The novel earns him both great fame and strong criticism. In spite of the controversies over Lawrence’s daring description of sexuality, the novel stands the test of time and becomes a classic of literature. The paper intends to reveal Lawrence’s ecological philosophy in Lady Chatterley’s Lover. By depicting harmonious nature and harmonious sex relationship, Lawrence presents his ecological philosophy. In the novel, harmonious nature is a silent protest against industrial civilization reflected by the contrast between Wragby and the wood. The harmonious sex relationship in nature is a great liberation of suppressed human nature. The disharmonious relationship between Clifford and Connie is like the deadwood lacking vitality, while the harmonious sex relationship between Mellors and Connie is like intertwining shoots which give mutual supports and vigor. Lady Chatterley’s Lover reflects Lawrence’s far-reaching ecological views and his concern about the whole ecosphere which embodies his strong social responsibility.

Output Feedback Fractional-Order Nonsingular Terminal Sliding Mode Control of Underwater Remotely Operated Vehicles

For the 4-DOF (degrees of freedom) trajectory tracking control problem of underwater remotely operated vehicles (ROVs) in the presence of model uncertainties and external disturbances, a novel output feedback fractional-order nonsingular terminal sliding mode control (FO-NTSMC) technique is introduced in light of the equivalent output injection sliding mode observer (SMO) and TSMC principle and fractional calculus technology. The equivalent output injection SMO is applied to reconstruct the full states in finite time. Meanwhile, the FO-NTSMC algorithm, based on a new proposed fractional-order switching manifold, is designed to stabilize the tracking error to equilibrium points in finite time. The corresponding stability analysis of the closed-loop system is presented using the fractional-order version of the Lyapunov stability theory. Comparative numerical simulation results are presented and analyzed to demonstrate the effectiveness of the proposed method. Finally, it is noteworthy that the proposed output feedback FO-NTSMC technique can be used to control a broad range of nonlinear second-order dynamical systems in finite time

Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network

The electricity consumption forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of electricity consumption forecasting. A method of probability density forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is proposed in this paper. First, important features are extracted from external factors affecting the electricity consumption forecasting by LASSO regression. Then, the LASSO-QRNN model is constructed to predict annual electricity consumption. The results of electricity consumption forecasting under different quantiles in the next several years are evaluated. Besides, we introduce kernel density estimation into our LASSO-QRNN model, which can give a probability distribution instead of a single-valued prediction. The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States. The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting, in comparison with existing quantile regression neural network (QRNN), back-propagation of errors neural network (BP), radial basis function neural network (RBF), quantile regression (QR) and nonlinear quantile regression (NLQR). LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting, but also provide more precise results

Particle velocity measurement of binary mixtures in the riser of a circulating fluidized bed by the combined use of electrostatic sensing and high-speed imaging

Zhang WB acknowledges the financial supports from the National Natural Science Foundation of China (No. 61403138) and Beijing Natural Science Foundation (No. 3202028). Zhan W and Wang CH acknowledge the research programme funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Grant Number R-706-001-102–281, National University of Singapore.Peer reviewedPublisher PD

Adsorption–desorption behavior of malachite green by potassium permanganate pre-oxidation polyvinyl chloride microplastics

Microplastics (MPs) and the typical hydrophilic organic pollutant Malachite green (MG) are frequently detected in sewage treatment plants. Potassium permanganate (KMnO4) pre-oxidation is an economical and effective technology in wastewater treatment. It is important to study the surface physicochemical characteristics of MPs and understand their fate in wastewater treatment plants after pre-oxidation. In this study, Polyvinyl chloride (PVC) MPs were treated by single and composite KMnO4 pre-oxidation with different pH values. After the pre-oxidation treatment, the appearance of Osingle bondMn spectra and surface nanoparticles indicated the oxides (MnO2) were produced on the MPs surface. Moreover, the adhesion of MnO2 is helpful to improve the hydrophilicity and adsorption capacity of MG. The adsorption capacity of pristine PVC for MG was 2.6 mg/g. But the adsorption capacity increased to 7.0 mg/g for single oxidation and 140.7 mg/g for composite oxidation, respectively. The desorption experiment results indicate the pre-oxidation process could reduce the release efficiency of MG from the PVC MPs due to the better binding of surface MnO2 nanoparticles to MG. However, the total desorption capacity is still high. which illustrates that there is a high potential risk of MG which can transfer from the surface of the PVC MPs to the gastrointestinal fluids.publishedVersio

Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control

Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient's surgical care. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning anesthesia strategies on real clinical datasets, is proposed. Conservative Q-Learning was first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is added to agent training to keep the policy distribution of the agent and the anesthesiologist consistent to ensure safer decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approach while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated, which were able to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions to increase the transparency of the model.Comment: 11 pages, 7 figure

Desorption of sulfamethoxazole from polyamide 6 microplastics: Environmental factors, simulated gastrointestinal fluids, and desorption mechanisms

Microplastics (MPs) can enrich pollutants after being released into the environment, and the contaminants-loaded MPs are usually ingested by organisms, resulting in a potential dual biotoxic effect. In this paper, the adsorption behavior of Sulfamethoxazole (SMX) on Polyamide 6 (PA6) MPs was systematically investigated and simulated by the kinetic and isotherm models. The effect of environmental conditions (pH, salinity) on the adsorption process was studied, and the desorption behavior of SMX-loaded PA6 MPs was focused on simulating the seawater, ultrapure water, gastric and intestinal fluids. We found that lower pH and solubilization of SMX by gastrointestinal components (bovine serum albumin (BSA), sodium taurocholate (NaT), and pepsin) can reduce the electrostatic interaction between the surface charge of PA6 MPs and SMX. The result will lead to an increase in the desorption capacity of SMX-loaded PA6 MPs in gastrointestinal fluids and therefore will provide a reasonable mechanism for the desorption of SMX-loaded PA6 MPs in the gastrointestinal fluids. This study will provide a theoretical reference for studying the desorption behavior of SMX-loaded PA6 MPs under gastrointestinal conditions.publishedVersio