Due to the development of the food delivery industry, a large amount of waste lunchboxes made of homo polypropylene (PP) plastic have been generated. This study developed a new technological strategy to effectively regenerate PP from waste lunchboxes. Through response surface curve analysis, it was found that under the optimal process conditions of hot alkali washing at 80 ℃, 30 min, and pH 13, the optimal contact angle was 65.55°, indicating a good oil stain removal effect. By identifying and analyzing the characteristics of impurities in waste lunchboxes, a physical sorting and granulation regeneration process was constructed. And through large-scale statistical analysis and data collection, it was further verified that recycled PP plastics maintained their physical stability and excellent processing performance. The quality stability of recycled PP plastics in terms of impurities content was also verified. By designing different formulations specifically, recycled PP was mixed with different virgin PP and antioxidants in appropriate proportions, and extruded into particles under 150-300 mesh filtration conditions to obtain modified recycled PP. Modified recycled PP was applied in textiles, clothing, and injection molded products. In conclusion, we achieve the up-cylcing of waste PP lunchboxes instead of down-cylcing.

The high-frequency pulse flow, equivalent to the natural frequency of rocks, is generated by a self-excited oscillating cavity to achieve resonance rock-breaking. The flow field and oscillating mechanism of the self-excited oscillating cavity were simulated using the large eddy simulation method of Computational Fluid Dynamics (CFD). A field-scale testing apparatus was developed to investigate the impulse characteristics and verify the simulation results. The results show that the fluid at the outlet at the tool is deflected due to the pulse oscillation of the fluid. The size and shape of low-pressure vortices constantly change, leading to periodic changes in fluid impedance within the oscillating cavity. The impulse frequency reaches its highest point when the length-diameter ratio is 0.67. As the length-diameter ratio increases, the tool pressure loss also increases. Regarding the cavity thickness, the impulse frequency of the oscillating cavity initially decreases, then increases, and finally decreases again. Moreover, both the impulse frequency and pressure loss increase with an increase in displacement. The numerical simulation findings align with the experimental results, thus confirming the validity of the theoretical model. This research provides theoretical guidance for the practical application of resonance rock-breaking technology.

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

Nickel-based superalloy Inconel 718 is widely used in the aerospace industry for its excellent high-temperature strength and thermal stability. However, milling Inconel 718 presents challenges because of the significantly increased cutting force and vibration, since Inconel 718 is a typical difficult-to-machine material. This paper takes the milling process of Inconel 718 as the research object, initially, and a milling force model of Inconel 718 is established. Subsequently, the finite element analysis method is used to analyze the stress field, temperature field, and milling force in the milling process of Inconel 718. Building upon this, a dynamic equation of the milling of Inconel 718 is established, and based on the modal experiment, stability lobe diagrams are drawn. Furthermore, milling experiments on Inconel 718 are designed, and the results calculated using the milling force model and finite element analysis are verified through comparison to the experimental results; then, the fmincon optimization algorithm is used to optimize the processing parameters of Inconel 718. Eventually, the results of the multi-objective optimization illustrate that the best processing parameters are a spindle speed of 3199.2 rpm and a feed speed of 80 mm/min with an axial depth of cut of 0.25 mm. Based on this, the best machining parameters are determined, which point towards an improvement of the machining efficiency and quality of Inconel 718.

In order to improve the surface forming quality and machining efficiency of composite materials and reduce tool wear, a two-dimensional rotary ultrasonic combined electro-machining (2DRUEM) technology with low electrical conductivity and low current density was proposed in this study. Additionally, a gap detection unit of the machining system was designed with the integration of grinding force and gap current, and the average errors and maximum errors of the model were 5.61% and 12.08%, respectively, which were better than single detection. Furthermore, the machining parameters were optimally selected via NSGA-II, and the maximum machining surface roughness error was 5.9%, the maximum material removal rate error was 5.5%, and the maximum edge accuracy error was 8.9%, as established through experiments.

In conventional parameters design, the driving circuit is usually simplified as an RLC second-order circuit, and the switching characteristics are optimized by selecting parameters, but the influence of switching characteristics on the driving circuit is not considered. In this paper, the insight mechanism for the gate-source voltage changed by overshoot and ringing caused by the high switching speed of SiC MOSFET is highlighted, and we propose an optimized design method to obtain optimal parameters of the SiC MOSFET driving circuit with consideration of parasitic parameters. Based on the double-pulse circuit, we evaluated the influence of main parameters on the gate-source voltage, including driving voltage, driving resistance, gate parasitic inductance, and stray inductance of the power circuit. A SiC-based boost PFC is constructed and tested. The test results show that the switching loss can be reduced by 7.282 W by using the proposed parameter optimization method, and the over-voltage stress of SiC MOSFET is avoided.

The municipal solid waste (MSW) with high water content can be pre-treated by the mechanical dewatering technology to significantly decrease the leachate generation in sequential landfill treatment or to improve the efficiency for solid waste incineration, which has attracted great concerns recently. However, the generated mechanical dewatering wastewater (MDW) containing high organics and nitrogenous content has been one of the big challenges for the sustainable treatment of MSW. In this study, a pilot-scale integrated system composed of physiochemical pretreatment, anaerobic sequencing batch reactor (ASBR), partial nitrification SBR (PN-SBR), denitrification SBR (DN-SBR), and UV/O3 advanced oxidation process, with a capacity of 1.0 m3/d to treat MDW containing over 34000 mg-chemical oxygen demand (COD)/L organics pollutant and 850 mg/L NH4+-N, was successfully developed. By explorations on the start-up of this integrated system and the process conditions optimization, after a long-term system operation, the findings demonstrated that this integrated system could reach the removal efficiency in the COD, NH4+-N and total nitrogen (TN) in the MDW of 99.7%, 98.2% and 96.9%, respectively. Partial nitrification and denitrification were successfully obtained for the TN removal with the nitrite accumulation rate of over 80%. The treatment condition parameters were optimized to be 800 mg/L polyaluminum chloride (PAC) and 2 mg/L polyacrylamide (PAM) under a pH of 9 for pretreatment, 36 h hydraulic retention time (HRT) for ASBR, 24 h for PN-SBR, and 2 h for UV/O3 unit. The organic sources in the MDW were also found to be feasible for the DN-SBR. Consequently, the resulting final effluent was stably in compliance with the discharge standard with high stability and reliability.

Remaining useful life prediction is of huge significance in preventing equipment malfunctions and reducing maintenance costs. Currently, machine learning algorithms have become hotspots in remaining useful life prediction due to their high flexibility and convenience. However, machine learnings require large amounts of data, and their prediction performance depends heavily on the selection of hyper-parameters. To overcome these shortcomings, a novel remaining useful life prediction method for small sample cases is proposed based on multi-support vector regression fusion. In the offline training phase, the fusion model is established, consisting of multiple support vector regression sub-models To obtain the optimal sub-model parameters, the Bayesian optimization algorithm is applied and an improved optimization target is formulated with various metrics describing regression and prediction performance. In the online prediction phase, an adaptive weight updating algorithm based on dynamic time warping is developed to measure the fitness of each sub-model and determine the corresponding weight value. The C-MAPSS engine dataset is used to test the performance of the proposed method, along with some existing machine learning methods as comparison. The proposed method only requires 30% of the training data sample to achieve high accuracy, with a root mean square error of 14.98, which is superior to other state-of-the-art methods. The results demonstrate the superiority of the proposed method.

The main steam temperature of boiler outlet has been deemed as a significant parameter of the safety and economic performances in the thermal power plant operation. The complex working status of the thermal generation endures highly uncertain factors and remarkable disturbance, which call for effective controlling approaches in the corresponding temperature management. The linear active disturbance rejection controller (LADRC) is a conducive and powerful controlling method, whereas strong correlation between LADRC parameters leads to difficulties in optimally determining the controller parameters. Aiming at eliminating the negative effects on main steam temperature control caused by uncertainties factors and disturbances, a high performance LADRC based on a novel parameters optimization strategy, the simultaneous heat transfer search (SHTS) algorithm, is designed to deliver a stability, rapidity, and precision of control process. In the presented SHTS algorithm, all the three phases of heat transfer are randomly and parallel operated, providing a significant improvement towards the optimization performance. The proposed algorithm is first verified on various benchmark functions contrasted to state-of-the-art counterparts in performance validating, and then adopted in the parameter selection of LADRC in the main steam temperature control system. The excellent control performance, strong robustness and disturbance rejection ability of the designed approach are illustrated through the simulation results on main steam temperature control system.

Machined surface integrity characteristics, including surface stresses, physical-mechanical properties and metallographic structures, play important roles in the fatigue performance of machined components. This work aimed at investigating the effects of machined surface integrity on high-temperature low-cycle fatigue life. The process parameters were optimized to obtain required surface integrity and fatigue life of the turning superalloy Inconel 718. The relationships between low-cycle fatigue life and machined surface integrity characterization parameters were established based on the low-cycle fatigue tests at a high temperature (650 °C). The sensitivities of turning process parameters to high-temperature low-cycle fatigue life were analyzed, and the optimization parameters were proposed with the goal of antifatigue manufacturing. Experimental results indicated that the impact order of the characterization parameters of machined surface integrity on the high-temperature low-cycle fatigue life were the degree of work hardening RHV, the residual stress in the cutting speed direction S22, the fatigue stress concentration factor Kf, the degree of grain refinement RD and the residual stress in the feed direction S33. In the range of turning parameters of the experiments in this research, the cutting speeds could be 80~110 m/min, and the feed rate could be 0.10~0.12 mm/rev to achieve a longer high-temperature low-cycle fatigue life. The results can be used for guiding the fatigue-resistant manufacturing research of aeroengine superalloy turbine disks.