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Propofol and fentanyl are commonly used agents for the induction of anesthesia, and are often associated with hemodynamic disturbances. Understanding pharmacodynamic impacts is vital for parasympathetic and sympathetic tones during the anesthesia induction period. Inspired by the thermodynamic interaction between drug concentrations and effects, we established a machine-learning-based response surface model (MLRSM) to address this predicament. Then, we investigated and modeled the biomedical phenomena in the autonomic nervous system. Our study prospectively enrolled 60 patients, and the participants were assigned to two groups randomly and equally. Group 1 received propofol first, followed by fentanyl, and the drug sequence followed an inverse procedure in Group 2. Then, we extracted and analyzed the spectrograms of electrocardiography (ECG) and pulse photoplethysmography (PPG) signals after induction of propofol and fentanyl. Eventually, we utilized the proposed MLRSM to evaluate the relationship between anesthetics and the integrity/balance of sympathetic and parasympathetic activity by employing the power of high-frequency (HF) and low-frequency (LF) bands and PPG amplitude (PPGA). It is worth emphasizing that the proposed MLRSM exhibits a similar mathematical form to the conventional Greco model, but with better computational performance. Furthermore, the MLRSM has a theoretical foundation and flexibility for arbitrary numbers of drug combinations. The modeling results are consistent with the previous literature. We employed the bootstrap algorithm to inspect the results’ consistency and measure the various statistical fluctuations. Then, the comparison between the modeling and the bootstrapping results was used to validate the statistical stability and the feasibility of the proposed MLRSM.

The mechanical failure of battery-pack systems (BPSs) under crush and vibration conditions is a crucial research topic in automotive engineering. Most studies evaluate the mechanical properties of BPSs under a single operating condition. In this study, a dual-objective optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed to evaluate the crushing stress of BPS modules and the vibration fatigue life of the BPS. This method can obtain better combinations of the thicknesses of the BPS components, which helps engineers achieve robust and efficient designs. First, a nonlinear finite element (FE) model of a BPS is developed and experimentally verified. The crush and vibration simulations are performed, and the FE analysis data are obtained. Second, two third-order response surface models are created to characterize the relationship between the input (thicknesses of the BPS components) and the output (crushing stress of the BPS modules and vibration fatigue life of the BPS). Finally, a linear weighting model and an NSGA-II model are used to conduct dual-objective optimization. The solution of the linear weighting method and the non-dominated Pareto solution set of the thicknesses of the BPS components are obtained and compared. Furthermore, a reasonable interval in the Pareto frontier is defined and considered the best solution to the dual-objective optimization problem. Therefore, the reliability of the BPS is improved to ensure the safety of electric vehicles in crushing and vibration environments. This method offers an effective solution to the problem of evaluating the mechanical responses of BPSs under various operating conditions. It can be used to generate a robust design for safe and durable BPSs.

Evaluating Behind Armor Blunt Trauma (BABT) is a critical step in preventing non-penetrating injuries in military personnel, which can result from the transfer of kinetic energy from projectiles impacting body armor. While the current NIJ Standard-0101.06 standard focuses on preventing excessive armor backface deformation, this standard does not account for the variability in impact location, thorax organ and tissue material properties, and injury thresholds in order to assess potential injury. To address this gap, Finite Element (FE) human body models (HBMs) have been employed to investigate variability in BABT impact conditions by recreating specific cases from survivor databases and generating injury risk curves. However, these deterministic analyses predominantly use models representing the 50th percentile male and do not investigate the uncertainty and variability inherent within the system, thus limiting the generalizability of investigating injury risk over a diverse military population. The DoD-funded I-PREDICT Future Naval Capability (FNC) introduces a probabilistic HBM, which considers uncertainty and variability in tissue material and failure properties, anthropometry, and external loading conditions. This study utilizes the I-PREDICT HBM for BABT simulations for three thoracic impact locations-liver, heart, and lower abdomen. A probabilistic analysis of tissue-level strains resulting from a BABT event is used to determine the probability of achieving a Military Combat Incapacitation Scale (MCIS) for organ-level injuries and the New Injury Severity Score (NISS) is employed for whole-body injury risk evaluations. Organ-level MCIS metrics show that impact at the heart can cause severe injuries to the heart and spleen, whereas impact to the liver can cause rib fractures and major lacerations in the liver. Impact at the lower abdomen can cause lacerations in the spleen. Simulation results indicate that, under current protection standards, the whole-body risk of injury varies between 6 and 98% based on impact location, with the impact at the heart being the most severe, followed by impact at the liver and the lower abdomen. These results suggest that the current body armor protection standards might result in severe injuries in specific locations, but no injuries in others.

This paper develops a robust framework for the multiscale design of three-dimensional lattices with macroscopically tailored structural characteristics. The work exploits the high process flexibility and precision of additive manufacturing to the physical realization of complex microstructure of metamaterials by developing and implementing a multiscale approach. Structures derived from such metamaterials exhibit properties which differ from that of the constituent base material. A periodic microscale model is developed whose geometric parameterization enables smoothly changing properties and for which the connectivity of neighboring microstructures in the large-scale domain is guaranteed by slowly changing large-scale descriptions of the lattice parameters. A lattice-based functional grading of material is derived using the finite element method with sensitivities derived by the adjoint method. The novelty of the work lies in the use of multiple geometry-based small-scale design parameters for optimization problems in three-dimensional real space. The approach is demonstrated by solving a classical compliance minimization problem. The results show improved optimality compared to commonly implemented structural optimization algorithms.

A non-dominated sorting modified teaching–learning-based optimization (NSMTLBO) is proposed to obtain the optimum solution for a multi-objective problem related to machining Polytetrafluoroethylene. Firstly, an experimental design is done and the L27 orthogonal array with three-level of cutting speed Vc, feed rate (f), depth of cut (ap) and nose radius Nr is formulated. A CNC turning machine is used to perform experiments with cemented carbide tool at an insert angle of 80° and the response variables known as surface finish and material removal rate are measured. A response surface model is rendered from the experimental results to derive the minimization function of surface roughness Ra and maximization function of material removal rate (MRR). Both optimization functions are solved simultaneously using NSMTLBO. A fuzzy decision maker is also integrated with NSMTLBO to determine the preferred optimum machining parameters from Pareto-front based on the relative importance level of each objective function. The best responses Ra = 2.2347 µm and MRR = 96.835 cm3/min are predicted at the optimum machining parameters of Vc = 160 mm/min, f = 0.5 mm/rev, ap = 0.98 mm and Nr = 0.8 mm. The proposed NSMTLBO is reported to outperform other six peer algorithms due to its excellent capability in generating the Pareto-fronts which are more uniformly distributed and resulted higher percentage of non-dominated solutions. Furthermore, the prediction results of NSMTLBO are validated experimentally and it is reported that the performance deviations between the predicted and actual results are lower than 3.7%, implying the applicability of proposed work in real-world machining applications.

Radon is a natural radioactive gas, which has great radiation hazards. Mastering the radon exhalation law under complex conditions is of great significance to the safety and stability of uranium tailings pond. Based on the self-developed artificial microclimate chamber, the single factor influence of water content, temperature and crack rate of overburden soil on radon exhalation rate of uranium tailings pond was explored through simulation experiments, and the corresponding single factor mathematical model was obtained. The response surface model of radon exhalation rate was constructed by combining whale optimization algorithm to achieve accurate prediction of radon exhalation rate. The test results of simulated seismic station vibration test also confirm the scientificity of the model. The results of this study can be used not only to guide the prediction and prevention of radon, but also to guide the early warning of uranium tailings pond instability.

To extend the working life of products made of titanium alloy, it is necessary to improve the polishing method to diminish the remaining defects on the workpiece surface. The Halbach array-assisted magnetic abrasive particle polishing method for titanium alloy was employed in this work. The distribution of magnetic field strength was simulated and verified at first to learn the characteristics of the Halbach array used in this work. Then, the polishing performance of the polishing tool was studied by conducting the polishing test, which aimed to display the relationship between shear force and surface roughness with polishing time, and the surface morphology during polishing was also analyzed. Following the establishment of the response surface model, a study on the optimal polishing parameters was conducted to obtain the suitable parameters for maximum shear force and minimum surface roughness. The results show that the maximum shear force 6.11 N and minimum surface roughness Sa 88 nm can be attained, respectively, under the conditions of (1) polishing tool speed of 724.254 r·min−1, working gap of 0.5 mm, and abrasive particle size of 200 μm; and (2) polishing tool speed of 897.87 r·min−1, working gap of 0.52 mm, and abrasive particle size of 160 μm.

Approximately 20% of concentrate will be produced from coal gasification wastewater after reverse osmosis treatment. The organic matter contained in the concentrate affects its evaporation crystallisation; therefore, the refractory organics must be removed. In this study, Cu-Co-Mn/AC catalytic ozonation was used to treat reverse osmosis concentrate (ROC). With the addition of the Cu-Co-Mn/AC catalyst, the production of ·OH increased by 82 μmol/L, thereby enhancing the ozonation performance. The pH, ozone dosage, and catalyst dosage all affected the catalytic ozonation performance. By constructing a response surface model, it was found that the catalyst dosage had the most significant effect on the catalytic ozonation performance. The predicted optimal reaction conditions were pH = 9.02, ozone dosage = 1.08 g/L, and catalyst dosage = 1.33 g/L, under which the chemical oxygen demand (COD) removal reached a maximum of 81.49%.

The findings presented in this paper demonstrate the effectiveness of a new method for analyzing the electroacoustic response of Love wave acoustic sensors to assess the mechanical and dielectric properties of liquid samples. The Love wave sensor was modified by applying a solution of extracellular polymeric substances (EPS) using a self-assembled monolayer technique. The mechanical parameters of the EPS-modified sensors, such as insertion losses and phase, were found to be slightly affected by turbidity, even at high levels, while allowing a chemical detection by mass loading effect. On the other hand, the sensor electrical parameters such as capacitance and resistance showed a positive correlation with increasing turbidity. The experimental chemical detection also showed changes in the mechanical parameters (wave amplitude and phase) of the EPS-modified sensor in response to different concentrations of mercury (Hg 2+ ), ranging from 10E−10 M to 10E−2 M. Furthermore, the study utilized response surface methodology to analyze the nonlinear behavior of the sensor under combined factors of turbidity and mercury concentration. Through this approach, a mathematical model was developed to simulate measurements of capacitance and resistance, considering both turbidity and mercury concentration as variables. This study demonstrated that integrating an EPS-Love wave sensor enables the simultaneous detection of mercury concentration and assessment of water turbidity via changes in capacitance and resistance, underscoring the sensor’s promise for high-performance environmental monitoring.

The paper presents a method of forecasting the product surface topography after five-axis machining with a lens-shaped end-mill. Surface roughness is one of the key parameters considered when assessing the effectiveness of the machining process, especially in the aviation, automotive, tooling and medical equipment industries. The developed method, the first published, presented in the paper is based on the analytical equations of the trajectory of the cutting edge motion, on the basis of which the cutter action surface is generated. The developed model takes into account: cutting depth, cutting width, feed, lead angle and radial runout. Experimental studies were conducted using three different materials: 40HM steel, Al7035 aluminum alloy and Ti Grade 5 titanium alloy. Various values of the cutting width parameters and different feeds were used in the tests. Based on the results of the experimental tests, an empirical model (response surface model) was determined and was then used to verify the simulation model. The simulation results and the results of experimental tests were compared and conclusions were drawn regarding the developed models. The developed models supported by numerical simulation can be used to approximately estimate the influence of the width of cut br and feed ft on selected height characteristics Sa and Sz^ of the geometric structure of the surface (GSS) after machining with a lens-shaped end-mill in terms of the process parameters adopted in the tests. It was found that the influence of the ft on the Sa and Sz^ is greater for small values of br. The effect of br is greater with lower ft values. The cutting width br has the greatest influence on Sa and Sz^, and ft and the interaction of these parameters has the least influence.