Optimization of a continuous flow electrocoagulation as pretreatment for membrane distillation of the waste stream in vinyl ester resin production

Vinyl ester resin production wastewater (VERW) contains high concentrations of organics particularly, methacrylic acid and bisphenol A, which are hazardous chemicals and harmful to the aquatic environment. Therefore, there is an urgent need to properly treat the effluent before discharge into the aquatic system. In this work, direct contact membrane distillation (DCMD) was explored as an advanced treatment of the VERW pre-treated by a continuous flow electrocoagulation (EC) and peroxi-electrocoagulation (PEC) processes. Optimization of EC and PEC processes were investigated and the DCMD performance was evaluated. Results showed that the optimal value of current density and polyacrylamide (PAM) dosage was 15 mA/cm2 and 1 mg/L, respectively in the EC process. For the PEC process, the optimal addition of hydrogen peroxide (H2O2) dosage was four times of the chemical oxygen demand (COD) concentration of EC effluent. The COD of VERW was effectively removed via EC followed by PEC (EC-PEC), resulting in the significant alleviation of membrane fouling during DCMD filtration of VERW. The initial flux of DCMD filtration of VERW pre-treated via EC-PEC improved by 35%, compared that only pre-treated by EC. Moreover, the concentration factor (CF) of the DCMD system reached up to 8.1 and the conductivity of distillate was less than 33.2 μS/cm. Hence, the EC and membrane distillation hybrid process paves a new way for the effective treatment of waste steam from resin production.</p

Optimization of a continuous flow electrocoagulation as pretreatment for membrane distillation of the waste stream in vinyl ester resin production

Vinyl ester resin production wastewater (VERW) contains high concentrations of organics particularly, methacrylic acid and bisphenol A, which are hazardous chemicals and harmful to the aquatic environment. Therefore, there is an urgent need to properly treat the effluent before discharge into the aquatic system. In this work, direct contact membrane distillation (DCMD) was explored as an advanced treatment of the VERW pre-treated by a continuous flow electrocoagulation (EC) and peroxi-electrocoagulation (PEC) processes. Optimization of EC and PEC processes were investigated and the DCMD performance was evaluated. Results showed that the optimal value of current density and polyacrylamide (PAM) dosage was 15 mA/cm2 and 1 mg/L, respectively in the EC process. For the PEC process, the optimal addition of hydrogen peroxide (H2O2) dosage was four times of the chemical oxygen demand (COD) concentration of EC effluent. The COD of VERW was effectively removed via EC followed by PEC (EC-PEC), resulting in the significant alleviation of membrane fouling during DCMD filtration of VERW. The initial flux of DCMD filtration of VERW pre-treated via EC-PEC improved by 35%, compared that only pre-treated by EC. Moreover, the concentration factor (CF) of the DCMD system reached up to 8.1 and the conductivity of distillate was less than 33.2 μS/cm. Hence, the EC and membrane distillation hybrid process paves a new way for the effective treatment of waste steam from resin production.</p

A novel clinical−radiomic nomogram for the crescent status in IgA nephropathy

ObjectiveWe used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy.MethodsPatients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical−radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.ResultsThe average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram.ConclusionML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies

Medical insurance payment schemes and patient medical expenses: a cross-sectional study of lung cancer patients in urban China

BackgroundAs the main cause of cancer death, lung cancer imposes seriously health and economic burdens on individuals, families, and the health system. In China, there is no national study analyzing the hospitalization expenditures of different payment methods by lung cancer inpatients. Based on the 2010-2016 database of insured urban resident lung cancer inpatients from the China Medical Insurance Research Association (CHIRA), this paper aims to investigate the characteristics and cost of hospitalized lung cancer patient, to examine the differences in hospital expenses and patient out-of-pocket (OOP) expenses under four medical insurance payment methods: fee-for-service (FFS), per-diem payments, capitation payments (CAP) and case-based payments, and to explore the medical insurance payment method that can be conducive to controlling the cost of lung cancer.MethodThis is a 2010-2016, 7-year cross-sectional study. CHIRA data are not available to researchers after 2016. The Medical Insurance Database of CHIRA was screened using the international disease classification system to yield 28,200 inpatients diagnosed with lung cancer (ICD-10: C34, C34.0, C34.1, C34.2, C34.3, C34.8, C34.9). The study includes descriptive analysis and regression analysis based on generalized linear models (GLM).ResultsThe average patient age was 63.4 years and the average length of hospital stay (ALOS) was 14.2 day; 60.7% of patients were from tertiary hospitals; and 45% were insured by FFS. The per-diem payment had the lowest hospital expenses (RMB7496.00/US$1176.87), while CAP had the lowest OOP expenses (RMB1328.18/US$208.52). Compared with FFS hospital expenses, per-diem was 21.3% lower (95% CI = -0.265, -0.215) and case-based payment was 8.4% lower (95% CI = -0.151, -0.024). Compared with the FFS, OOP expenses, per-diem payments were 9.2% lower (95% CI = -0.130, -0.063) and CAP was 15.1% lower (95% CI = -0.151, -0.024).ConclusionFor lung cancer patients, per-diem payment generated the lowest hospital expenses, while CAP meant patients bore the lowest OOP costs. Policy makers are suggested to give priority to case-based payments to achieve a tripartite balance among medical insurers, hospitals, and insured members. We also recommend future studies comparing the disparities of various diseases for the cause of different medical insurance schemes

Lightweight Neural Network Optimization for Rubber Ring Defect Detection

Surface defect detection based on machine vision and convolutional neural networks (CNNs) is an important and necessary process that enables rubber ring manufacturers to improve production quality and efficiency. However, such automatic detection always consumes substantial computer resources to guarantee detection accuracy. To solve this problem, in this paper, a CNN optimization algorithm based on the Ghost module is presented. First, the convolutional layer is replaced with the Ghost module in CNNs so that feature maps can be generated using cheaper linear operations. Second, an optimization method is used to obtain the best replacement of the Ghost module to balance computer resource consumption and detection accuracy. Finally, an image preprocessing method that includes inverting colors is applied. This algorithm is integrated into YOLOv5, trained on a dataset of rubber ring surface defects. Compared to the original network, the network size decreases by 30.5% and the computational cost decreases by 23.1%, whereas the average precision only decreases by 1.8%. Additionally, the network&rsquo;s training time decreases by 16.1% as a result of preprocessing. These results show that the proposed approach greatly helps practical rubber ring surface defect detection

Application and optimization of residual connection neural network in spacecraft thermal design

In thermal analysis modeling, the finite element method (FEM) is commonly used; however, it incurs high computational costs and complicates the global optimization of thermal parameters. To address these challenges, developing simplified surrogate models is crucial for enhancing analysis efficiency. Yet, constructing such models demands exceptional predictive accuracy, making conventional parameter adjustment methods inadequate for design needs. This paper introduces a novel Residual connection Neural Network model, called Res-NN, designed to approximate the CMOS finite element model. By employing residual connections, the Res-NN model significantly reduces fitting errors between networks, achieving a predictive accuracy of 94.6 %, which is 6.6 % higher than that of comparable Multi-Layer Perceptron (MLP) models. Moreover, Res-NN is over 100 times faster than traditional FEM in prediction speed, effectively circumventing the difficulties associated with parameter adjustments. To optimize the temperature fluctuations of the CMOS model, we utilized the Res-NN model as an iterative object within an optimization algorithm. Through experimental comparisons, we identified the PSO algorithm as the most effective option. The PSO optimization results demonstrated a chip temperature difference of 0.045 °C, with a simulation error of only 0.0649 °C, meeting design specifications and achieving a 61.7 % reduction in temperature variance compared to traditional thermal design method. This validates the superiority of the entire optimization process

Refined response axis decoupling axiom for a coupled vibrating system with spectrally-varying mount properties

Real-life engine mounts inherently exhibit considerable frequency- and amplitude-dependent characteristics, and the base flexibility has a significant effect on engine vibration and forces transmitted to the vehicle body. A new analytical formulation is proposed that incorporates spectrally-varying stiffness and damping properties of multi-dimensional mounts in the presence of a compliant base (with many vibration modes over the applicable lower frequency regime). A refined analytical axiom for the response axis decoupling of coupled system is also mathematically formulated using spectral response axis decoupling indices. Two examples are chosen to prove the refined axiom. Firstly, a powertrain mounting system with two hydraulic mounts is redesigned in terms of their stiffness and damping properties, and mount locations for both powertrain and sub-frame systems used the refined axiom in torque roll axis (TRA) direction. Frequency and time domain results demonstrate that the TRA of the redesigned powertrain mounting system is indeed decoupled from other powertrain motions. The effects of parameter uncertainties on the response axis decoupling indices are also examined. Then, a laboratory experiment consisting of a powertrain, three powertrain mounts including two hydraulic mounts, a sub-frame, and four bushings is then used to mathematically validate the refined axiom in vertical axis direction. The quasi-linear system formulation of the coupled system is also verified by comparing the frequency responses with the results obtained by the direct (matrix) inversion method and measurements. </jats:p

Analysis of Battery Reduction for an Improved Opportunistic Wireless-Charged Electric Bus

As an attractive alternative to the traditional plug-in charged electric vehicles (EVs), wireless-charged EVs have recently been in the spotlight. Opportunistically charged utilizing the wireless-charging infrastructure installed under the road at bus stops, an electric bus can have a smaller and lighter battery pack. In this paper, an improved opportunistic wireless-charging system (OWCS) for electric bus is introduced, which includes the opportunistic stationary wireless-charging system (OSWCS) and opportunistic hybrid wireless-charging system (OHWCS) consisting of stationary wireless-charging and dynamic wireless-charging. A general battery reduction model is established for the opportunistic wireless-charged electric bus (OWCEB). Two different battery-reduction models are built separately for OWCEB on account of the characteristics of OSWCS and OHWCS. Additionally, the cost saving models including the production cost saving, the operation cost saving and total cost saving are established. Then, the mathematical models are demonstrated with a numerical example intuitively. Furthermore, we analyze several parameters that influence the effectiveness of battery reduction due to the application of an opportunistic wireless-charging system on an electric bus. Finally, some points worth discussing in this work are performed.</jats:p