Article
Computer Science, Artificial Intelligence
Mengjun Hu
Summary: The relations on agents and issues in three-way conflict analysis are important topics. While there is agreement on individual agent and issue relations, different opinions exist when it comes to sets of agents or issues. These diverse perspectives enrich conflict analysis approaches and strategies, but a general and unified framework is still needed for systematic investigation. This study proposes a framework based on quantitative subsethood measures and conducts a comprehensive and systematic study of the relations in conflict analysis, particularly focusing on agent relations with issue sets and relations between individual agents and issue sets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Yang
Summary: In this paper, a new type of fuzzy covering-based rough set model over two different universes is proposed using Zadeh's extension principle. The paper focuses on defining fuzzy beta-neighborhood, investigating its properties, defining the new fuzzy covering-based rough set model, and studying its properties. The paper also explores the necessary and sufficient condition for two fuzzy beta-coverings to generate the same fuzzy covering lower approximation or upper approximation. Moreover, the matrix representations of the fuzzy covering lower and upper approximation operators are investigated, and a new approach to a multiple criteria decision making problem is proposed based on the fuzzy covering-based rough set model over two universes.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Xin Yang, Yujie Li, Dun Liu, Tianrui Li
Summary: The approximation learning of fuzzy concepts associated with fuzzy rough sets and three-way decisions is an important technology for handling uncertain knowledge. This article explores the connection and interplay between fuzzy rough approximations and three-way approximations, proposing a hierarchical fuzzy rough approximation method in a dynamic fuzzy open-world environment. The article also discusses the interpretation and representation of fuzzy three-way regions in fuzzy rough sets. Experimental results demonstrate the effectiveness of the proposed hierarchical approximation learning models.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhanao Xue, Bingxin Sun, Haodong Hou, Wenli Pang, Yanna Zhang
Summary: This article proposes intuitionistic hesitant fuzzy sets and multi-granulation rough intuitionistic hesitant fuzzy set models, and establishes three-way decision models. The research results show that these models can effectively evaluate objects with different attitudes and provide decision-making solutions.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Dandan Zou, Yaoliang Xu, Lingqiang Li, Zhenming Ma
Summary: Variable precision (fuzzy) rough sets are generalizations of Pawlak rough sets that handle uncertain and imprecise information well. However, many existing variable precision (fuzzy) rough sets lack the comparable property (CP), which is fundamental in Pawlak rough sets. To address this issue, a novel variable precision fuzzy rough set model with CP is proposed, along with an associated three-way decision model.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jie Yang, Xiaoqi Wang, Guoyin Wang, Deyou Xia
Summary: From the perspective of human cognition, three-way decision (3WD) explores thinking, problem solving, and information processing in three paradigms. Rough fuzzy sets (RFS) are constructed to handle fuzzy concepts by extending the classical rough sets. In three-way decision with rough fuzzy sets (3WDRFS), the introduction of uncertainty measure provides a new perspective for 3WD theory, and the proposed 3WDRFS with the idea of minimizing uncertainty loss demonstrates better performance than the 0.5-approximation model.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Information Systems
Xianyong Zhang, Jiefang Jiang
Summary: This study improves the Variable Precision Multigranulation Fuzzy Rough Sets (VP-MFRSs) by proposing Decision-Theoretic Multigranulation Fuzzy Rough Sets (DT-MFRSs) which systematically fuse the multigranulation maximum and minimum. DT-MFRSs provide tri-level analysis of measurement, modeling, and reduction via three-way decisions. The study extends and improves VP-MFRSs by introducing optimistic, pessimistic, and compromised models, and enhances uncertainty optimization through a new reduction criteria.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Chengyong Jin, Bao Qing Hu
Summary: This paper introduces the concept of hesitant sets to unify different types of fuzzy sets. It discusses the construction of decision evaluation functions and three-way decisions based on hesitant sets in three-way decision spaces. The paper presents methods for constructing decision evaluation functions in semi-three-way and quasi-three-way decision spaces, as well as transformation methods to three-way decision spaces. The importance of these methods is supported by numerous examples.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoxue Wang, Xianyong Zhang
Summary: Rough sets and vague sets are fundamental methods for uncertainty, and their integration through rough vague sets provides a robust platform for data analysis. To improve vague sets, we propose linear-combined rough vague sets and model them as probabilistic rough sets using three-way decision, while also investigating optimization of uncertainty measurement.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Xiaonan Li, Xuan Wang, Bingzhen Sun, Yanhong She, Lu Zhao
Summary: This paper generalizes the model of three-way decision from 0-1 tables to general information tables, with the assignment of values to the set of objects and the construction of tri-partitions. The fundamental result identifies finitely many pairs of thresholds and describes the variation of the positive region based on thresholds. The evaluation of these finite tri-partitions by weighted entropy allows for obtaining an optimal tri-partition.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Wenyan Xu, Bing Jia, Xiaonan Li
Summary: This paper discusses the development of the theory of three-way decision and how to trisect based on different intentions, proposing a new model based on two universes. By constructing a two-universe model, the original intentions of trisecting can be more accurately expressed, and different types of problems can be addressed.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Wenqing Fu, Ahmed Mostafa Khalil
Summary: This paper introduces the concept of neighborhood rough sets and studies their basic properties, relationships with covering rough sets, and probabilistic neighborhood rough sets. It also discusses neighborhood rough sets over two different universes and algorithms designed to solve rough decision-making problems based on them. Comparisons between different approaches are made to clarify differences in applicability.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Bin Yang, Mohammed Atef
Summary: In this paper, we propose novel classes of fuzzy beta-covering-based rough set models over two distinct universes. We define fuzzy beta-minimal and beta-maximal descriptions over two distinct universes and investigate their characteristics. We also propose four novel types of fuzzy beta-neighborhoods and investigate their properties, as well as studying four types of beta-neighborhoods.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guoping Lin, Linlin Xie, Jinjin Li, Jinkun Chen, Yi Kou
Summary: This paper introduces an important expanded quantification fuzzy rough set model, the local double quantitative fuzzy rough set model over two universes, which is used to measure the relative quantitative information between fuzzy similarity classes and basic concepts. It addresses the issue of existing models ignoring the absolute quantitative information in the fuzzy information system. The properties, decision rules, and an effective reduction method of the model are studied, and experimental comparisons demonstrate its computational efficiency and approximate accuracy in concept approximation and reduction.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Murat Diker, Ayseguel Altay Ugur
Summary: The purpose of this paper is to demonstrate the reasonable connection between fuzzy relations and (textural) fuzzy direlations using fuzzy logical connectives, and to provide a new perspective on approximation operators by utilizing fuzzy rough set models over two universes with textures. The study investigates the serialities of fuzzy relations in terms of fuzzy logic connectives using fuzzy direlations. Various fuzzy logic systems are explored, and it is shown that the textural serialities offer alternative descriptions of the serialities of a fuzzy relation based on parameters. Moreover, the paper discusses and obtains natural fuzzifications of rough set models over two universes without considering the motivation of the semantic link with classical Pawlak rough sets. It is further demonstrated that a textural fuzzy rough set model serves as an efficient template for the basic properties of various fuzzy rough set models over two universes. The revised fuzzy rough set approximations with two universes are shown to be the natural extensions of the loose and tight approximations proposed by De Cock et al.
FUZZY SETS AND SYSTEMS
(2022)
Article
Materials Science, Ceramics
Meng Zeng, Kuixin Lin, Zhukun Zhou, Hongmei Chen, Xiaoma Tao, Yifang Ouyang, Yong Du
Summary: A series of SiC/Al composites with different contents of 3-dimensional graphene networks (3DGN) were prepared using mechanical alloying and spark plasma sintering. The effects of 3DGN on the microstructure, mechanical properties, and corrosion behaviors of SiC-3DGN/Al composites were investigated. The results showed that 3DGN substantially enhanced the synergistic strength-toughness of SiC/Al composites by improving the interface cohesion and increasing hardness, density, ductility, and corrosion resistance in NaCl solution.
CERAMICS INTERNATIONAL
(2023)
Article
Engineering, Environmental
Hongmei Chen, Fengyi Chen, Hui Chen, Hongsheng Liu, Ling Chen, Long Yu
Summary: This study investigated the thermal degradation and combustion behaviors of popular synthetic biodegradable polymers in the market. The results showed that these polymers exhibit different decomposition and combustion characteristics under different conditions, providing important data for managing city waste.
WASTE MANAGEMENT & RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Lingzhi Wang, Hongmei Chen, Bo Peng, Tianrui Li, Tengyu Yin
Summary: This study proposes a robust MFS method, which addresses the issues in multi-label feature selection using graph regularization and matrix factorization. The effectiveness of the algorithm is demonstrated through experiments.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhong Yuan, Baiyang Chen, Jia Liu, Hongmei Chen, Dezhong Peng, Peilin Li
Summary: This paper proposes an anomaly detection method based on fuzzy-rough density, which takes into account both the density and fuzziness of the samples. The method defines fuzzy-rough density, calculates attribute weights, and constructs an anomaly score. Experimental results show that the proposed method achieves better performance on three types of datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Biochemistry & Molecular Biology
Zhihao Sun, Keke Liu, Chi Chen, Daibo Chen, Zequn Peng, Ran Zhou, Ling Liu, Dengmei He, Wenjing Duan, Hongmei Chen, Chenbo Huang, Zheyan Ruan, Yingxin Zhang, Liyong Cao, Xiaodeng Zhan, Shihua Cheng, Lianping Sun
Summary: In this study, the role of OsLDDT1 gene in rice pollen wall formation and pollen development was identified. It regulates lipid biosynthesis to affect pollen cell wall formation. The mutant oslddt1 showed abnormal development of pollen, leading to complete abortion. These findings have important implications for hybrid rice breeding.
Article
Oncology
Jianxiong Li, Huaguo Liang, Wentao Xiao, Peng Wei, Hongmei Chen, Zexin Chen, Ruihui Yang, Huan Jiang, Yongli Zhang
Summary: Through whole-exome sequencing, it was found that CCDC170 gene is closely associated with the survival and prognosis of ovarian cancer. High expression of CCDC170 can significantly prolong the overall survival of ovarian cancer patients, and its expression in ovarian cancer cells is significantly lower than that in normal cells.
Article
Medicine, Research & Experimental
Wenyun Huang, Wensi Niu, Hongmei Chen, Wujun Jiang, Yanbing Fu, Xiuxiu Li, Minglei Li, Jun Hua, Chunxia Hu
Summary: We aimed to develop a nomogram to predict the risk of severe influenza in previously healthy children. A total of 1135 children infected with influenza in a retrospective cohort study were included. Risk factors were identified through logistic regression analysis and a nomogram was established. The nomogram showed good predictive ability in both the training and validation cohorts.
JOURNAL OF INTERNATIONAL MEDICAL RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Qian Gao, Zhong-Yuan Zhou, Ya-Ning He, Ming-Hui Dong, Zhao-Ning Wang, Hong-Mei Chen
Summary: This study evaluated the toxicity of BDE-47 towards RAW264.7 mouse macrophage cells and found that it caused a decrease in cell viability, an increase in apoptosis, and inhibition of phagocytosis. The mechanisms involved mitochondrial pathway-induced cell apoptosis and oxidative damage. Treatment with an antioxidant reversed the effects, while treatment with a ROS-inducer exacerbated them. These findings highlight the critical role of oxidative damage in BDE-47-induced suppression of immune function.
Article
Immunology
Man Luo, Yuanyuan Chen, Xiangyang Pan, Hongmei Chen, Lang Fan, Yi Wen
Summary: In this study, the application and effect of the probiotic EcN on the gut microbiota-metabolism-IL-22-mitochondrial damage axis in PCOS were investigated. The results showed that EcN promoted the recovery of sex hormone levels and ovarian tissue morphology, inhibited mitochondrial autophagy in PCOS mice, and improved metabolic disorders by regulating specific metabolic pathways. Clinical trials further confirmed the beneficial effects of EcN in reducing IL-22 levels and mitochondrial damage in PCOS patients.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Lei Ma, Chuan Luo, Tianrui Li, Hongmei Chen, Dun Liu
Summary: With the accumulation of interesting data in various application fields, incremental datasets are becoming more common. However, selecting informative attributes from dynamically changing datasets poses challenges. Therefore, an incremental processing mechanism is desired to update the attribute reducts efficiently. In this paper, a novel dynamic graph-based fuzzy rough attribute reduction approach is proposed to handle the maintenance of fuzzy rough attribute reduction in dynamic data, which outperforms existing methods in terms of speed and quality preservation.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Weiyi Li, Hongmei Chen, Tianrui Li, Tengyu Yin, Chuan Luo
Summary: In this paper, a robust unsupervised feature selection method, DSLRAS, is proposed, which can capture the correlation between features and the correlation between samples through latent representation learning in both feature space and data space. Adaptive graph learning is used to maintain the local geometric structure of data more accurately, and a regularization term is added to guarantee row-sparsity and achieve better results.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Xiong, Hongmei Chen, Tianrui Li, Xiaoling Yang
Summary: Multi-view graph clustering has attracted extensive research attention due to its ability to capture consistent and complementary information between views. However, multi-view data are mostly high-dimensional and may contain redundant and irrelevant features. In addition, the original data are often contaminated by noise and outliers, affecting the reliability of the learned affinity matrix. This study proposes a robust multi-view clustering model that combines low-dimensional and low-rank latent space learning, self-representation learning, and multi-view discrepancy induction fusion. Experimental results on benchmark datasets show that the proposed model outperforms state-of-the-art comparison models in terms of robustness and clustering performance.
APPLIED INTELLIGENCE
(2023)
Article
Infectious Diseases
Hongmei Chen, Mingze Tang, Lemeng Yao, Di Zhang, Yubin Zhang, Yingren Zhao, Han Xia, Tianyan Chen, Jie Zheng
Summary: mNGS is a novel nucleic acid method that can detect unknown and difficult pathogenic microorganisms. Its application in the etiological diagnosis of fever of unknown origin (FUO) is not well studied. This study aimed to comprehensively assess the value of mNGS in diagnosing FUO and investigate its impact on diagnosis time, hospitalization days, antibiotic consumption, and cost.
BMC INFECTIOUS DISEASES
(2023)
Article
Metallurgy & Metallurgical Engineering
Si-shu Wang, Qian-hao Zang, Hong-mei Chen, Yu-hang Guo, Feng-jian Shi, Di Feng
Summary: The Mg-1Gd-0.75Er-0.5Zn-0.18Zr (at.%) alloy with LPSO phase was prepared by metal mold casting and hot extrusion. The samples showed a bimodal microstructure, and the annealed samples had different fractions of equiaxed grains. The extrusion temperature affected the percentage of fine grains and the presence of LPSO phase, which influenced the tensile properties of the Mg alloys.
JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL
(2023)
Article
Cardiac & Cardiovascular Systems
Yonghua Bi, Hongmei Chen, Wenguang Zhang, Xinwei Han, Jianzhuang Ren
Summary: We describe a case where the guidewire lasso technique was used to remove an embedded esophageal self-expanding metal stent (SEMS) after a failed attempt with the removal hook. The strut of the stent fractured during the removal procedure, making it impossible to retrieve using the hook. The retained stent was successfully removed using the lasso technique with a guidewire, and the patient remained free of dysphagia after 3 months of follow-up.
ANNALS OF THORACIC AND CARDIOVASCULAR SURGERY
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
