Search Results (16)

Despite extensive research on financial literacy and financial decision-making, the scholarly literature remains conceptually fragmented, particularly regarding how behavioral biases mediate or moderate the relationship between knowledge and financial behavior. The existing literature often focuses on financial literacy or behavioral biases in isolation, limiting a systematic understanding of their interaction. This study addresses this gap by conducting a bibliometric analysis of research at the intersection of financial literacy, behavioral finance, and decision-making. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 267 peer-reviewed publications indexed in Web of Science and Scopus over the period 2010–2025 using the Bibliometrix 5.2.1 R package and VOSviewer 1.6.20 for co-occurrence, thematic clustering, and trend analysis. The results identify three interconnected research clusters: (i) socio-demographic and educational determinants of financial literacy, (ii) cognitive and behavioral biases influencing financial decision processes, and (iii) applied investment decision contexts. Overconfidence and herding dominate the literature, whereas biases such as framing, mental accounting, and intertemporal inconsistency remain comparatively underexplored. The analysis further reveals a post-2022 surge in publications, increasing internationalization, and emerging integration of digital finance and artificial intelligence themes. By systematically mapping the intellectual structure of this research domain, this study clarifies theoretical fragmentation, identifies under-researched behavioral mechanisms, and provides an evidence-based framework to guide future interdisciplinary and policy-relevant research on how financial literacy translates into financial behavior. Full article

Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this study, we developed a multi-stage natural language processing (NLP) pipeline to automate knowledge extraction, organization, and integration from the agricultural research literature into a domain-consistent crop disease knowledge graph. The model combines transformer-based sentence embeddings with variational deep clustering to extract topics, which are further refined via facet-aware relevance scoring for sentence selection to be included in the summary. Lexicon-guided named entity recognition helps in the precise identification and normalization of terms for crops, diseases, symptoms, etc. Relation extraction based on a combination of lexical, semantic, and contextual features leads to the meaningful generation of triplets for the knowledge graph. The experimental results show that the method yielded consistently good results at each stage of the knowledge extraction process. Among the combinations of embedding and deep clustering methods, SciBERT + VaDE achieved the best clustering results. The extraction of representative sentences for disease symptoms, control/treatment, and prevention obtained high F1-scores of around 0.8. The resulting knowledge graph has high node coverage and high relation completeness, as well as high precision and recall in triplet generation. The multi-stage NLP pipeline effectively converts unstructured agricultural research texts into a coherent and semantically rich knowledge graph, providing a basis for further research in crop disease analysis, knowledge retrieval, and data-driven decision support in agricultural informatics. Full article

Knowledge distillation (KD) enables the training of lightweight yet effective models, particularly in the visual domain. Meanwhile, reinforcement learning (RL) facilitates adaptive learning through environment-driven interactions, addressing the limitations of KD in handling dynamic and complex tasks. We propose a novel two-stage framework integrating Knowledge Distillation with Reinforcement Learning (KDRL) to enhance model adaptability to complex data distributions, such as remote sensing and medical imaging. In the first stage, supervised fine-tuning guides the student model using logit and feature-based distillation. The second stage refines the model via RL, leveraging confidence-based and cluster alignment rewards while dynamically reducing reliance on task loss. By combining the strengths of supervised knowledge distillation and reinforcement learning, KDRL provides a comprehensive approach to address the dual challenges of model efficiency and domain heterogeneity. A key innovation is the introduction of auxiliary layers within the student encoder to evaluate and reward the alignment of the characteristics with the teacher’s cluster centers, promoting robust feature learning. Our framework demonstrates superior performance and computational efficiency across diverse tasks, establishing a scalable design for efficient model training. Across remote sensing benchmarks, KDRL boosts the lightweight CLIP/ViT-B-32 student to 69.51% zero-shot accuracy on AID and 80.08% on RESISC45; achieves state-of-the-art cross-modal retrieval on RSITMD with 67.44% (I→T) and 74.76% (T→I) at R@10; and improves DIOR-RSVG visual-grounding precision to 64.21% at Pr@0.9. These gains matter in real deployments by reducing missed targets and speeding analyst search on resource-constrained platforms. Full article

Aim: With the intensification of population aging, the public health challenges posed by multimorbidity have become increasingly severe. This study employs bibliometric analysis to elucidate research hotspots and trends in the field of multimorbidity against the backdrop of global aging. The immediate aim is to systematically map the intellectual landscape and evolving patterns in multimorbidity research. The ultimate long-term aim is to provide a scientific basis for optimizing chronic disease prevention systems and guiding future research directions. Methods: The study adopted the descriptive research method and employed a bibliometric approach, analyzing 8129 publications related to multimorbidity from the Web of Science Core Collection. Using CiteSpace, we constructed and visualized several knowledge structures, including collaboration networks, keyword co-occurrence networks, burst detection maps, and co-citation networks within the multimorbidity research domain. Results: The analysis included 8129 articles from 2004 to 2024, published across 1042 journals, with contributions from 740 countries/regions, 33,931 institutions, and 40,788 authors. The five most frequently occurring keywords were prevalence, health, older adult, mortality, and risk. The top five contributing countries globally were the United States, the United Kingdom, Germany, China, and Spain. Five pivotal research trajectories delineate the intellectual architecture of this field: ① Evolution of Disease Cluster Management: Initial investigations (2013–2014) prioritized disease cluster coordination within general practice settings, particularly cardiovascular comorbidity management through primary care protocols and self-management strategies. ② Paradigm Shifts in Health Impact Assessment: Multimorbidity outcome research demonstrated sequential transitions—from physical disability evaluation (2013) to mental health consequences like depression (2016), culminating in current emphasis on holistic health indicators including frailty syndromes (2015–2019). ③ Expansion of Risk Factor Exploration: Analytical frameworks evolved from singular physical activity metrics (2014) toward comprehensive lifestyle-related determinants encompassing behavioral and environmental dimensions (2021). ④ Emergence of Polypharmacy Scholarship: Medication optimization studies emerged as a distinct research stream since 2016, addressing therapeutic complexities in multimorbidity management. ⑤ Frontier Investigations: Cutting-edge directions (2019–2021) feature cardiometabolic multimorbidity patterns and their dementia correlations, signaling novel interdisciplinary interfaces. Conclusions: The prevalence of multimorbidity is on the rise globally, particularly in older populations. Therefore, it is essential to prioritize the prevention of cardiometabolic conditions in older adults and to provide them with appropriate and effective health services, including disease risk monitoring and community-based chronic disease care. Full article

Taxonomies provide essential hierarchical structures for classifying information, enabling effective retrieval and knowledge organization in diverse domains such as e-commerce, academic research, and web search. Traditional taxonomy construction, heavily reliant on manual curation by domain experts, faces significant challenges in scalability, cost, and consistency when dealing with the exponential growth of digital data. Recent advancements in Large Language Models (LLMs) and Natural Language Processing (NLP) present powerful opportunities for automating this complex process. This paper explores the potential of LLMs for automated taxonomy generation, focusing on methodologies incorporating semantic embedding generation, keyword extraction, and machine learning clustering algorithms. We specifically investigate and conduct a comparative analysis of two primary LLM-based approaches using a dataset of eBay product descriptions. The first approach involves fine-tuning a pre-trained LLM using structured hierarchical data derived from chain-of-layer clustering outputs. The second employs prompt-engineering techniques to guide LLMs in generating context-aware hierarchical taxonomies based on clustered keywords without explicit model retraining. Both methodologies are evaluated for their efficacy in constructing organized multi-level hierarchical taxonomies. Evaluation using semantic similarity metrics (BERTScore and Cosine Similarity) against a ground truth reveals that the fine-tuning approach yields higher overall accuracy and consistency (BERTScore F1: 70.91%; Cosine Similarity: 66.40%) compared to the prompt-engineering approach (BERTScore F1: 61.66%; Cosine Similarity: 60.34%). We delve into the inherent trade-offs between these methods concerning semantic fidelity, computational resource requirements, result stability, and scalability. Finally, we outline potential directions for future research aimed at refining LLM-based taxonomy construction systems to handle large dynamic datasets with enhanced accuracy, robustness, and granularity. Full article

The graded management of structured sensitive data has become a key challenge in data security governance, particularly amid digital transformation in sectors such as government, finance, and healthcare. The existing methods suffer from limited generalization, low efficiency, and reliance on static rules. This paper proposes PPM-SACG, a privacy preference matrix-based model for sensitive attribute classification and grading. The model adopts a three-stage architecture: (1) composite sensitivity metrics are derived by integrating information entropy and group privacy preferences; (2) domain knowledge-guided clustering and association rule mining improve classification accuracy; and (3) mutual information-based hierarchical clustering enables dynamic grouping and grading, incorporating high-sensitivity isolation. Experiments using real-world vehicle management data (50 attributes, 3000 records) and user privacy surveys verify the method’s effectiveness. Compared with existing approaches, PPM-SACG doubles computational efficiency and supports scenario-aware deployment, offering enhanced compliance and practicality for structured data governance. Full article

Due to access to the source data during the transfer phase, conventional domain adaptation works have recently raised safety and privacy concerns. More research attention thus shifts to a more practical setting known as source-data-free domain adaptation (SFDA). The new challenge is how to obtain reliable semantic supervision in the absence of source domain training data and the labels on the target domain. To that end, in this work, we introduce a novel Gradual Geometry-Guided Knowledge Distillation (G2KD) approach for SFDA. Specifically, to address the lack of supervision, we used local geometry of data to construct a more credible probability distribution over the potential categories, termed geometry-guided knowledge. Then, knowledge distillation was adopted to integrate this extra information for boosting the adaptation. More specifically, first, we constructed a neighborhood geometry for any target data using a similarity comparison on the whole target dataset. Second, based on pre-obtained semantic estimation by clustering, we mined soft semantic representations expressing the geometry-guided knowledge by semantic fusion. Third, using the soften labels, we performed knowledge distillation regulated by the new objective. Considering the unsupervised setting of SFDA, in addition to the distillation loss and student loss, we introduced a mixed entropy regulator that minimized the entropy of individual data as well as maximized the mutual entropy with augmentation data to utilize neighbor relation. Our contribution is that, through local geometry discovery with semantic representation and self-knowledge distillation, the semantic information hidden in the local structures is transformed to effective semantic self-supervision. Also, our knowledge distillation works in a gradual way that is helpful to capture the dynamic variations in the local geometry, mitigating the previous guidance degradation and deviation at the same time. Extensive experiments on five challenging benchmarks confirmed the state-of-the-art performance of our method. Full article

Nickel (Ni) resources are critical for the development of modern industry. This study investigates the knowledge structure and frontier evolution of Ni deposit research by constructing a domain-specific knowledge graph using bibliometric analysis and semantic extraction from 7090 publications (1966–2024) sourced from the Web of Science Core Collection. The results show that Ni research has three distinct phases: slow growth (1966–1990), early growth (1991–2010), and rapid expansion (2011–present). The collaborative network of institutions in which articles are published forms three regional clusters centered on China, Russia, and Australia. Keyword burst analysis identifies emerging frontiers, including sulfur isotopes, pyrite geochemistry, and LA-ICP-MS applications. Temporal keyword analysis identifies “platinum-group minerals”, “ore-forming fluids”, “isotopic analysis”, and “Eastern Tianshan” interactions as five pivotal research frontiers in nickel deposit studies. The knowledge graph framework demonstrates significant potential in mitigating data fragmentation, enhancing interdisciplinary data accessibility, and guiding future exploration strategies. This study shows the important role of knowledge maps in optimizing the study of nickel deposits. Full article

As construction projects become increasingly complex, modular integrated construction (MiC) has emerged as a pivotal solution, driving integrated development in complex projects. However, the reliance on prefabricated modules underscores the crucial role of supply chain management (SCM) in MiC, necessitating strategic planning and operational control. This study aimed to use bibliometric analysis to map the SCM knowledge domain within MiC. Through the use of keywords related to “supply chain” and “MiC”, 196 relevant papers were extracted from the Web of Science database. These papers were subjected to co-citation analysis, keyword co-occurrence analysis, and time span analysis to elucidate the historical evolution, multidisciplinary domains, and future directions in planning and control within SCM-MiC. The research identified two milestones in SCM-MiC’s historical trajectory, enhancing our understanding of its foundations. Moreover, 11 clusters were identified, illustrating the multidisciplinary nature of SCM-MiC. Dividing the literature into seven stages of the supply chain, the research outlined four research directions aligned with project complexity and technological development, highlighting current hotspots and gaps of the strategic planning and control. These directions bridge the construction management and information technology domains, guiding future SCM-MiC research within complex project management. Full article

One of the main limitations of traditional neural-network-based classifiers is the assumption that all query data are well represented within their training set. Unfortunately, in real-life scenarios, this is often not the case, and unknown class data may appear during testing, which drastically weakens the robustness of the algorithms. For this type of problem, open-set recognition (OSR) proposes a new approach where it is assumed that the world knowledge of algorithms is incomplete, so they must be prepared to detect and reject objects of unknown classes. However, the goal of this approach does not include the detection of new classes hidden within the rejected instances, which would be beneficial to increase the model’s knowledge and classification capability, even after training. This paper proposes an OSR strategy with an extension for new class discovery aimed at vehicle make and model recognition. We use a neuroevolution technique and the contrastive loss function to design a domain-specific CNN that generates a consistent distribution of feature vectors belonging to the same class within the embedded space in terms of cosine similarity, maintaining this behavior in unknown classes, which serves as the main guide for a probabilistic model and a clustering algorithm to simultaneously detect objects of new classes and discover their classes. The results show that the presented strategy works effectively to address the VMMR problem as an OSR problem and furthermore is able to simultaneously recognize the new classes hidden within the rejected objects. OSR is focused on demonstrating its effectiveness with benchmark databases that are not domain-specific. VMMR is focused on improving its classification accuracy; however, since it is a real-world recognition problem, it should have strategies to deal with unknown data, which has not been extensively addressed and, to the best of our knowledge, has never been considered from an OSR perspective, so this work also contributes as a benchmark for future domain-specific OSR. Full article

In the current challenging and competitive dynamic business world today, automotive companies have been rapidly developing and promoting autonomous vehicles (AVs), which aim to reduce crashes, energy consumption, pollution, and congestion and increase transport accessibility. To ensure the successful adoption of AVs, an increasing number of studies have been conducted to understand public acceptance. This paper used the bibliometric technique to understand the distribution, emerging trend, and the research cluster in the context of AV technology acceptance through knowledge mapping. The Web of Science database was used to retrieve 401 scientific articles from 2000 to June 2022. The findings reported that the previous studies mainly focused on the research clusters related to the domains of attitude, trust, technology, impact, and models. Finally, this study added to the existing body of literature by providing the current knowledge landscape to guide the future research. Full article

To solve the problem of inadequate feature extraction by the model due to factors such as occlusion and illumination in person re-identification tasks, this paper proposed a model with a joint cross-consistency learning and multi-feature fusion person re-identification. The attention mechanism and the mixed pooling module were first embedded in the residual network so that the model adaptively focuses on the more valid information in the person images. Secondly, the dataset was randomly divided into two categories according to the camera perspective, and a feature classifier was trained for the two types of datasets respectively. Then, two classifiers with specific knowledge were used to guide the model to extract features unrelated to the camera perspective for the two types of datasets so that the obtained image features were endowed with domain invariance by the model, and the differences in the perspective, attitude, background, and other related information of different images were alleviated. Then, the multi-level features were fused through the feature pyramid to concern the more critical information of the image. Finally, a combination of Cosine Softmax loss, triplet loss, and cluster center loss was proposed to train the model to address the differences of multiple losses in the optimization space. The first accuracy of the proposed model reached 95.9% and 89.7% on the datasets Market-1501 and DukeMTMC-reID, respectively. The results indicated that the proposed model has good feature extraction capability. Full article

Objectives: This study aimed to visualize the evidence in the global research on health education to better improve the nation’s health literacy and to guide future research. Method: We searched the Web of Science (Core Collection) electronic databases. The search strategies: topic: (“Health Education” OR “Education, Health” OR “Community Health Education” OR “Education, Community Health” OR “Health Education, Community”) AND document: (Article) AND language:(English). Articles of evidence from January 2011 to December 2021 with those words in the title or abstract or keywords will be included in this review. We used the Citespace 5.6.R5 (64-bit) to investigate and determine the thematic patterns, and emerging trends of the knowledge domain, and presented a narrative account of the findings. Result: We analyzed 10,273 eligible articles. It showed that BMC Public Health displays the most prolific journals. Author MARCO PAHOR is highlighted in health education. The University of Sydney has published the most studies about health education. The USA plays an important role in these studies. Specifically, the visualization shows several hotspots: disease prevalence surveys and a specific population of knowledge, attitude and practice surveys, health intervention, chronic and non-communicable management, youth-health action, sexual and reproductive health, and physical activity promotion. Furthermore, document co-citation analysis indicated that there are 10 main clusters, which means the research front in health education. Meanwhile, by the citation detected, COVID-19, has achieved universal health coverage in related studies, however, public health education and the health workforce might be more popular in the coming years. Conclusion: Health education is an effective measure to shift the concept of public health and improve healthy living standards. The present study facilitates an extensive understanding of the basic knowledge and research frontiers that are pivotal for the developmental process of health education and allows scholars to visualize the identification modes and tendencies. Full article

Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability. Full article

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented. Full article