The Effect of Pushing Rate on Foam Stability in the Tessari Method.

BACKGROUND: The Tessari method is commonly used in sclerotherapy for producing foam, involving 2 syringes pushed back and forth 20 times with the use of a 3-way connector. Many factors affect the foam stability which is crucial for clinical efficacy. OBJECTIVE: This study aimed to identify the optimal pushing rate which may impact the foam stability. MATERIALS AND METHODS: Polidocanol (POL) solution (1% and 3%) was used to make sclerosant foam via the Tessari method, with a total of 20 pushes performed at different time durations: 10, 15, 20, 25, 30, 35, and 40 seconds. The foam stability was recorded using foam half-life time (FHT), and the pushing pressure to the syringe was recorded using a self-made electric device. Both FHT and the pressure among different groups were compared respectively. RESULTS: The FHT was decreased as pushing duration exceeding 20 seconds in POL 1% and 15 seconds in POL 3%. Both the highest FHT and pressure point were located in the 10-second group. CONCLUSION: It is recommended to complete 20 back-and-forth passages within 10 seconds to create stable foam.

Combined application of gluteus maximus muscle flaps and random pattern skin flaps in repairing sacrococcygeal sinus tracts: a prospective study and technical note.

BACKGROUND: Sacrococcygeal sinus ulcers are caused by pressure sores, trauma, or surgery. Several surgical techniques have been developed for its treatment; however, studies are ongoing to develop the ideal method. The present study aims to introduce an innovative application of gluteus maximus muscle flaps (GMMFs) packing combined with local random pattern skin flaps (RPSFs) in repairing sacrococcygeal sinus tracts (SSTs). METHODS: This was a prospective analysis study conducted on the patients (n = 26) with SSTs underwent an innovative repairing surgery in Qilu Hospital of Shandong University, China between December 2015 and December 2020. The repairing surgery was GMMFs combined with RPSFs. The demographic information and the clinical parameters including operative time, postoperative healing time, and recurrence rate were used to evaluate the efficacy of the operation. RESULTS: Except one case of partial necrosis at the edge of the flap due to severe fibrosis, which was cured after dressing change, all the other cases showed effective healing after the operation. CONCLUSION: The combined application of GMMFs and RPSFs for the treatment of sacrococcygeal sinus tracts gains the advantages of short operative time, quick postoperative recovery, and low postoperative recurrence rate.

Stabilizing Co2C with H2O and K promoter for CO2 hydrogenation to C2+ hydrocarbons.

The decomposition of cobalt carbide (Co2C) to metallic cobalt in CO2 hydrogenation results in a notable drop in the selectivity of valued C2+ products, and the stabilization of Co2C remains a grand challenge. Here, we report an in situ synthesized K-Co2C catalyst, and the selectivity of C2+ hydrocarbons in CO2 hydrogenation achieves 67.3% at 300°C, 3.0 MPa. Experimental and theoretical results elucidate that CoO transforms to Co2C in the reaction, while the stabilization of Co2C is dependent on the reaction atmosphere and the K promoter. During the carburization, the K promoter and H2O jointly assist in the formation of surface C* species via the carboxylate intermediate, while the adsorption of C* on CoO is enhanced by the K promoter. The lifetime of the K-Co2C is further prolonged from 35 hours to over 200 hours by co-feeding H2O. This work provides a fundamental understanding toward the role of H2O in Co2C chemistry, as well as the potential of extending its application in other reactions.

Surface change of microplastics in aquatic environment and the removal by froth flotation assisted with cationic and anionic surfactants.

Microplastics (MPs) are increasingly released into the environment due to the widespread usage and improper management of plastics. Considerable research efforts have been devoted to the remediation of MPs. Froth flotation has been demonstrated as an effective method to remove MPs in water and sediment. However, there is a lack of knowledge on the regulation of the hydrophobicity/hydrophilicity of MPs surfaces. We found that exposure to the natural environment resulted in the increased hydrophilicity of MPs. The flotation efficiencies of polyvinyl chloride (PVC), polypropylene (PP), polystyrene (PS), and polyethylene glycol terephthalate (PET) MPs decreased to zero after six months of natural incubation in rivers. According to various characterizations, the hydrophilization mechanism is mainly correlated with surface oxidation and the deposition of clay minerals. Inspired by surface wettability conversion, we applied surfactants (collectors) to enhance MPs hydrophobicity and flotation efficiency. Anionic sodium oleate (NaOL) and cationic dodecyl trimethyl ammonium chloride (DTAC) were used to regulate surface hydrophobicity. The effects of collector concentration, pH, conditioning time, and metal ions on MPs flotation were thoroughly elucidated. Characterizations and adsorption experiments were performed to describe the heterogeneous adsorption of surfactants on MPs surfaces. The interaction between surfactants and MPs was explained through density functional theory (DFT) simulations. The dispersion energy between hydrophobic hydrocarbon chains attracts collectors on the MPs surface, and the collector molecules wrap and laminate to MPs surfaces. Flotation using NaOL exhibited a higher removal efficiency, and NaOL was environmentally friendly. Subsequently, we investigated the activation of Ca2+, Fe3+, and Al3+ to further improve the collecting efficiency of NaOL. Under the optimized conditions, MPs in natural rivers could be removed by froth flotation. This study shows the great promise of froth flotation for the application of MPs removal.

Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer.

Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.

Insight into the microplastics release from disposable face mask: Simulated environment and removal strategy.

The fight against the COVID-19 epidemic significantly raises the global demand for personal protective equipment, especially disposable face masks (DFMs). The discarded DFMs may become a potential source of microplastics (MPs), which has attracted much attention. In this work, we identified the detailed source of MPs released from DFMs with laser direct infrared spectroscopy. Polypropylene (PP) and polyurethane (PU) accounted for 24.5% and 57.1% of released MPs, respectively. The melt-blown fabric was a dominant MPs source, however, previous studies underestimated the contribution of mask rope. The captured polyethylene terephthalate (PET), polyamide (PA), polyethylene (PE), and polystyrene (PS) in airborne only shared 18.4% of released MPs. To deepen the understanding of MPs release from medical mask into the aquatic environment, we investigated the effects of environmental factors on MPs release. Based on regression analysis, the effects of temperature, incubation time, and wearing time significantly affect the release of MPs. Besides, acidity, alkalinity, sodium chloride, and humic acid also contributed to the MPs release through corroding, swelling, or repulsion of fibers. Based on the exposure of medical mask to simulated environments, the number of released MPs followed the order: seawater > simulated gut-fluid > freshwater > pure water. Considering the risk of MPs released from DFMs to the environment, we innovatively established a novel flotation removal system combined with cocoamidopropyl betaine, achieving 86% removal efficiency of MPs in water. This work shed the light on the MPs release from DFMs and proposed a removal strategy for the control of MPs pollution.

Is the presence of Cu(II) and p-benzoquinone a challenge for the removal of microplastics from landfill leachate?

A large number of plastic wastes generated eventually end up in landfills. The leachate from landfills has become a potential destination for microplastics (MPs). Many researchers have turned their attention to the distribution of MPs in landfill leachate. However, rare researchers mentioned that the efficient removal of MPs in landfill leachate was hard to realized. In this work, we analyzed MPs distribution and composition in leachate from a municipal landfill. Subsequently, to understand the causes of hydrophilization of MPs in leachate, we investigated the flotation percentage of polyvinyl chloride (PVC), polyethylene terephthalate (PET), and polystyrene (PS) MPs when exposure to p-benzoquinone and Cu2+. We conducted experiments on factors including the concentration of pollutants, pH, and interaction time. Meanwhile, the adsorption kinetics, adsorption isotherms, and synergistic effects of p-benzoquinone and Cu2+ were further investigated. The order of the strength of the hydrophilic effect of contaminants on MPs in leachate was p-benzoquinone + Cu2+ > p-benzoquinone > Cu2+. The physisorption and chemisorption of p-benzoquinone and Cu2+ on the MPs surface, respectively, resulted in the hydrophilization of the MPs surface. The order of hydrophilization and the adsorption capacity for pollutants of the three MPs were consistent: PVC > > PET ≈ PS. We proposed a feasible scheme with the oleic acid to restore the hydrophobicity of MPs, which could increase the removal rate of MPs by 87.37 %. This work revealed the hydrophilization effects of pollutants on MPs and proposed a novel insight into the MPs removal from landfill leachate.

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR.

Coal miners' occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners' working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.

Stepwise flotation separation of WEEE plastics by polymeric aluminum chloride towards source control of microplastics.

The mismanagement of waste electrical and electronic equipment (WEEE) resulted in numerous discarded plastics in the natural environment, and these waste plastics might experience aging, breaking, and migration, which becomes a crucial microplastic source. Sustainable management of WEEE plastics presents a considerable opportunity for resource recovery and microplastic pollution prevention. Flotation separation is a significant process of mechanical recycling, while most flotation methods can only deal with binary plastic mixtures. In this work, an advanced, stepwise, and sustainable flotation method was advocated to separate multi-plastics by polymeric aluminum chloride (PAC) modification. The abundant hydrophilic groups and environmental friendliness of PAC prompted us to further investigate the wetting effect. PAC had varied hydrophilization effects on acrylonitrile butadiene styrene (ABS) and polystyrene (PS) surfaces, but polyethylene terephthalate (PET) retained hydrophobicity. Treatment conditions, including PAC dosage, temperature, time, and pH were optimized. 100% of PET could be purified after primary separation, and the purities of ABS and PS could reach 100% and 97.4% after secondary separation, respectively. The strength of the interaction was determined by the different surface potentials and functional groups. In PAC solution, long-chain molecules or ions might interact with plastic surfaces electrostatically, and Al3+ could bridge long-chain molecules and plastic surfaces, thereby strengthening the polymer hydrophilicity. We further improved the PAC treatment process, and the reuse of PAC reduced modifier usage to 84.4 g/ton waste plastics, which was cost-effective in industrial applications. A preliminary evaluation of the energy consumption and environmental impact indicated that PAC treatment was superior to other modification methods. This work is an initial attempt at the stepwise separation of waste plastic and shows promising prospects for recycling plastic waste.

Flotation separation of hazardous polyvinyl chloride towards source control of microplastics based on selective hydrophilization of plasticizer-doping surfaces.

As the single largest chlorine source of plastics, hazardous polyvinyl chloride (PVC) has become an increasing environmental concern with the rapid microplastics accumulation. An advanced separation method is advocated to purify waste PVC plastics, optimize physical recycling, and protect aquatic and terrestrial environment safety. In this study, we proposed a novel scheme for the flotation separation of PVC plastics with diverse plasticizer contents (PVCs) via regulating hydrophilicity based on a selective ferric deposition. Rigid PVCs were prone to loading ferric ions and generating hydrophilic shells than flexible PVCs. Plasticizers can diffuse freely through the interior and surface of PVC plastics. Abundant plasticizers thereby overlaid the surface of flexible PVC and shielded PVC matrix from ferric ions. By regulating the ferric concentration, the wettability of PVCs was adjusted to separate rigid and flexible PVCs by froth flotation. Waste PVCs could also be separated from each other through the compound process of ferric deposition and flotation, further confirming its feasibility and stability. Thus far, this study supplies distinctive insights into the wettability regulation of plasticizer-doping PVC surfaces, contributes a pioneering hydrophilization method to PVCs separation and recycling, and mitigates hazardous PVC microplastics by source control.

Is froth flotation a potential scheme for microplastics removal? Analysis on flotation kinetics and surface characteristics.

Increasing microplastics (MPs) cause significant threats to the ecosystem and society. The tremendous advances concerning the sources, occurrence, chemical behavior, toxicology, and ecological effects contribute to the emerging MPs removal. Based on the intrinsic hydrophobicity of MPs, froth flotation can remove MPs from water environments via bubble attachment on hydrophobic surfaces. This study comprehensively investigated plastic, aqueous, and operating variables in the flotation removal of polyethylene terephthalate (PET) and polystyrene (PS) MPs, assisted by numerous bench-scale experiments and a first-order model with rectangular distribution of floatability. Froth flotation performed better to remove MPs with higher density, larger size, and lower concentration. K+ (0-50 mM), Na+ (0-150 mM), and Ca2+ (0-10 mM) did not affect the flotation recovery of MPs. MPs particles could be thoroughly removed by froth flotation when humic acid (HA) and Al3+ concentrations were less than 30 mg/L and 0.05 mM, respectively. 100% of MPs could be removed at a rapid flotation rate under aeration volume of 5.4 mL/min and frother dosage of 28 mg/L. Non-covalent interactions and near-surface water film might favor the adhesion of hydrophilic species and obstruct the flotation removal of MPs. The froth flotation-based MPs removal had potential application in multiple flow systems due to its simplicity and continuity.

Perspectives on long pentraxin 3 and rheumatoid arthritis: several potential breakthrough points relying on study foundation of the past.

Rheumatoid arthritis (RA) is a systemic chronic autoimmune inflammatory disease which is mainly characterized by synovitis and results in a severe burden for both the individual and society. To date, the underlying mechanisms of RA are still poorly understood. Pentraxin 3 (PTX3) is a typical long pentraxin protein which has been highly conserved during evolution. Meanwhile, functions as well as properties of PTX3 have been extensively studied. Several studies identified that PTX3 plays a predominate role in infection, inflammation, immunity and tumor. Interestingly, PTX3 has also been verified to be closely associated with development of RA. We therefore accomplished an elaboration of the relationships between PTX3 and RA. Herein, we mainly focus on the associated cell types and cognate cytokines involved in RA, in combination with PTX3. This review infers the insight into the interaction of PTX3 in RA and aims to provide novel clues for potential therapeutic target of RA in clinic.

Particle trajectories and its charging behaviors: effect of gas type and electric field.

Conductive particles in the gas insulated transmission lines (GIL) induce the breakdown between electrodes or the flashover along insulators. To solve the problem of particle moving and realize the particle-moving regulation, particle trajectories should firstly be determined in air and SF6. This paper presents the results of particle trajectories and its charging behaviors at vacuum and SF6, respectively. Metal particles with different materials and sizes were introduced and the charge quantity was calculated. The results showed that the particle lift-off electric field in SF6 was higher than that in air under the same gas pressure, that is, the charge on particle in SF6 was about 0.789 times lower than that in air under the same condition. Besides, the lift-off electric field of particle increased with the pressure increase of SF6. The charge on particle was affected by the concentration of electric field near particle and the electrical negative features of SF6. The work provided the data support for development of DC GIL in particle defect suppression.

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction.

Breast cancer is one of the most common cancer diseases in women. The rapid and accurate diagnosis of breast cancer is of great significance for the treatment of cancer. Artificial intelligence and machine learning algorithms are used to identify breast malignant tumors, which can effectively solve the problems of insufficient recognition accuracy and long time-consuming in traditional breast cancer diagnosis methods. To solve these problems, we proposed a method of attribute selection and feature extraction based on random forest (RF) combined with principal component analysis (PCA) for rapid and accurate diagnosis of breast cancer. Firstly, RF was used to reduce 30 attributes of breast cancer categorical data. According to the average importance of attributes and out of bag error, 21 relatively important attribute data were selected for feature extraction based on PCA. The seven features extracted from PCA were used to establish an extreme learning machine (ELM) classification model with different activation functions. By comparing the classification accuracy and training time of these different models, the activation function of the hidden layer was determined as the sigmoid function. When the number of neurons in the hidden layer was 27, the accuracy of the test set was 98.75%, the accuracy of the training set was 99.06%, and the training time was only 0.0022 s. Finally, in order to verify the superiority of this method in breast cancer diagnosis, we compared with the ELM model based on the original breast cancer data and other intelligent classification algorithm models. The algorithm used in this article has a faster recognition time and a higher recognition accuracy than other algorithms. We also used the breast cancer data of breast tissue reactance features to verify the reliability of this method, and ideal results were obtained. The experimental results show that RF-PCA combined with ELM can significantly reduce the time required for the diagnosis of breast cancer, which has the ability of rapid and accurate identification of breast cancer and provides a theoretical basis for the intelligent diagnosis of breast cancer.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG.

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.

Dual-functional dielectric metasurface doublets.

Multi-functional metasurface devices are of great importance for miniaturization and integration. Here we propose a novel approach to design dual-functional dielectric metasurface doublets based on the principle of Pancharatnam-Berry phase. The functionalities for the two operating wavelengths can be independently designed by adjusting the orientation angle of the nanofins in the upper layer and that in the lower layer, respectively. The functionality can be switched by simply changing the incident wavelength. The simulated results of the examples demonstrate that the designed dual-functional doublet works well as expected.

Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network.

The application of laser-induced fluorescence (LIF) combined with machine learning methods can make up for the shortcomings of traditional hydrochemical methods in the accurate and rapid identification of mine water inrush in coal mines. However, almost all of these methods require preprocessing such as principal component analysis (PCA) or drawing the spectral map as an essential step. Here, we provide our solution for the classification of mine water inrush, in which a one-dimensional convolutional neural network (1D CNN) is trained to automatically identify mine water inrush according to the LIF spectroscopy without the need for preprocessing. First, the architecture and parameters of the model were optimized and the 1D CNN model containing two convolutional blocks was determined to be the best model for the identification of mine water inrush. Then, we evaluated the performance of the 1D CNN model using the LIF spectral dataset of mine water inrush containing 540 training samples and 135 test samples, and we found that all 675 samples could be accurately identified. Finally, superior classification performance was demonstrated by comparing with a traditional machine learning algorithm (genetic algorithm-support vector machine) and a deep learning algorithm (two-dimensional convolutional neural network). The results show that LIF spectroscopy combined with 1D CNN can be used for the fast and accurate identification of mine water inrush without the need for complex pretreatments.