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1. Euler systems and relative Satake isomorphism

Author

Cai, Li, Fan, Yangyu, and Lai, Shilin

Subjects
Mathematics - Number Theory
Abstract

We explain how the tame norm relation in Euler systems is a formal consequence of the unramified Plancherel formula in the relative Langlands program and a mild refinement of the generalized Cartan decomposition. This uniformly recovers many of the known Euler systems and produces a new split anticyclotomic Euler system in a case studied by Cornut., Comment: 18 pages

Published
2024

2. Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation

Author

Huang, Hongzhe, Yu, Zhewen, Liu, Jiang, Cai, Li, Jiao, Dian, Zhang, Wenqiao, Tang, Siliang, Li, Juncheng, Jiang, Hao, Li, Haoyuan, and Zhuang, Yueting

Subjects
Computer Science - Artificial Intelligence
Abstract

Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning. Such automatic instruction collection pipelines, however, inadvertently introduce significant variability in data quality. This paper introduces a novel instruction curation algorithm, derived from two unique perspectives, human and LLM preference alignment, to compress this vast corpus of machine-generated multimodal instructions to a compact and high-quality form: (i) For human preference alignment, we have collected a machine-generated multimodal instruction dataset and established a comprehensive set of both subjective and objective criteria to guide the data quality assessment critically from human experts. By doing so, a reward model was trained on the annotated dataset to internalize the nuanced human understanding of instruction alignment. (ii) For LLM preference alignment, given the instruction selected by the reward model, we propose leveraging the inner LLM used in MLLM to align the writing style of visual instructions with that of the inner LLM itself, resulting in LLM-aligned instruction improvement. Extensive experiments demonstrate that we can maintain or even improve model performance by compressing synthetic multimodal instructions by up to 90%. Impressively, by aggressively reducing the total training sample size from 158k to 14k (9$\times$ smaller), our model consistently outperforms its full-size dataset counterpart across various MLLM benchmarks. Our project is available at https://github.com/DCDmllm/Align2LLaVA.

Published
2024

3. Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning

Author

Chen, Xinhao, Yang, Chong, Lan, Man, Cai, Li, Chen, Yang, Hu, Tu, Zhuang, Xinlin, and Zhou, Aimin

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Empathetic response generation endows agents with the capability to comprehend dialogue contexts and react to expressed emotions. Previous works predominantly focus on leveraging the speaker's emotional labels, but ignore the importance of emotion cause reasoning in empathetic response generation, which hinders the model's capacity for further affective understanding and cognitive inference. In this paper, we propose a cause-aware empathetic generation approach by integrating emotions and causes through a well-designed Chain-of-Thought (CoT) prompt on Large Language Models (LLMs). Our approach can greatly promote LLMs' performance of empathy by instruction tuning and enhancing the role awareness of an empathetic listener in the prompt. Additionally, we propose to incorporate cause-oriented external knowledge from COMET into the prompt, which improves the diversity of generation and alleviates conflicts between internal and external knowledge at the same time. Experimental results on the benchmark dataset demonstrate that our approach on LLaMA-7b achieves state-of-the-art performance in both automatic and human evaluations.

Published
2024

4. Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints

Author

Song, Ran, He, Shizhu, Gao, Shengxiang, Cai, Li, Liu, Kang, Yu, Zhengtao, and Zhao, Jun

Subjects
Computer Science - Computation and Language
Abstract

Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like (h, r, ?) in different languages by reasoning a tail entity t thus improving multilingual knowledge graphs. Previous studies leverage multilingual pretrained language models (PLMs) and the generative paradigm to achieve mKGC. Although multilingual pretrained language models contain extensive knowledge of different languages, its pretraining tasks cannot be directly aligned with the mKGC tasks. Moreover, the majority of KGs and PLMs currently available exhibit a pronounced English-centric bias. This makes it difficult for mKGC to achieve good results, particularly in the context of low-resource languages. To overcome previous problems, this paper introduces global and local knowledge constraints for mKGC. The former is used to constrain the reasoning of answer entities, while the latter is used to enhance the representation of query contexts. The proposed method makes the pretrained model better adapt to the mKGC task. Experimental results on public datasets demonstrate that our method outperforms the previous SOTA on Hits@1 and Hits@10 by an average of 12.32% and 16.03%, which indicates that our proposed method has significant enhancement on mKGC., Comment: 11 pages, ACL 2023

Published
2024

5. DuetRAG: Collaborative Retrieval-Augmented Generation

Author

Jiao, Dian, Cai, Li, Huang, Jingsheng, Zhang, Wenqiao, Tang, Siliang, and Zhuang, Yueting

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks. However, contemporary RAG approaches suffer from irrelevant knowledge retrieval issues in complex domain questions (e.g., HotPot QA) due to the lack of corresponding domain knowledge, leading to low-quality generations. To address this issue, we propose a novel Collaborative Retrieval-Augmented Generation framework, DuetRAG. Our bootstrapping philosophy is to simultaneously integrate the domain fintuning and RAG models to improve the knowledge retrieval quality, thereby enhancing generation quality. Finally, we demonstrate DuetRAG' s matches with expert human researchers on HotPot QA., Comment: 5 pages

Published
2024

6. A universal strategy for decoupling stiffness and extensibility of polymer networks

Author

Huang, Baiqiang, Nian, Shifeng, and Cai, Li-Heng

Subjects
Condensed Matter - Soft Condensed Matter, Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract

Since the invention of polymer networks in the 19th century (e.g., crosslinked natural rubber by Goodyear), it has been a dogma that stiffer networks are less stretchable, a trade-off inherent to the molecular nature of polymer network strands. Here, we report a universal strategy for decoupling the stiffness and extensibility of single-network elastomers. Instead of using linear polymers as network strands, we use foldable bottlebrush polymers, which feature a collapsed backbone grafted with many linear side chains. Upon elongation, the collapsed backbone unfolds to release stored length, enabling remarkable extensibility. By contrast, the network elastic modulus is inversely proportional to the network strand mass and is determined by the side chains. We validate this concept by creating a series of unentangled single-network elastomers with nearly constant Young's modulus (30 kPa) while increasing tensile breaking strain by 40-fold, from 20% to 800%. We show that this strategy applies to networks of different polymer species and topologies. Our discovery opens an avenue for developing polymer networks with extraordinary mechanical properties., Comment: 4 figures; 10 extended figures; 1 extended table

Published
2024

7. A Survey on Temporal Knowledge Graph: Representation Learning and Applications

Author

Cai, Li, Mao, Xin, Zhou, Yuhao, Long, Zhaoguang, Wu, Changxu, and Lan, Man

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledge graph representation learning methods, and provide an in-depth analysis of different methods in each category. Finally, we present various downstream applications related to the temporal knowledge graphs. In the end, we conclude the paper and have an outlook on the future research directions in this area.

Published
2024

8. Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space

Author

Cai, Li, Mao, Xin, Wang, Zhihong, Zhao, Shangqing, Zhou, Yuhao, Wu, Changxu, and Lan, Man

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time. Existing methods, operating in real or complex spaces, have demonstrated promising performance in this task. This paper advances beyond conventional approaches by introducing more expressive quaternion representations for TKGC within hypercomplex space. Unlike existing quaternion-based methods, our study focuses on capturing time-sensitive relations rather than time-aware entities. Specifically, we model time-sensitive relations through time-aware rotation and periodic time translation, effectively capturing complex temporal variability. Furthermore, we theoretically demonstrate our method's capability to model symmetric, asymmetric, inverse, compositional, and evolutionary relation patterns. Comprehensive experiments on public datasets validate that our proposed approach achieves state-of-the-art performance in the field of TKGC.

Published
2024

10. Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs

Author

Xu, Yao, He, Shizhu, Wang, Cunguang, Cai, Li, Liu, Kang, and Zhao, Jun

Subjects
Computer Science - Artificial Intelligence
Abstract

Complex Query Answering (CQA) is a challenge task of Knowledge Graph (KG). Due to the incompleteness of KGs, query embedding (QE) methods have been proposed to encode queries and entities into the same embedding space, and treat logical operators as neural set operators to obtain answers. However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency. In this paper, we propose Query to Triple (Q2T), a novel approach that decouples the training for simple and complex queries. Q2T divides the training into two stages: (1) Pre-training a neural link predictor on simple queries to predict tail entities based on the head entity and relation. (2) Training a query encoder on complex queries to encode diverse complex queries into a unified triple form that can be efficiently solved by the pretrained neural link predictor. Our proposed Q2T is not only efficient to train, but also modular, thus easily adaptable to various neural link predictors that have been studied well. Extensive experiments demonstrate that, even without explicit modeling for neural set operators, Q2T still achieves state-of-the-art performance on diverse complex queries over three public benchmarks., Comment: Accepted by EMNLP 2023 findings

Published
2023

11. Ichino period for CM forms

Author

Cai, Li, Fan, Yangyu, and Jiang, Yong

Subjects
Mathematics - Number Theory
Abstract

In both local and global settings, we establish explicit relations between Ichino triple product period and Waldspurger toric periods for CM forms via the theta lifting and the see-saw principle.

Published
2023

12. A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Author

Yuanning Zheng, Cai Li, Wen Zhou, Zhantang Xu, Xianqing Zhang, Wenxi Cao, Zeming Yang, and Changjian Liu

Subjects
Water quality monitoring, Machine learning, Chlorophyll a, Hydro-meteorology, South China Sea, Medicine, Science
Abstract

Abstract Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

Published
2024
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13. Unravelling the shift: exploring consumers’ adoption or resistance of E-Pharmacy through behavioural reasoning theory

Author

Agyemang Kwasi Sampene, Cai Li, and John Wiredu

Subjects
Adoption, Consumer behaviour, Behaviour reasoning theory, E-pharmacy, Resistance, Public aspects of medicine, RA1-1270
Abstract

Abstract In the ever-evolving nature of the healthcare sector, the advent of electronic-Pharmacy introduces a dynamic shift in how consumers acquire and access medical and pharmaceutical products. The research utilized the behaviour reasoning theory. The study evaluated the reasons for adopting and against adopting electronic-Pharmacy. By employing the qualitative approach, this study unravels rich contextual and narrative insights, shedding light on the complexities of individual decision-making processes. The study received responses from 28 through an in-depth interview, and thematic data analysis was employed for the data analysis. The outcome of the research is summarized as follows. The respondents indicated that essential reasons for adopting electronic pharmacy services include convenience and accessibility, prescription management, cost and affordability, logistics, and timely delivery. On the other hand, the reasons against adoption include trust and security concerns, regulatory challenges and legal uncertainties, lack of internet access and privacy concerns. As technology changes healthcare delivery, this research closes the knowledge gap between theory and practice by offering crucial insights into the behavioural aspects influencing electronic-Pharmacy adoption or resistance. The findings are anticipated to significantly impact the academic discourse surrounding electronic-health and the practical implementation of strategies to enhance the integration of electronic-Pharmacy services into conventional healthcare systems.

Published
2024
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14. The association between serum S100β levels and prognosis in acute stroke patients after intravenous thrombolysis: a multicenter prospective cohort study

Author

Yang Qu, Hang Jin, Reziya Abuduxukuer, Shuang Qi, Xiang-Kun Si, Peng Zhang, Ke-Jia Zhang, Si-Ji Wang, Xiang-Yu Zheng, Yu Zhang, Jian-Hua Gao, Xian-Kun Zhang, Xiao-Dong Liu, Chun-Ying Li, Guang-Cai Li, Junmin Wang, Huimin Jin, Ying He, Ligang Jiang, Liang Liu, Yongfei Jiang, Rui-Hong Teng, Yan Jia, Bai-Jing Zhang, Zhibo Chen, Yingbin Qi, Xiuping Liu, Song Li, Xin Sun, Thanh N. Nguyen, Yi Yang, Zhen-Ni Guo, and on behalf of the Biomarkers of Brain Injury Investigator Study Group

Subjects
S100β, Intravenous thrombolysis, Acute ischemic stroke, Outcome, Astroglial injury, Medicine
Abstract

Abstract Background S100β is a biomarker of astroglial damage, the level of which is significantly increased following brain injury. However, the characteristics of S100β and its association with prognosis in patients with acute ischemic stroke following intravenous thrombolysis (IVT) remain unclear. Methods Patients in this multicenter prospective cohort study were prospectively and consecutively recruited from 16 centers. Serum S100β levels were measured 24 h after IVT. National Institutes of Health Stroke Scale (NIHSS) and hemorrhagic transformation (HT) were measured simultaneously. NIHSS at 7 days after stroke, final infarct volume, and modified Rankin Scale (mRS) scores at 90 days were also collected. An mRS score ≥ 2 at 90 days was defined as an unfavorable outcome. Results A total of 1072 patients were included in the analysis. The highest S100β levels (> 0.20 ng/mL) correlated independently with HT and higher NIHSS at 24 h, higher NIHSS at 7 days, larger final infarct volume, and unfavorable outcome at 3 months. The patients were divided into two groups based on dominant and non-dominant stroke hemispheres. The highest S100β level was similarly associated with the infarct volume in patients with stroke in either hemisphere (dominant: β 36.853, 95% confidence interval (CI) 22.659–51.048, P

Published
2024
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15. An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation

Author

Cai, Li, Mao, Xin, Xiao, Youshao, Wu, Changxu, and Lan, Man

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion. With the wide use of temporal knowledge graphs (TKGs), time-aware EA (TEA) methods appear to enhance EA. Existing TEA models are based on Graph Neural Networks (GNN) and achieve state-of-the-art (SOTA) performance, but it is difficult to transfer them to large-scale TKGs due to the scalability issue of GNN. In this paper, we propose an effective and efficient non-neural EA framework between TKGs, namely LightTEA, which consists of four essential components: (1) Two-aspect Three-view Label Propagation, (2) Sparse Similarity with Temporal Constraints, (3) Sinkhorn Operator, and (4) Temporal Iterative Learning. All of these modules work together to improve the performance of EA while reducing the time consumption of the model. Extensive experiments on public datasets indicate that our proposed model significantly outperforms the SOTA methods for EA between TKGs, and the time consumed by LightTEA is only dozens of seconds at most, no more than 10% of the most efficient TEA method., Comment: Accepted by IJCAI 2023

Published
2023

16. On the graph products of simplicial groups and connected Hopf algebras

Author

Cai, Li

Subjects
Mathematics - Algebraic Topology, 55T05, 55U10, 55P48
Abstract

In this paper we study the classifying spaces of graph products of simplicial groups and connected Hopf algebras over a field, and show that they can be uniformly treated under the framework of polyhedral products. It turns out that these graph products are models of the loop spaces of polyhedral products over a flag complex and their homology, respectively. Certain morphisms between graph products are also considered. In the end we prove the structure theorems of such graph products in the form we need., Comment: 45 pages

Published
2023

34. Multiplicities of Representations in Algebraic Families

Author

Cai, Li and Fan, Yangyu

Subjects
Mathematics - Number Theory, Mathematics - Representation Theory
Abstract

In this short notes, we consider multiplicities of representations in general algebraic families, especially the upper semi-continuity of homological multiplicities and the locally constancy of Euler-Poincare numbers. This generalizes the main result of Aizenbud-Sayag for unramified twisting families., Comment: Part of the results overlaps with arXiv:2107.05921v1. To appear in Comptes Rendus-S\'erie Math\'ematique

Published
2023

35. Spherical Characters in Families: the unitary Gan-Gross-Prasad case

Author

Cai, Li and Fan, Yangyu

Subjects
Mathematics - Representation Theory, Mathematics - Number Theory
Abstract

We consider the variation of spherical characters in families. We formulate conjectures for the rationality and meromorphic property of spherical characters. As an example, we establish these conjectures in the unitary Gan-Gross-Prasad case., Comment: Part of the results overlaps with arXiv:2107.05921

Published
2023

36. Higher Ext-groups in the triple product case

Author

Cai, Li and Fan, Yangyu

Subjects
Mathematics - Number Theory, Mathematics - Representation Theory
Abstract

In this short note, we compute higher extension groups for all irreducible representations and deduce the multiplicity formula for finite length representations in triple product case., Comment: To appear in Math. Res. Lett

Published
2023

37. Research advances of cell autophagy in focal segmental glomerulosclerosis

Author

Min Zhang, Yan Mi, and Cai-li Wang

Subjects
focal segmental glomerulosclerosis, autophagy, mtorc1, atg, Internal medicine, RC31-1245
Abstract

As a clinical pathological syndrome, focal segmental glomerulosclerosis （FSGS） is characterized by focal glomerulosclerosis, disappearance of podocyte processes and massive proteinuria. Current researches have confirmed that foot cell injury is a central pathogenetic cause of FSGS. And a growing number of studies have demonstrated that oxidative stress, inflammation and disruption of proximal tubular epithelial cells （PTECs） and glomerular endothelial cells （GECs） contribute to the development of FSGS. In recent years, autophagy has attracted greater attention in various research fields. It proved that autophagy plays an important regulatory role in FSGS, And it provided a novel therapeutic target FSGS. This review focused upon autophagy and its roles in FSGS.

Published
2024
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38. Design and Implementation of Core Surveillance System for HPR1000 Reactor

Author

ZHANG Xiangju, CAI li, WANG Junling, YANG Mengyi, LUO Shijie, LU Haoliang, PENG Sitao, LI Jinggang, WANG Ting

Subjects
hpr1000, spnd, core surveillance system, sophora, Nuclear engineering. Atomic power, TK9001-9401, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Difference from the movable neutron detector for CPR1000 reactor can only be used periodically, HPR1000 and other Gen-Ⅲ reactor innovatively use self-powered neutron detector (SPND) to continuously measure the in-core neutron flux. This paper provided detailed design and implementation of the high-precision core surveillance system for the first HPR1000 reactor. Firstly, a novel SPND signal processing method was proposed, which not only solved the limitation of traditional design software for high-precision SPND current calculation, but also enabled high-precision signal processing of measured SPND. Based on this, a high-precision core 3D power reconstruction method was established. Subsequently, the online monitoring system SOPHORA for the HPR1000 reactor core designed based on this theoretical model was described, and its uncertainty analysis method was explained in detail. The uncertainty analysis results indicate that the accuracy of the key parameters of the system can meet the needs of high-precision core monitoring. Finally, in order to confirm the performance of the system, a comparative analysis was conducted on the deviation between the measurement and theoretical prediction of SPND current during the start-up process of the HPR1000 reactor, as well as the deviation of component power distribution. The results show that the deviation is much smaller than the regulatory requirements for initiating physical tests. The double validation of the uncertainty analysis results and the component power deviation analysis results during the start-up process indicates that SOPHORA achieves high-precision core monitoring, and its use of SPND signal processing and core 3D power reconstruction methods has significant accuracy and reliability.

Published
2024
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39. Normalized solutions for the double-phase problem with nonlocal reaction

Author

Cai Li and Zhang Fubao

Subjects
double-phase problem, normalized solutions, hardy-littlewood-sobolev critical exponent, 35a15, 35b09, 35j92, Analysis, QA299.6-433
Abstract

In this article, we consider the double-phase problem with nonlocal reaction. For the autonomous case, we introduce the methods of the Pohozaev manifold, Hardy-Littlewood Sobolev subcritical approximation, adding the mass term to prove the existence and nonexistence of normalized solutions to this problem. For the nonautonomous case, we show the existence of normalized solutions to the double-phase problem by using the Pohozaev restrict method and describing the relationship between the energy of this problem and its limit problem. Moreover, we study the existence of normalized solutions to the double-phase problem involving double Hardy-Littlewood-Sobolev critical exponents.

Published
2024
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40. Geological carbon storage and compressed gas energy storage: current status, challenges, and prospects

Author

Qingcheng HE, Cai LI, Chaobin GUO, Xinwen WANG, Baojian ZHANG, Lichao YANG, Xia LI, and Yutong ZHU

Subjects
geological carbon storage, geological compressed gas energy storage, saline aquifers, depleted gas/oil reservoirs, salt caverns, survey and evaluation, Geology, QE1-996.5
Abstract

Carbon capture and storage (CCS) and geological energy storage are essential technologies for mitigating global warming and achieving China’s “dual carbon” goals. Carbon storage involves injecting carbon dioxide into suitable geological formations at depth of 800 meters or more for permanent isolation. Geological energy storage, on the other hand, involves compressing air or other gases using surplus electricity during off-peak hours and temporarily storing them in underground reservoirs. These gases are then released during peak hours for power generation. Both technologies share commonalities in reservoir selection, with aquifers, depleted oil and gas reservoirs, and salt caverns all serving as potential storage sites. Carbon storage demands long-term containment, while geological energy storage necessitates multiple cycles of storage and release, requiring careful consideration during site evaluation. CCS projects are rapidly increasing globally, evolving towards networked and clustered configurations. In China, CCS projects are primarily focused on CO2-enhanced oil recovery, with fewer dedicated storage projects. However, direct storage projects are projected to dominate in the future and are also transitioning towards clustered development. China possesses favorable geological conditions for carbon storage, with relatively accurate estimates for oil and gas reservoir storage potential. Nevertheless, significant uncertainties persist regarding the storage capacity of saline aquifers. Compressed air energy storage in salt caverns is currently the predominant type of geological energy storage projects. Germany, the USA, and China have a total of five operating compressed air salt cavern energy storage power plants. China has abundant salt cavern resources, albeit with complex geological conditions. Suitable construction sites are concentrated in the eastern regions, and numerous projects are already underway. Compared to salt caverns, porous formations such as aquifers and depleted oil and gas reservoirs are more widespread and offer higher storage potential. However, technical challenges related to multiphase flow and chemical reactions need to be addressed. However, current site selection, potential assessment, efficiency optimization, and monitoring technologies face considerable challenges in meeting the demands of large-scale practical applications. Traditional hydrogeological exploration methods prove inadequate for selecting suitable sites, highlighting the need for efficient monitoring and risk control techniques. Additionally, there is a lack of cost-effective and accurate continuous monitoring technologies specifically designed for pressure and stress changes in storage and caprock formations. The development of key equipment components, such as monitoring and power generation systems, with independent intellectual property rights remains limited. Moreover, our reservoir simulation software requires further advancements to effectively simulate complex reservoirs at large scales. It is crucial to prioritize research and development in resource exploration, site selection technologies, and engineering equipment for both carbon sequestration and geological energy storage. The establishment of diverse demonstration projects and facilities for various storage options such as saline aquifers, depleted oil/gas fields are needed in the future as well.

Published
2024
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41. Site suitability evaluation method and application of compressed gas geological energy storage in lithologic trap

Author

Chaobin GUO, Fugang WANG, Cai LI, and Yutong ZHU

Subjects
geological storage, suitability evaluation, numerical simulation, hydrological, lithologic trap, Geology, QE1-996.5
Abstract

The current evaluation of the suitability of lithological trap-type compressed gas geological storage sites is mostly based on static factors. It lacks a coupled analysis of dynamic multiple factors, leading to a significant gap between the assessment results and actual engineering applications. To develop a refined method for evaluating the suitability of energy storage sites, an integrated approach that combines static feasibility analysis with dynamic performance assessment, considering key aspects such as reservoir properties, energy storage safety, and practical operability was proposed. The method was applied in the A2 geological formation of the Gudong Oilfield. Through the static analysis of site geological features, quantitative evaluation using GPSFLOW numerical simulation software, and on-site pilot gas injection tests, the results show that after injecting 9.4×104 m3 of air, the pressure in the A2 geological formation decreases by 8.16% within 6 days. It indicates the good sealing performance meeting the requirements of energy storage space. Considering the dynamic performance of energy storage systems, the suitability evaluation method can provide more accurate data support for the site selection, construction, evaluation, and optimization of energy storage efficiency, further promoting sustainable development of clean energy utilization and energy transition.

Published
2024
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42. Effect of acupuncture on tic disorder: a randomized controlled clinical trial based on energy metabolomics and infrared thermography

Author

Yi-ming Fan, Ying-xin Li, Yi Zhang, Dong Chen, Meng-qian Yuan, Yan-cai Li, Elsie Sin May Teo, Ming-hui Xu, Yang-yang Zhou, Pan-feng Yang, Cheng-mei Zhao, Jing-jing Zheng, Jian-bing Li, and Chao Bao

Subjects
Acupuncture, Tic disorder, Metabonomics, Infrared thermography, Randomized controlled trial, Study protocol, Other systems of medicine, RZ201-999
Abstract

Abstract Background Acupuncture is a method for treating tic disorder. However, there is a lack of sufficient clinical objective basis in regards of its treatment efficacy. Indeed, there are structural abnormalities present in energy metabolism and infrared thermography in children with tic disorder. Therefore, this study proposes a clinical trial scheme to explore the possible mechanism of acupuncture in treating tic disorder. Methods This randomized controlled trial will recruit a total of 90 children, in which they will be divided into non-intervention group and intervention group. The non-intervention group consists of 30 healthy children while the intervention group consists of 60 children with tic disorder. The intervention group will be randomly allocated into either the treatment group or the control group, with 30 children randomly assigned in each group. Children either received acupuncture treatment and behavioral therapy (treatment group) or sham acupuncture treatment and behavioral therapy (control group), 3 treatment sessions per week for a period of 12 weeks, with a total of 36 treatment sessions. Outcome measures include YGTSS, urinary and fecal metabolomics, infrared thermography of body surface including governor vessel. For the intervention group, these outcome measures will be collected at the baseline and 90th day prior to intervention. Whereas for the non-intervention group, outcome measures (excluding YGTSS) will be collected at the baseline. Discussion The main outcome will be to observe the changes of the severity of tic condition, the secondary outcome will be to observe the changes of structural characteristic of infrared thermography of body surface/acupoints along the governor vessel and to evaluate the changes of urinary and fecal metabolomics at the end of the treatment, so as to analyze the relationship between them and to provide further knowledge in understanding the possible mechanism of acupuncture in improving the clinical symptoms via regulating and restoring the body metabolomics network, which in future it can develop as a set of clinical guideline (diagnosis, treatment, assessment, prognosis) in treating tic disorder. ChiCTR2300075188(Chinese Clinical Trial Registry, http://www.chictr.org.cn , registered on 29 August 2023).

Published
2024
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43. Environmental technology to stimulate life expectancy: A deep dive into the role of PM2.5 and carbon emission

Author

Agyemang Kwasi Sampene, Cai Li, and John Wiredu

Subjects
Environmental technology, Cleaner energy transitions, Industrialization, PM2.5, Carbon emission, Life expectancy, Environmental sciences, GE1-350
Abstract

As the Latin American economies contend with improving public health while simultaneously dealing with ecological issues, understanding the determinant of life expectancy becomes imperative. This study explores the interaction among environmental technology, economic development, cleaner energy transitions, industrial growth, particulate Matter 2.5 (PM2.5), carbon emission, and their collective impact on life expectancy across Latin American nations. The research incorporates advanced econometric approaches comprising Mean Group (MG), Pooled Mean Group (PMG), and Method of Moments Quantile Regression (MMQR). The study’s outcome established that cleaner energy transitions, Gross Domestic Product (GDP), industrialization and environmental technologies significantly improve life expectancy, especially at higher quantiles. However, the outcome highlighted that PM2.5 and carbon emissions reduce life expectancy. Additionally, this study supports the unidirectional connection between environmental technology, economic development, cleaner energy transitions, industrial growth, life expectancy, and the bidirectional connection flowing from carbon emission PM2.5 to life expectancy. The study offers a roadmap for Latin America and global economies to invest in technology, cleaner power supply and industrialization to provide a route toward sustainable development and better public health that will eventually improve their citizens’ life expectancy.

Published
2024
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44. Analytical and clinical validation of a NGS panel in detecting targetable variants from ctDNA of metastatic NSCLC patients

Author

Feifei Fan, Guozhong Jiang, Juan Lv, Hongmin Wang, Wenjie Li, Chenglin Liu, Yu Zhao, Zhou Zhang, Haiwei Du, Zhihong Zhang, Xiangnan Li, and Wen‐cai Li

Subjects
circulating tumor DNA, concordance, next‐generation sequencing, targetable variants, tissue sample, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for noninvasive cancer diagnostics, particularly in the context of metastatic non‐small‐cell lung cancer (NSCLC). Detecting targetable variants through ctDNA analysis offers the potential to guide treatment decisions, especially in cases where tissue samples are insufficient or unavailable. Method In this study, we developed and validated a next‐generation sequencing panel targeting 101 cancer‐related genes (101‐test) to detect somatic variants in ctDNA from a large cohort of Chinese patients with metastatic NSCLC. The performance of the 101‐test was assessed by evaluating its limit of detection (LOD), accuracy, and precision in identifying molecular variants. Additionally, the concordance between ctDNA and tissue samples for detecting targetable variants was analyzed in 904 patients. Results The 101‐test demonstrated a LOD of 0.38% for single‐nucleotide variants (SNVs), 0.33% for insertions and deletions (InDels), and 0.33% for fusions. Sensitivity was 98.3% for SNVs, 100% for InDels, and 100% for fusions when compared to digital droplet PCR (ddPCR)/breakpoint PCR reference methods. The by‐variant sensitivity for somatic variants was 97.5%, with a specificity of 99.9% between tumor‐only and tumor‐normal analyses. In a real‐world cohort, the concordance between ctDNA and tissue samples for identifying targetable variants was 72.2% (457/633). Notably, the EGFR S768I variant, recently recommended by clinical guidelines, achieved an 80% concordance rate. Furthermore, 4.3% of patients (27/633) with targetable variants were identified exclusively through ctDNA testing. Conclusion The ctDNA‐based 101‐test is a highly sensitive and specific tool for detecting targetable variants in metastatic NSCLC, particularly in cases with insufficient tissue samples. The findings support the use of ctDNA testing as a reliable and complementary method to traditional tissue‐based molecular analysis, enhancing the precision of treatment strategies for NSCLC patients.

Published
2024
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46. Incremental Model Fit Assessment in the Case of Categorical Data: Tucker-Lewis Index for Item Response Theory Modeling.

Author

Cai, Li, Chung, Seung, and Lee, Taehun

Subjects
Categorical data analysis, Goodness of fit, Item response theory, Limited-information testing, Model evaluation, TLI, Humans, Factor Analysis, Statistical, Psychometrics, Reproducibility of Results
Abstract

The Tucker-Lewis index (TLI; Tucker & Lewis, 1973), also known as the non-normed fit index (NNFI; Bentler & Bonett, 1980), is one of the numerous incremental fit indices widely used in linear mean and covariance structure modeling, particularly in exploratory factor analysis, tools popular in prevention research. It augments information provided by other indices such as the root-mean-square error of approximation (RMSEA). In this paper, we develop and examine an analogous index for categorical item level data modeled with item response theory (IRT). The proposed Tucker-Lewis index for IRT (TLIRT) is based on Maydeu-Olivares and Joes (2005) [Formula: see text] family of limited-information overall model fit statistics. The limited-information fit statistics have significantly better Chi-square approximation and power than traditional full-information Pearson or likelihood ratio statistics under realistic situations. Building on the incremental fit assessment principle, the TLIRT compares the fit of model under consideration along a spectrum of worst to best possible model fit scenarios. We examine the performance of the new index using simulated and empirical data. Results from a simulation study suggest that the new index behaves as theoretically expected, and it can offer additional insights about model fit not available from other sources. In addition, a more stringent cutoff value is perhaps needed than Hu and Bentlers (1999) traditional cutoff criterion with continuous variables. In the empirical data analysis, we use a data set from a measurement development project in support of cigarette smoking cessation research to illustrate the usefulness of the TLIRT. We noticed that had we only utilized the RMSEA index, we could have arrived at qualitatively different conclusions about model fit, depending on the choice of test statistics, an issue to which the TLIRT is relatively more immune.

Published
2023

47. Anomalous Nernst effect in compensated ferrimagnetic CoxGd1-x films

Author

Liu, Ruihao, Cai, Li, Xu, Teng, Liu, Jiahao, Cheng, Yang, and Jiang, Wanjun

Subjects
Condensed Matter - Materials Science
Abstract

The anomalous Nernst effect (ANE) is one of the most intriguing thermoelectric phenomena which has attracted growing interest both for its underlying physics and potential applications. Typically, a large ANE response is observed in magnets with pronounced magnetizations or nontrivial Berry curvature. Here, we report a significant ANE signal in compensated ferrimagnetic CoxGd1-x alloy films, which exhibit vanishingly small magnetization. In particular, we found that the polarity of ANE signal is dominated by the magnetization orientation of the transition metal Co sublattices, rather than the net magnetization of CoxGd1-x films. This observation is not expected from the conventional understanding of ANE but is analogous to the anomalous Hall effect in compensated ferrimagnets. We attribute the origin of ANE and its Co-dominant property to the Co-dominant Berry curvature. Our work could trigger a more comprehensive understanding of ANE and may be useful for building energy-harvesting devices by employing ANE in compensated ferrimagnets.

Published
2022
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48. Infinite time bubble towers in the fractional heat equation with critical exponent

Author

Cai, Li, Wang, Jun, Wei, Jun-Cheng, and Yang, Wen

Subjects
Mathematics - Analysis of PDEs
Abstract

In this paper, we consider the fractional heat equation with critical exponent in $\mathbb{R}^n$ for $n>6s,s\in(0,1),$ \begin{equation*} u_t=-(-\Delta)^su+|u|^{\frac{4s}{n-2s}}u,\quad (x,t)\in \mathbb{R}^n\times\mathbb{R}. \end{equation*} We construct a bubble tower type solution both for the forward and backward problem by establishing the existence of the sign-changing solution with multiple blow-up at a single point with the form \begin{equation*} u(x,t)=(1+o(1))\sum_{j=1}^{k}(-1)^{j-1}\mu_j(t)^{-\frac{n-2s}{2}}U\left(\frac{x}{\mu_j(t)}\right) \quad\mbox{as}\quad t\to+\infty, \end{equation*} and the positive solution with multiple blow-up at a single point with the form \begin{equation*} u(x,t)=(1+o(1))\sum_{j=1}^{k}\mu_j(t)^{-\frac{n-2s}{2}}U\left(\frac{x}{\mu_j(t)}\right) \quad\mbox{as}\quad t\to-\infty, \end{equation*} respectively. Here $k\ge2$ is a positive integer, $$U(y)=\alpha_{n,s}\left(\frac{1}{1+|y|^2}\right)^{\frac{n-2s}{2}},$$ and \begin{equation*} \mu_j(t)=\beta_j |t|^{-\alpha_j}(1+o(1))~\mathrm{as}~t\to\pm\infty, \quad \alpha_j=\frac{1}{2s}\left(\frac{n-2s}{n-6s}\right)^{j-1}-\frac{1}{2s}, \end{equation*} for some certain positive numbers $\beta_j,j=1,\cdots,k.$, Comment: 44 pages

Published
2022

49. A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs

Author

Cai, Li, Mao, Xin, Ma, Meirong, Yuan, Hao, Zhu, Jianchao, and Lan, Man

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanism to incorporate relational and temporal information into entity embeddings. The approaches outperform the previous methods by using temporal information. However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations. Therefore, we propose a simple graph neural network (GNN) model combined with a temporal information matching mechanism, which achieves better performance with less time and fewer parameters. Furthermore, since alignment seeds are difficult to label in real-world applications, we also propose a method to generate unsupervised alignment seeds via the temporal information of TKG. Extensive experiments on public datasets indicate that our supervised method significantly outperforms the previous methods and the unsupervised one has competitive performance., Comment: Accepted by COLING 2022

Published
2022

50. Fine-scale measurements unravel the side effects of biochar capping on the bioavailability and mobility of phosphorus in sediments

Author

Xin Ma, Yijun Song, Yilun Shen, Liyuan Yang, Shiming Ding, Cai Li, and Qin Sun

Subjects
Biochar remediation, Sediment phosphorus, Spatiotemporal variation, Risk assessment, Environmental sciences, GE1-350, Agriculture
Abstract

Abstract Biochar is widely used for sediment remediation owing to its excellent adsorption properties and low carbon footprint. However, the impacts of biochar capping on phosphorus (P) bioavailability and mobility in the sediment are little known. In this study, the P mobilization processes in sediments capped with biochar were investigated by combining advanced high-resolution sampling techniques and microbiome analysis. The results showed that biochar is a double-edged sword for the sediment P release, depending on the application dosage and the capping time. In the short term (30 days), 2-cm biochar capping decreased the release flux of soluble reactive phosphorus (SRP) by 73.1%, whereas the 1-cm biochar capping significantly increased the release flux of SRP by 51.0%. After aging of biochar (80 days), the resupply capacity of sediment P was improved, resulting in increases of more than 33.7% and 121.5% in the release fluxes of SRP in the 1-cm and 2-cm capping groups, respectively, compared to the control group. Chemisorption played a pivotal role in regulating the levels of SRP, particularly during the short-term capping period. And more biochar can provide more adsorption sites on P. The P mobilization increase could be attributed to P desorption from biochar after biochar aging. Furthermore, biochar capping intensified the microbial-mediated iron reduction and organic matter decomposition, which enhanced P mobility. Our study highlights the importance of biochar application dosage and the capping time in sediment remediation, providing a scientific basis for the optimization of biochar capping techniques. Graphical Abstract

Published
2024
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