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| 1 | Hybrid superhydrophilic-superhydrophobic micro/nanostructures fabricated by femtosecond laserinduced forward transfer for sub-femtomolar Raman detection显示文摘Raman spectroscopy plays a crucial role in biochemical analysis.Recently,superhydrophobic surface-enhanced Raman scattering(SERS)substrates have enhanced detection limits by concentrating target molecules into small areas.However,due to the wet transition phenomenon,further reduction of the droplet contact area is prevented,and the detection limit is restricted.This paper proposes a simple method involving femtosecond laser-induced forward transfer for preparing a hybrid superhydrophilic–superhydrophobic SERS(HS-SERS)substrate by introducing a superhydrophilic pattern to promote the target molecules to concentrate on it for ultratrace detection.Furthermore,the HS-SERS substrate is heated to promote a smaller concentrated area.The water vapor film formed by the contact of the solution with the substrate overcomes droplet collapse,and the target molecules are completely concentrated into the superhydrophilic region without loss during evaporation.Finally,the concentrated region is successfully reduced,and the detection limit is enhanced.The HS-SERS substrate achieved a final contact area of 0.013mm2,a 12.1-fold decrease from the unheated case.The reduction of the contact area led to a detection limit concentration as low as 10−16 M for a Rhodamine 6G solution.In addition,the HS-SERS substrate accurately controlled the size of the concentrated areas through the superhydrophilic pattern,which can be attributed to the favorable repeatability of the droplet concentration results.In addition,the preparation method is flexible and has the potential for fluid mixing,fluid transport,and biochemical sensors,etc. | Xiaodan Ma Lan Jiang Xiaowei Li Bohong Li Ji Huang Jiaxing Sun Zhi Wang Zhijie Xu Liangti Qu Yongfeng Lu Tianhong Cui | 2019 | Microsystems & Nanoengineering2019，5，1： | 3 |
| 2 | Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services显示文摘The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved. | Chao Ma YinggangSun Zhenguo Yang Hai Huang Dongyang Zhan Jiaxing Qu | 2022 | Computers, Materials & Continua2022，，6： | 1 |
| 3 | Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases显示文摘Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms. | Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie | 2024 | Computer Modeling in Engineering & Sciences2024，139，6： | 0 |
| 4 | Federation Boosting Tree for Originator Rights Protection显示文摘The problem of data island hinders the application of big data in artificial intelligence model training,so researchers propose a federated learning framework.It enables model training without having to centralize all data in a central storage point.In the current horizontal federated learning scheme,each participant gets the final jointly trained model.No solution is proposed for scenarios where participants only provide training data in exchange for benefits,but do not care about the final jointly trained model.Therefore,this paper proposes a newboosted tree algorithm,calledRPBT(the originator Rights Protected federated Boosted Tree algorithm).Compared with the current horizontal federal learning algorithm,each participant will obtain the final jointly trained model.RPBT can guarantee that the local data of the participants will not be leaked,while the final jointly trained model cannot be obtained.It is worth mentioning that,from the perspective of the participants,the scheme uses the batch idea to make the participants participate in the training in random batches.Therefore,this scheme is more suitable for scenarios where a large number of participants are jointly modeling.Furthermore,a small number of participants will not actually participate in the joint training process.Therefore,the proposed scheme is more secure.Theoretical analysis and experimental evaluations show that RPBT is secure,accurate and efficient. | Yinggang Sun Hongguo Zhang Chao Ma Hai Huang Dongyang Zhan Jiaxing Qu | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 5 | Engineering surface oxygen vacancy of mesoporous CeO_(2) nanosheets assembled microspheres for boosting solar-driven photocatalytic performance显示文摘Surface oxygen vacancy defects of mesoporous CeO_(2)nanosheets assembled microspheres(D-CeO_(2))are engineered by polymer precipitation,hydrothermal and surface hydrogenation strategies.The resultant D-CeO_(2)with a main pore diameter of 9.3 nm has a large specific surface area(~102.3 m^(2)/g)and high thermal stability.The mesoporous nanosheets assembled microsphere structure prevents the nanosheets from aggregation,which is beneficial to effective mass transfer and shortens the migration distance of charge carriers.After surface hydrogenation,the photoresponse extends to long wavelength region,combing with the band gap from 2.63 eV reduced to 2.39 eV.Under AM 1.5 G radiation,the photocatalytic degradation rate of tetracycline(TC)can be up to 99.99%,which is three times as high as that of pristine CeO_(2)microspheres.The excellent solar-driven photocatalytic performance can be attributed to the efficient surface oxygen vacancy engineering and the mesoporous nanosheets assembled microsphere structure,which narrows the band gap,shortens the migration distance of carriers,promotes the spatial separation of photogenerated electron-hole pairs and favors mass transfer.The strategy provides new insights for fabricating other high-efficient oxide photocatalysts. | Decai Yang Yachao Xu Kai Pan Chuanxin Yu Jiaxing Wu Mingxia Li Fan Yang Yang Qu Wei Zhou | 2022 | Chinese Chemical Letters2022，33，1： | 0 |
| 6 | Road Distance Computation Using Homomorphic Encryption in Road Networks显示文摘Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services.The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation.To alleviate privacy concerns about location privacy leaks during road distance computation,it is desirable to have a secure and efficient road distance computation approach.In this paper,we propose two secure road distance computation approaches,which can compute road distance over encrypted data efficiently.An approximate road distance computation approach is designed by using Partially Homomorphic Encryption and road network set embedding.An exact road distance computation is built by using Somewhat Homomorphic Encryption and road network hypercube embedding.We implement our two road distance computation approaches,and evaluate them on the real cityscale road network.Evaluation results show that our approaches are accurate and efficient. | Haining Yu Lailai Yin Hongli Zhang Dongyang Zhan Jiaxing Qu Guangyao Zhang | 2021 | Computers, Materials & Continua2021，，12： | 0 |
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