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The quality of traditional sMDT(single-centered multi-disciplinary treatment team) teaching model will be affected by the centerlocation, leve of heathcare service, performance habits, dominant disciplines, and superior technologies, which will directly affect the depth and breadth of students' knowledge learning. Based on the traditional sMDT teaching, the new multi-centered MDT (mMDT) teaching model establishes cooperative teaching relationships with multiple hospitals and disciplines, cocultivates undergraduate and graduate students, and regularly conducts mMDT teaching, which effectively avoids the defects of the traditional sMDT teaching model and meets the needs of students at different levels. Through the mMDT teaching mode, students can learn the advanced clinical reasoning and techniques in different disciplines or hospitals, have a deeper understanding of the concept of comprehensive diagnosis and treatment of tumors, and improve the comprehensive quality of clinical research.

Slow freezing is the most commonly used technique for the cryopreservation of spermatozoa in clinical practice. However, it has been shown to have a negative impact on sperm function and structure. Vitrification as a successful alternative method has been proved to have better protective effects on human embryos, but vitrification of spermatozoa is still subject to low recovery rates. In this study, a modified vitrification method for native spermatozoa was developed. A total of 28 semen samples were included; each sample was divided into three equal parts and assigned to fresh, slow freezing, and vitrification groups. Sperm vitality, motility, morphology, DNA integrity, and acrosome reaction were assessed for each of the groups. The results showed that vitrification achieves better results for several sperm protection parameters than slow freezing; vitrification achieves a higher recovery rate (P < 0.05), motility (P

In this work, we developed artificial intelligence-based models for prediction and correlation of CO2 solubility in amino acid solutions for the purpose of CO2 capture. The models were used to correlate the process parameters to the CO2 loading in the solvent. Indeed, CO2 loading/solubility in the solvent was considered as the sole model’s output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoost-DT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R2 scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R2 Score of 0.998. With an R2 of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R2 of 0.9.

In this work, we implemented different models for predicting adsorption separation of a dye from aqueous solution using porous materials. The equilibrium data of solute concentrations were collected from resources and used in the models for training and verification purposes to develop the models. For prediction of the equilibrium solute concentrations (Ce), we used tree models: Multi-layer Perceptron (MLP), Passive aggressive regression, and Decision Tree (DT) Regressor. In the modeling, we considered the adsorbent dosage as well as solution pH as the input parameters to the model, and the model was able to generate the output values, i.e., equilibrium concentrations based on the input variables. The evaluation of the models’ performances revelated that the final R2 scores are 0.99, 0.98, 0.93 for DT, MLP and Passive-Aggressive, respectively and a very low RMSE of 0.055 for decision tree that shows this model is the best among models used in this study. Indeed, decision tree model is recommended among the other three models to be employed for correlation of adsorption equilibrium data.

In this study, the problem of grouping motion for a second‐order multi‐agent network with both matched disturbances and mismatched disturbances is investigated, and finite‐time convergent algorithm of the dynamic system is proposed. By designing the disturbance observers to eliminate disturbances in the multi‐agent systems, a compound communication protocol with active disturbance compensation is presented by means of the sliding‐mode control. By applying finite‐time stable theory and input‐to‐state stable theory, the finite‐time group convergence of multi‐agent systems with multiple disturbances is analysed, and group flocking can be achieved asymptotically with the cooperative controller. The numerical simulations demonstrate the validity of the results.

Monitoring pest populations in paddy fields is important to effectively implement integrated pest management. Light traps are widely used to monitor field pests all over the world. Most conventional light traps still involve manual identification of target pests from lots of trapped insects, which is time-consuming, labor-intensive and error-prone, especially in pest peak periods. In this paper, we developed an automatic monitoring system for rice light-trap pests based on machine vision. This system is composed of an intelligent light trap, a computer or mobile phone client platform and a cloud server. The light trap firstly traps, kills and disperses insects, then collects images of trapped insects and sends each image to the cloud server. Five target pests in images are automatically identified and counted by pest identification models loaded in the server. To avoid light-trap insects piling up, a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects. There was a close correlation (r=0.92) between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap. Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.

Abstract The topology concept in the condensed physics and acoustics is introduced into the elastic wave metamaterial plate, which can show the topological property of the flexural wave. The elastic wave metamaterial plate consists of the hexagonal array which is connected by the piezoelectric shunting circuits. The Dirac point is found by adjusting the size of the unit cell and numerical simulations are illustrated to show the topological immunity. Then the closing and breaking of the Dirac point can be generated by the negative capacitance circuits. These investigations denote that the topological immunity can be achieved for flexural wave in mechanical metamaterial plate. The experiments with the active control action are finally carried out to support the numerical design.

This article presents, for the first time, the efficacy and curing depth analysis of photo-thermal dual polymerization in metal (Fe) polymer composites for 3D printing of a three-component (A/B/M) system based on the proposed mechanism of our group, in which the co initiators A and B are Irgacure-369 and charge–transfer complexes (CTC), respectively, and the monomer M is filled by Fe. Our formulas show the depth of curing (Zc) is an increasing function of the light intensity, but a decreasing function of the Fe and photoinitiator concentrations. Zc is enhanced by the additive [B], which produces extra thermal radical for polymerization under high temperature. The heat (or temperature) increase in the system has two components: (i) due to the light absorption of Fe filler and (ii) heat released from the exothermic photopolymerization of the monomer. The heat is transported to the additive (or co-initiator) [B] to produce extra radicals and enhance the monomer conversion function (CF). The Fe filler leads to a temperature increase but also limits the light penetration, leading to lower CF and Zc, which could be overcome by the additive initiator [B] in thick polymers. Optimal Fe for maximal CF and Zc are explored theoretically. Measured data are analyzed based on our derived formulas.

Microplastics are emerging contaminants and their presence in water and soil ecosystems has recently drawn considerable attention because they pose a great threat to entire ecosystems. Recent researches have focused on the detection, occurrence, characterization, and toxicology of microplastics in marine and freshwater ecosystems; however, our understanding of the ecological effects of microplastics in soil ecosystems is still limited compared with that in aquatic ecosystems. Here, we have compiled literature, studying the sources, migration of microplastics in soil, negative impacts on soil health and function, trophic transfer in food chains, and the corresponding adverse effects on soil organisms in order to address the potential ecological and human health risks caused by microplastics in soil. This review aims to address gaps in knowledge, shed light on the ecological effects of microplastics in soil, and propose future studies on microplastic pollution and the resultant soil ecotoxicity. Furthermore, this review is focused on limiting microplastics in soil and establishing management and remediation measures to mitigate the risks posed by microplastic pollution. Keywords: Microplastics, Soil, Migration, Toxicology, Health risks