SWOT Analysis on the Belt and Road Innovation Cooperation between Guangdong and Brazil

As China-US relations and COVID-19 situation are getting more and more serious,the cooperation between the Guangdong and Portuguesespeaking countries may also be affected.This article starts with the research on the cooperation mechanism and situation between China and Brazil under the Belt and Road Initiative.It uses SWOT to analyze Brazil's innovation advantages and shortcomings,the opportunities brought by scientific and technological cooperation and how to handle to the corresponding threats.This article attempts to propose areas and suggestions for the development of scientific and technological cooperation between the two sides.

Prospects for Science and Technology Cooperation belween the Guangdong-Hong Kong-Macao Greater Bay Area and Portugal under the Belt and Road Initiative

President Xi,focusing on building a new pattern of all-round opening up to the outside world and promoting the common prosperity and progress of all countries,put forward a major proposal for the construction of the Silk Road Economic Belt and the maritime Silk Road in twenty-first Century.The bay area economy as an important coastal economic form,is the highlight of the current international economic map,and is a significant symbol of the world's first-class coastal city.The international first-class bay areas,such as New York Bay area,San Francisco Bay area and Tokyo Bay area,are characterized by openness,innovation,livability and internationalization.The Guangdong-Hong Kong-Macao Greater Bay Area has four worldclass cities,name Guangzhou,Shenzhen,Hong Kong and Macao,and the Pearl River Delta city cluster.It has an open economic structure,efficient resource allocation ability,strong agglomeration and spillover function and development international communication network plays a core function of leading innovation and gathering radiation.It is an important growth pole for promoting the economic development of the maritime Silk Road and a leader in technological change.With the help of Portugal's geographical position on the maritime Silk Road and market access advantages within the EU system,the Greater Bay Area should further deepen scientific and technological cooperation,improve the ability of scientific and technological innovation of both sides,let the scientific research subjects of the bay area help Portugal's scientific and technological development,and enhance PortugaFs scientific and technological position in the EU.From the perspective of the Belt and Road Initiative,combined with the basic situation and cooperation of China,especially Guangdong,Hong Kong and Macao,and the development of science and technology in Portugal,this article explores the technological fields,policy measures that are suitable for scientific and technological cooperation between the two sides,and puts forward corresponding suggestions to contribute to the technological development of China,Portugal and the global economic and social sustainable development.

Multifunctional polyeugenol-based nanoparticles with antioxidant and antibacterial properties

Over the past few decades,extensive scientific has been dedicated to polymer synthesis employing renewable resources.In this study,we devised and synthesized multifunctional polyeugenol-based nanoparticles,exhibiting remarkable antioxidant and antibacterial properties.The grafting of eugenol onto the nanoparticle surface was achieved via a thiol-ene chemical reaction with a grafting rate of 3.5%.To comprehend the properties of the synthesized nanoparticles,we employed Fourier transform infrared spectroscopy,X-ray photoelectron spectroscopy and pyrolysis-gas chromatography mass spectrometry.Subsequent scanning electron microscopic and transmission electron microscopic analyses revealed the presence of a crosslinked structure within the polyeugenol-based nanoparticles,as well as a heteroge-neous microsphere structure on the surface.Due to the inherent crosslinking structure,the polyeugenol-based nanoparticles demonstrated robust the resistance to solvents,as ascertained through thermog-ravimetric analysis and solvent resistance tests.Notably,nitrogen adsorption/desorption studies confirmed the adsorption capacity of the polyeugenol-based nanoparticles,rendering them potentially suitable for drug transport applications.Moreover,the assays for assessing cytocompatibility and reactive oxygen species scavenging activities demonstrated better performance of the eugenol-based nano-particles compared to eugenol.Furthermore,the polyeugenol-based nanoparticles exhibited certain bactericidal activities against Escherichia coli,Staphylococcus aureus,and Fusobacterium nucleatum.Consequently,these observations indicated the nontoxic nature and expansive application prospects of the polyeugenol-based nanoparticles in the domains of medicine and food preservation.This work presented a pioneering concept for the development of antioxidant and antibacterial multifunctional polymer materials derived from eugenol.

Filtering enhanced tomographic PIV reconstruction based on deep neural networks

Tomographic particle image velocimetry(Tomo-PIV)has been successfully applied in measuring three-dimensional(3D)flow field in recent years.Such technology highly relies on the reconstruction technique which provides the spatial particle distribution by using images from multiple cameras at different viewing angles.As the most popular reconstruction method,the multiplicative algebraic reconstruction technique(MART)has advantages in high computational speed and high accuracy for low particle seeding reconstruction.However,the accuracy is not satisfactory in the case of dense particle distributions to be reconstructed.To overcome this problem,a symmetric encode-decoder fully convolutional network is proposed in this paper to improve the reconstruction quality of MART.The input of the neural network is the particle field reconstructed by the MART approach,while the output is the regenerated image with the same resolution.Numerical evaluations indicate that those blurred or irregular particles can be significantly refined by the trained neural network.Most of the ghost particles can also be removed by this filtering method.The reconstruction accuracy can be improved by more than 10%without increasing the computational cost.Experimental evaluations indicate that the trained neural network can also provide similar satisfactory reconstruction and improved velocity fields.

Modulating the tumor microenvironment via oncolytic virus and PI3K inhibition synergistically restores immune checkpoint therapy response in PTEN-deficient glioblastoma

Dear Editor,Glioblastoma(GBM)is a lethal primary brain cancer,with a median survival of less than 2 years.1 Immune checkpoint blockades(ICBs)have revolutionized cancer therapy in the last decade,but they have little clinical benefit in GBM.1 Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations in GBM patients resistant to ICBs.2 PTEN deficiency activates the phosphatidylinositol 3-kinase(PI3K)-AKT pathway to shape an immunosuppressive microenvironment.

Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

Particle image velocimetry(PIV)is an essential method in experimental fluid dynamics.In recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of PIV.However,the supervised learning of PIV is driven by large volumes of data with ground truth information.Therefore,the authors consider unsupervised PIV methods.There has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning PIV.The authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical constraints.In this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV methods.The authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic dataset.Besides,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle images.As a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.