Browsing by Author "Li, Xiang"
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Item Cathode electrocatalyst in aprotic lithium oxygen (Li-O2) battery: A literature survey(Elsevier Science B.V., 2023-08-01) Qiu, Qianyuan; Long, Jilan; Yao, Penghui; Wang, Jiaqi; Li, Xiang; Pan, Zheng-Ze; Zhao, Yicheng; Li, Yongdan; Department of Chemical and Metallurgical Engineering; Industrial chemistryLithium oxygen battery (LOB) is a highly promising energy storage device for the next generation electric vehicles due to its high theoretical energy density. However, many challenges hinder its practical application. The electrochemical performances, such as discharge capacity, discharge and charge overpotentials, power density and stability are all far from satisfactory. In recent years, a great progress has been made and numerous works on cathode materials have been published. This article focuses on the state-of-the-art of LOB cathode materials and the reaction mechanism happens on the cathode. The principles of cathode reactions are summarized and the requirements for cathode materials are discussed. The performance of LOBs with carbon-based and carbon free cathode materials are described. The approaches on improving the cathode performance via temperature increase, material surface engineering, texture optimization and oxygen vacancy regulation are elaborated. A perspective on the research trend on the cathode catalyst is finally proposed.Item Control of Foxp3 stability through modulation of TET activity(ROCKEFELLER UNIVERSITY PRESS, 2016) Yue, Xiaojing; Trifari, Sara; Äijö, Tarmo; Tsagaratou, Ageliki; Pastor, William A.; Zepeda-Martínez, Jorge A.; Lio, Chan Wang J; Li, Xiang; Huang, Yun; Vijayanand, Pandurangan; Lähdesmäki, Harri; Rao, Anjana; Department of Computer Science; Professorship Lähdesmäki Harri; Centre of Excellence in Molecular Systems Immunology and Physiology Research Group, SyMMys; La Jolla Institute for Allergy and ImmunologyTen-eleven translocation (TET ) enzymes oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine and other oxidized methylcytosines, intermediates in DNA demethylation. In this study, we examine the role of TET proteins in regulating Foxp3, a transcription factor essential for the development and function of regulatory T cells (T reg cells), a distinct lineage of CD4+ T cells that prevent autoimmunity and maintain immune homeostasis. We show that during T reg cell development in the thymus, TET proteins mediate the loss of 5mC in T reg cell-specific hypomethylated regions, including CNS1 and CNS2, intronic cis-regulatory elements in the Foxp3 locus. Similar to CNS2-deficient T reg cells, the stability of Foxp3 expression is markedly compromised in T reg cells from Tet2/Tet3 double-deficient mice. Vitamin C potentiates TET activity and acts through Tet2/ Tet3 to increase the stability of Foxp3 expression in TGF -β-induced T reg cells. Our data suggest that targeting TET enzymes with small molecule activators such as vitamin C might increase induced T reg cell efficacy.Item Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP(2018-09-07) Sui, Jinping; Liu, Zhen; Jung, Alex; Liu, Li; Li, Xiang; Department of Computer Science; Professorship Jung Alexander; National University of Defense TechnologyA key problem within data mining is clustering of data streams. Most existing algorithms for data stream clustering are based on quite restrictive models for the cluster dynamics. In an attempt to overcome the limitations of existing methods, we propose a novel data stream clustering method, which we refer to as improved streaming affinity propagation (ISTRAP). The ISTRAP is based on an integrated evolution detection framework which ensures that new emerging clusters are recognized timely. Moreover, within ISTRAP, outdated clusters are removed and recurrent clusters are efficiently detected rather than being treated as novel clusters. The proposed ISTRAP is non-parametric in the sense of not requiring any prior information about the number or the centers of clusters. The effectiveness of ISTRAP is evaluated using numerical experiments.Item Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams(IEEE, 2022) Sui, Jinping; Liu, Zhen; Liu, Li; Jung, Alex; Li, Xiang; Department of Computer Science; Professorship Jung Alexander; Helsinki Institute for Information Technology (HIIT); National University of Defense TechnologyIn an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.Item EEG Based Emotion Recognition: A Tutorial and Review(Association for Computing Machinery, 2022) Li, Xiang; Zhang, Yazhou; Tiwari, Prayag; Song, Dawei; Hu, Bin; Yang, Meihong; Zhao, Zhigang; Kumar, Neeraj; Marttinen, Pekka; Department of Computer Science; Professorship Marttinen P.; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML)Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.Item Emotion Recognition from Multi-channel EEG Data through A Dual-pipeline Graph Attention Network(2021) Li, Xiang; Li, Jing; Zhang, Yazhou; Tiwari, Prayag; Department of Computer Science; Huang, Yufei; Kurgan, Lukasz; Luo, Feng; Hu, Xiaohua Tony; Chen, Yidong; Dougherty, Edward; Kloczkowski, Andrzej; Li, Yaohang; Professorship Marttinen P.; Qilu University of Technology; Jiuquan Satellite Launch Center; Zhengzhoug University of Light IndustryEEG based emotion recognition technology is currently an important concept in artificial intelligence, and also holds great potential in emotional health care. Nevertheless, one major limitation of the prior approaches is they do not capture the relationships between different time-series and channels explicitly, resulting in inevitable low performance, especially in subject-independent recognition settings. In this paper, we propose a novel graph attention network based model to address this issue. Our framework includes dual-pipeline Graph Attention Network layers in parallel to learn the complex dependencies of multi-channel EEG in both temporal and spatial dimensions. The proposed method outperforms other state-of-the-art models on benchmark SEED dataset. Further analysis shows that our method also has good interpretability. As far as we know, it is the first work that introduce graph attention network into EEG based emotion detection research.Item MedSeq2Seq: A Medical Knowledge Enriched Sequence to Sequence Learning Model for COVID-19 Diagnosis(2021) Zhang, Yazhou; Rong, Lu; Li, Xiang; Tiwari, Prayag; Zheng, Qian; Liang, Hui; Department of Computer Science; Huang, Yufei; Kurgan, Lukasz; Luo, Feng; Hu, Xiaohua Tony; Chen, Yidong; Dougherty, Edward; Kloczkowski, Andrzej; Li, Yaohang; Professorship Marttinen P.; Zhengzhoug University of Light Industry; Qilu University of TechnologyThe COVID-19 pandemic has had a severe impact on humans' lives and and healthcare systems worldwide. How to early, fastly and accurately diagnose infected patients via multimodal learning is now a research focus. The central challenges in this task mainly lie on multi-modal data representation and multi-modal feature fusion. To solve such challenges, we propose a medical knowledge enriched multi-modal sequence to sequence learning model, termed MedSeq2Seq. The key components include two attention mechanisms, viz. intra-modal (Ia) and inter-model (Ie) attentions, and a medical knowledge augmentation mechanism. The former two mechanisms are to learn multi-modal refined representation, while the latter aims to incorporate external medical knowledge into the proposed model. The experimental results show the effectiveness of the proposed MedSeq2Seq framework over state-of-the-art baselines with a significant improvement of 1%-2%.Item Multi-Task Learning for Jointly Detecting Depression and Emotion(2021) Zhang, Yazhou; Li, Xiang; Rong, Lu; Tiwari, Prayag; Department of Computer Science; Huang, Yufei; Kurgan, Lukasz; Luo, Feng; Hu, Xiaohua Tony; Chen, Yidong; Dougherty, Edward; Kloczkowski, Andrzej; Li, Yaohang; Professorship Marttinen P.; Zhengzhoug University of Light Industry; Qilu University of TechnologyDepression is a typical mood disease that makes people a persistent feeling of sadness and loss of interest and pleasure. Emotion thus comes into sight and is tightly entangled with depression in that one helps the understanding of the other. Depression and emotion detection has been a new research task. The central challenges in this task are multi-modal interaction and multi-task correlation. The existing approaches treat them as two separate tasks, and fail to model the relationships between them. In this paper, we propose an attentive multi-modal multitask learning framework, called AMM, to generically address such issues. The core modules are two attention mechanisms, viz. inter-modal (I {mathrm{e}}) and inter-task (I {t}) attentions. The main motivation of I {mathrm{e}} attention is to learn multi-modal fused representation. In contrast, It attention is proposed to learn the relationship between depression detection and emotion recognition. Extensive experiments are conducted on two large scale datasets, i.e., DAIC and multi-modal Getty Image depression (MGID). The results show the effectiveness of the proposed AMM framework, and also shows that AMM obtains better performance for the main task, i.e., depression detection with the help of the secondary emotion recognition task.Item Online Non-Cooperative Radar Emitter Classification from Evolving and Imbalanced Pulse Streams(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-07-15) Sui, Jinping; Liu, Zhen; Liu, Li; Peng, Bo; Liu, Tianpeng; Li, Xiang; Department of Computer Science; National University of Defense Technology; University of OuluRecent research treats radar emitter classification (REC) problems as typical closed-set classification problems, i.e., assuming all radar emitters are cooperative and their pulses can be pre-obtained for training the classifiers. However, such overly ideal assumptions have made it difficult to fit real-world REC problems into such restricted models. In this paper, to achieve online REC in a more realistic way, we convert the online REC problem into dynamically performing subspace clustering on pulse streams. Meanwhile, the pulse streams have evolving and imbalanced properties which are mainly caused by the existence of the non-cooperative emitters. Specifically, a novel data stream clustering (DSC) algorithm, called dynamic improved exemplar-based subspace clustering (DI-ESC), is proposed, which consists of two phases, i.e., initialization and online clustering. First, to achieve subspace clustering on subspace-imbalanced data, a static clustering approach called the improved ESC algorithm (I-ESC) is proposed. Second, based on the subspace clustering results obtained, DI-ESC can process the pulse stream in real-time and can further detect the emitter evolution by the proposed evolution detection strategy. The typically dynamic behavior of emitters such as appearing, disappearing and recurring can be detected and adapted by the DI-ESC. Extinct experiments on real-world emitter data show the sensitivity, effectiveness, and superiority of the proposed I-ESC and DI-ESC algorithms.Item REOB/SBS Composite-Modified Bitumen Preparation and Modification Mechanism Analysis(MDPI AG, 2023-07) Li, Xiang; Guo, Dedong; Xu, Meng; Guo, Changyun; Wang, Di; Department of Civil Engineering; Mineral Based Materials and Mechanics; Shandong Jiaotong UniversityTo investigate the effect of recycled engine oil bottoms (REOB) as a compatibilizer on the properties of styrene–butadiene–styrene-modified bitumen (SBS-PMB), this paper studied the preparation method, properties, and micro-mechanism of composite modification of matrix bitumen with SBS and REOB. Firstly, a multi-factor orthogonal experiment determined the optimal preparation scheme of REOB/SBS composite-modified bitumen (REOB/SBS-PMB). Then, the high-temperature stability rheological properties, and anti-aging performance of REOB-PMB were studied by testing Brookfield viscosity, elasticity recovery, and dynamic shear rheology (DSR) and by short-term aging simulation (TFOT). Finally, the microstructure, fraction content, and SBS particle dispersion of 70# matrix bitumen (70-MB), SBS-PMB, and REOB/SBS-PMB were compared and analyzed by tests of Fourier transform infrared spectroscopy (FTIR), rod-shaped thin-layer chromatography, and fluorescence microscopy to reveal the micro-mechanism of REOB improving SBS and bitumen compatibility. The research results showed that the mixing form of SBS and REOB in bitumen was mainly physical swelling and blending, with chemical changes also present that have a minor impact. The light fraction in REOB increased the flowability of bitumen, promoted the swelling of SBS, improved the compatibility between SBS and bitumen, and improved the high-temperature stability and rheological properties while reducing the impact of aging.Item Study on the Long-Term Salt Release Characteristics of Self-Melting Ice Asphalt Mixtures and Their Impact on Pavement Performance(MDPI AG, 2024-05) Liu, Chenyang; Guo, Dedong; Sun, Xupeng; Li, Xiang; Xu, Meng; Losa, Massimo; Riccardi, Chiara; Wang, Teng; Cannone Falchetto, Augusto; Department of Civil Engineering; Mineral Based Materials and Mechanics; Shandong Jiaotong University; University of PisaSelf-melting ice asphalt pavement materials inhibit pavement freezing and improve driving safety. This paper aims to study the long-term salt release characteristics of self-melting ice asphalt mixtures and the impact on pavement after complete salt release. Firstly, a method to accelerate the rapid release of salt based on the Los Angeles abrasion tester. Then, long-term salt release patterns were elucidated under the influence of deicing agent dosage, type of asphalt, and type of gradation. Finally, a quantitative analysis of the pavement performance after complete salt release is conducted. The results indicate that the release efficiency of the Los Angeles abrasion tester method has increased by 91 times compared to the magnetic stirrer immersion flushing method and by 114 times compared to the natural soaking method. The SBS-modified self-melting ice asphalt mixture possesses a longer duration of salt release, but the uniformity of salt release is inferior. Salt release duration is directly proportional to the dosage of deicing agents. SMA-13 self-melting ice asphalt mixture exhibits poorer uniformity in salt release. After complete salt release, high-temperature stability of self-melting ice asphalt mixtures decreased by 31.6%, low-temperature performance decreased by 15.4%, water stability decreased by 26.7%, and fatigue life decreased by 35.9%.Item A synergistic approach for lignin biofuel production : Integrating non-catalytic solvolysis with catalytic product upgrading(Elsevier Science Inc., 2024-09-01) Sang, Yushuai; Li, Gen; Li, Xiang; Gong, Hanzhang; Yang, Mingze; Savary, David; Fei, Zhaofu; Dyson, Paul J.; Chen, Hong; Li, Yongdan; Department of Chemical and Metallurgical Engineering; Industrial chemistry; Industrial chemistry; Swiss Federal Institute of Technology Lausanne; Tianjin UniversityEnzymatic hydrolysis lignin (EHL) is a large-scale industrial waste generated from the bio-ethanol production process. Its complex and heterogeneous nature as well as its low solubility in common solvents, has posed a persistent barrier to its effective utilization. Here, EHL was converted into biofuel through a two-step process, involving non-catalytic solvolysis followed by catalytic product upgrading. The non-catalytic solvolysis step achieved complete EHL liquefaction in a mixture of isopropanol and H2O without char formation. In this step, H2O disrupts the hydrogen bonds in EHL and isopropanol break π-π stacking interactions between the aromatic rings in EHL·H2O also enhances EHL depolymerization, while isopropanol, as a hydrogen donor solvent, provides hydrogen that stabilizes active intermediates. In the catalytic product upgrading step, the liquid product of the first step was transformed into biofuels rich in cycloalkanes, arenes and alkylphenols, with a total carbon yield of 45.6 %. Isopropanol-H2O reforming reactions provided hydrogen for the upgrading process, avoiding the introduction of H2. This work demonstrates an effective approach for converting EHL into biofuels.Item Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global Scale(IEEE, 2020-08) Chen, Yang; Hu, Jiyao; Xiao, Yu; Li, Xiang; Hui, Pan; Department of Communications and Networking; Mobile Cloud Computing; Fudan University; University of HelsinkiBeing a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this article, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found several unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected component, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users and then look into the tip contents from the perspectives of tip venues, temporal patterns, and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to the profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users and are constructive for different relevant entities, including LBSN service providers, Internet service providers, and third-party application providers.Item User involvement in agile development processes in small and medium-sized enterprises: An interaction designer’s perspective(2017) Li, Xiang; Uusitalo, Severi; Department of Design; Muotoilun laitos; Taiteiden ja suunnittelun korkeakoulu; School of Arts, Design and Architecture; Roto, VirpiWith the popularization of Internet technology, more and more small and medium-sized enterprises (SMEs) start to provide their services online. Agile software development, as one of the most widely-adopted development methodologies in these companies, does not put the end-user aspects in the key position. The combination of user-centered design and agile software development can complement each other and improve the user satisfaction of a product, thus attracts increasing academic attention. However, in practical terms, engaging the end-users in the agile development processes seems to be vague and has many difficulties especially within SMEs. To explore the ways of enhancing the user involvement in the agile development process in SMEs as an interaction designer, I joined an ongoing mobile application development project in a small Chinese financial company called Rong360. By following an action research process, I am able to solve the user involvement problems in Rong360 and conclude the responsibilities, practical challenges and suggestions for the interaction designers, which could support their work within other similar contexts.
