Use Of Knowledge Research Articles

Neocolonialism and science diplomacy: lessons learned from the field and the way forward

To level the playing field in the production of global knowledge, we need to understand the practical implications of colonial heritage and how it has disproportionately affected scientific discourse and the generation and utilization of scientific knowledge from and about the Global South. This article explores how research practitioners can level the playing field. We must think about how we can collectively change the narrative so that every emerging scientist from the Global South can flourish and have an equal opportunity to conduct research that is meaningful to them and their societies. We argue the time has come for innovative and flexible models allowing science diplomacy to integrate features of reflexive and inclusive governance in its very core structures.

Multi-state sequential Boolean networks: An analysis of its combinatorial dynamics

Multi-state Boolean networks model the behavior of interrelated (non-binary) Boolean entities over time. These networks can be used in several contexts, and extend the usual (simpler) binary-state Boolean networks. Leveraging the established knowledge of binary-state Boolean networks, we explore the dynamics of sequential multi-state Boolean networks, where their entities or nodes have states which belong to a (general) Boolean algebra with more than two elements and whose updates are determined in an specific order. This study contributes to the advancement of multi-state Boolean networks, enriching their theoretical knowledge and practical utility.

The Development of Knowledge-Based Economies Through Creation, Dissemination and Use of Knowledge, and Information in Native Languages

"If you talk to a man in a language he understands, that goes to his head. If you talk to him in his language, that goes to his heart"- Nelson Mandela.Conveying your messages in a language your audience both understands and resonates with is of utmost importance to establish an emotional connection. Language proficiency is crucial in determining how well the information or knowledge is communicated which subsequently influences comprehension of the same by the audience. By contrast, when knowledge is synthesized and shared in a language unfamiliar to a community, the extent to which that knowledge is utilized depends largely on that specific language proficiency of the community.Knowledge-based economy is characterized by the production, distribution and utilization of knowledge and information. In such systems, education, innovation and skill development are fundamental to economic growth. As highlighted above, the availability of knowledge and information in one's native language serves as a significant facilitator for the development of a system. Thus, the promotion of education in local languages set the stage for the establishment of environment for the citizens to fully involve in knowledge systems, apply the acquired skills and contribute to innovation. This leads to the development of a highly-skilled workforce, advancement of technology as well as the formation of competitive industries.

Colonial Recognition? The Appropriation of Dutch Land and Population Registers as Legal Documents in Eighteenth-Century Sri Lanka

Abstract This paper considers the utilisation, appropriation, and renegotiation of colonial knowledge in the form of land and population registers by local litigants in eighteenth-century Dutch colonial Sri Lanka. Using a database compiled from thirty-three civil court cases held before the Landraad rural council of Colombo, I highlight how Lankan litigants frequently used the colonial thombo registers as evidence to have their property recognised. Moreover, I show that these registers were not just utilised but also altered through this process, particularly through the promotion of alternative knowledge in the form of local witness testimonies and ola palm leaf documents during court cases. I subsequently argue that we should reconsider how we view colonial knowledge. Rather than a static, top-down view from a foreign bureaucracy on a colonised society, this knowledge could be appropriated and even altered through the acts of local agents, in turn changing what was known by the colonial state and thus creating a “looping effect” of knowledge production.

Managing knowledge and sustainability in resource constrained disaster environments

AbstractOrganizations use both tangible and intangible resources for their existence to achieve sustainable development goals. Organizational knowledge mainly comprises of tacit knowledge held by employees. It becomes more critical when the situations are unpredictable, for example in rescue/disaster projects involving human lives at risk. Tacit knowledge utilization has been understudied particularly in such environments bounded by resource constraint and sustainability issues. This qualitative research attempts to fill this gap by collecting data from the knowledge workers in a large public sector non‐profit rescue organization. The study reveals various individual level, group level, organizational level, community level, and situational level factors, which impact the tacit knowledge utilization of rescue workers to properly execute emergency life threatening rescue operations and sustainability issues.

Prevalence of economic violence among the geriatric population and their knowledge and utilization of social security schemes in Chengalpattu district, Tamil Nadu

ABSTRACT Context: Economic independence is an important factor in the prevention of violence among the elderly. Optimal utilization of various social security schemes in India helps them in achieving financial independence and proper utilization of health care services. However, there is a knowledge and service utilization gap among the elderly in our population. Aim and Objectives: To assess the prevalence of economic violence and awareness and utilization of social security schemes among the geriatric population. Settings and Design: Community-based cross-sectional study conducted in rural and urban field practice areas of the tertiary care center. Methods and Material: A simple random sampling method was adopted. The data were collected using a pilot-tested semistructured questionnaire among 406 participants from three selected villages and wards of Venmalagaram and Maduranthakam during January to July 2023. Statistical Analysis Used: Frequencies and proportions were used for categorical variables, and Chi-square test was applied to test the level of significance. P <0.05 was considered statistically significant. Results: The prevalence of economic violence was found to be 12.1%. Among them, neglect was seen in a major proportion (61.2%). Awareness about the old age pension was seen in 70.7%, followed by Annapurna scheme (17%) and PMVVY (4.9%). The utilization of these schemes was found to be 34.0%, 11.6%, and 2.7%, respectively. Conclusions: The prevalence of economic violence among the geriatric population was in the form of neglect and property misuse. The knowledge and utilization of social security schemes were poor among the elderly.

A “Wonderful” Reference Dataset of Mira Variables

The conditions in Mira variable atmospheres make them wonderful laboratories to study a variety of stellar physics such as molecule–grain formation, dust production, shock chemistry, stellar winds, mass loss, opacity-driven pulsation, and shocks. We were awarded an NSF grant to analyze over a decade of synoptic observations from the Palomar Testbed Interferometer (PTI) of 106 Miras to curate a Mira Reference Dataset. The Miras included in this dataset include M-types, S-types, and C-types, and span a wide range of pulsation periods. PTI measured K-band angular sizes that when combined with a distance allow us to directly determine fundamental stellar parameters such as effective temperature, radial size, and bolometric flux. Supplementing observations with interferometric measurements of the stars opens the Mira laboratory to a wealth of different experiments. We provide two case studies to serve as examples of the power of the Mira Reference Dataset. The first case study describes combining PTI measurements with Spitzer IRS spectra of M-type Miras, which allowed us to fully characterize CO2 gas in their atmospheres. The second case study examines how PTI narrow-band data can be used to study phase-dependent pulsation effects on the stellar atmosphere. We provide a list of all the Miras (with coordinates) included in the set for anyone who would like to add them to their observing programs. All the data we produce and collate for this Mira Reference Dataset will be hosted and curated on a website open to the public so that other researchers and citizen scientists can participate in expanding the utility and body of knowledge on this set of “wonderful” stars.

Traditional African Vegetables Knowledge Translation: A Scoping Review

Traditional African vegetables (TAVs) are an important component of an ecologically sustainable approach to addressing food and nutrition security in Africa. This review aims to identify the scope of the knowledge translation literature that exists across multiple aspects of the TAV value chain and to identify gaps in knowledge translation research. A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was conducted. The Population, Concept, and Context (PCC) framework was used to guide the development of inclusion and exclusion criteria for this review. The Web of Science, Scopus, Science Direct, and ABI/Inform databases were searched to identify the relevant studies which were then screened using the Covidence 2024 online software to select studies and extract information for analysis and reporting. A total of 182 studies/articles were selected and examined. The most significant knowledge documented about TAVs pertains to their vast genetic diversity contributing to their rich nutritional and medicinal properties, their ability to withstand harsh environmental conditions such as drought, pests, and diseases, as well as the effects of various post-handling techniques on the nutrient content of TAVs, with the production phase of the TAV value chain being the most studied. This review found that, although a breadth of knowledge about TAVs has been generated, there is still little documentation about transferring and utilizing this knowledge across the TAV value chain. This review identified that more research is needed to document the strategies and practices for the transfer and utilization of knowledge to ensure that it is utilized to enhance nutritional outcomes across Africa.

From research to resources: developing a case-based learning curriculum for navigating clinical uncertainty

Purpose This study aimed at rethinking ways in which educators from different fields can collaborate to respond to the rapidly evolving demands of health professions education (HPE). The goal was to investigate how a research-to-resources approach can be applied to engage in knowledge translation (KT) of research findings for the benefit of introducing medical students to uncertainty in the clinical learning environment. Design/methodology/approach An interdisciplinary team of medical educators, human resource development (HRD) scholars and emergency medicine fellows engaged in iterative cycles of action research (AR) to develop, pilot and refine case-based learning resources on clinical uncertainty. The team leveraged prior research on physicians’ decision-making during COVID-19, experimented with generative AI tools, and collected feedback from medical students to guide resource development. Findings The findings of this study are twofold. On the one hand, the authors reflect on the lessons learned of developing case-based learning with the help of generative AI. While student feedback indicated that the case helped normalize and process experiences with uncertainty, key challenges included adapting research data to create relevant, sustainable learning resources and designing effective discussion prompts. On the other hand, the authors provide insights into the opportunities and challenges of our interdisciplinary collaboration. The authors show that knowledge utilization is not simple, but complex, and that more work needs to be done to effectively disseminate resources as part of the desired uncertainty curriculum. Practical implications This study attempts to apply a KT framework for bridging the research-practice gap in HPE through interdisciplinary collaboration and AR. It provides lessons learned for developing case-based curricula on complex topics like uncertainty. The findings highlight the need for adaptive KT processes when dealing with rapidly evolving healthcare contexts. Originality/value This paper offers a novel example of research-to-resource KT in medical education, integrating perspectives from HRD and leveraging emerging technologies. It contributes to understanding how interdisciplinary teams can collaborate to create timely, evidence-based educational resources for navigating uncertainty in professional practice. The study also provides insights into the challenges and opportunities of translating complex research findings into practical learning tools to tackle real-world challenges in HPE.

Knowledge Organization Capability and Competitiveness of Chartered Public Universities in Kenya

Purpose: Knowledge has increasingly become one of the most critical determining factors for organizational competitiveness. In order to acquire and sustain a competitive advantage, it is vital for an organization to develop effective strategies that foster knowledge creation, transfer and integration in the performance of the organization. Knowledge organization refers to activities such as document description, indexing, and classification that provide systems of representation and order for information and knowledge materials. The purpose of this study was to establish the relationship between knowledge organization capability and competitiveness of chartered public universities in Kenya. Methodology: The study adopted a descriptive design. The population of the research consisted of 31 chartered public universities authorized to operate in Kenya as at December 2020. The study adopted a census methodology while purposeful and convenient sampling was adopted to select respondents amongst the middle level management staff. The study used both secondary and primary data. A self-administered questionnaire was used as the primary data collection instrument. Out of 155 questionnaires that were sent out, 123 were filled and returned. Statistical Package for Social Studies (SPSS) was used for data analysis. The study used regression model to estimate the level of significance which yielded a positive and significant influence of knowledge organization capability on competitiveness of chartered public universities in Kenya. Findings: The findings indicate that organizational initiatives such as effective knowledge collaboration and utilization, deployment of information technology systems that support knowledge organisation and easy access to information and data contribute to a healthy environment conducive for knowledge management. Owing to the foregoing, the study concluded that knowledge organization capability had a significant positive impact on competitiveness of chartered public universities. Unique Contribution to Theory, Practice and Policy: The study recommended that public universities needed to embrace knowledge organization capability in order to obtain sustainable competitive advantage.

Health literacy in the evolving era of personalised medicine and digital health

Abstract Health literacy is pivotal for advancing public health, empowering individuals to grasp health information and make informed decisions. However, health literacy levels are often influenced by social and economic factors, with lower levels correlating with reduced disease-specific knowledge and underutilization of preventive health services. As healthcare technology rapidly evolves, particularly with the advent of personalised medicine (PM) and novel genetic testing methods, there’s an increasing demand for heightened health literacy to effectively navigate these transformative innovations and optimize health outcomes. The European ExACT project aims to assess the understanding of PM and related technologies across the EU. In this context, through a survey conducted in several European countries, including Italy, France, Spain, Poland, the Netherlands, Romania, Hungary, and Germany, demographic data and attitudes towards PM, genetic testing, health portals, and big data were gathered. Logistic regression analysis identified factors influencing knowledge and attitudes. Among 6581 respondents, the results highlight a broad awareness of genetic testing (81%) and health portals (54%) yet a lesser familiarity with PM (48%) and big data (37%). Education level, age, and gender influenced awareness, with Hungary showing significant awareness across all topics (OR 2.09 95%CI 1.46-2.98), Spain excelling in big data (OR 1.88 95%CI 1.55-2.27), and Poland leading in genetic testing knowledge (OR 2.68 95%CI 1.82-3.96). Differences between countries may indicate successful policies and factors that can be replicated. Overall, the study emphasizes the importance of enhancing health literacy and sheds light on the predicting role of sociodemographic factors. It underscores the significance of bolstering health literacy to effectively navigate the advancements in PM and digital health, improving health outcomes in light of these evolving healthcare landscapes. Key messages • In Europe, awareness of personalised medicine, health portals and big data is lacking. Educational initiatives are crucial for effective navigation of innovations, considering regional differences. • Social and economic factors affect health literacy levels, impacting disease-specific knowledge and healthcare utilization.

CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher.

Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM's technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results.

A Low Transformed Tubal Rank Tensor Model Using a Spatial-Tubal Constraint for Sample Clustering with Cancer Multi-omics Data

Background: Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed. Objective: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer. Method: In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively. Results: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. result: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. Conclusion: The LTTRT method achieves precise segmentation of different types of cancer samples.

The citizens have participated – what now? An action research study of factors impacting the use of participatory citizen knowledge in planning processes

ABSTRACT Citizen participation is embedded into planning practice and policy, yet it remains unclear how the results of participation are shared in planning organisations and utilised to inform planning outcomes. This article examines barriers and enablers for systematic gathering, sharing and utilisation of participatory citizen knowledge and analyses the collaborative development of knowledge-sharing practices in two Finnish municipalities. The article focuses specifically on the collection and dissemination of local, experiential knowledge from two municipality-wide Public Participation GIS surveys. The study adopts an action research approach to deepen the understanding of planners' complex relationships with participatory citizen knowledge: how they gather and share it, how it informs their work and how they develop their everyday practice. The results suggest that a variety of practical, technical and cultural factors influence whether planners access and utilise gathered citizen knowledge. Planners' backgrounds, skills and personalities also influence how they perceive and utilise citizen knowledge. When provided with accessible and representative data about citizens' behaviours and preferences, adequate resources and a sense of agency through supportive organisation culture, officials from different sectors were eager to develop the use of participatory knowledge. These emergent practices can be used to inform wider iterative development to meet practitioners' needs.

Pathway to Trauma-Informed Rehabilitation Counseling: Vocational Rehabilitation Counselors’ Training Experiences and Use of Trauma-Relevant Knowledge in Daily Practice

The compelling evidence regarding the prevalence and profound impacts of traumatic experiences on individuals with disabilities underscores the need to establish trauma-informed service environments within vocational rehabilitation (VR). In an initial endeavor towards cultivating trauma-informed service, this study investigates the VR counselors’ professional training experiences in the topical areas of trauma. Further, the applicability of the Technology Acceptance Model (TAM 2) is examined in elucidating the utilization of trauma knowledge by VR counselors in their practices. A needs assessment focusing on trauma training was distributed to 169 VR counselors to measure their trauma-related training history and utilization of trauma knowledge in practices. Path analysis was employed to examine the relationship between training experience and the actual use of trauma knowledge in counseling based on the TAM 2 framework. Participants reported having 9.71 hours of post-graduate training. Path analysis suggested adding three additional paths to the original research model, explaining how post-graduate training leads to the use of trauma knowledge in VR service through perceived ease of use, usefulness, attitude toward using, and behavioral intention. The profession of rehabilitation counseling needs to incorporate more trauma-related training into its practice, education, and research to better serve individuals with disabilities and improve the effectiveness of services.

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

Animal breeding and feeding tools may close human nutrition gaps

The last century has witnessed many innovations in agriculture and food technologies that have ensured the production of sufficient quantities of good hygienic food. Animal scientists have contributed substantially to efficient breeding and feeding practices by adapting animals for faster growth and improving feed efficiency and utilization. Breeding goals and feeding recommendations have been proposed with a primary focus on profitability to promote significant changes in the macronutrient content, i.e., animal body protein and fat reduction. However, improving the nutritional quality of meat was not included in the profit assessment. Increasing the lean meat fraction is consistent with the goal of public dietary guidelines for human nutrition proposed in 1980, emphasizing the importance of reducing the consumption of animal fat, particularly saturated fat. The application of breeding and feeding tools to modify and improve the fatty acid composition has been partly implemented in pigs and broiler chickens to supplement the dietary recommendations for humans. The health benefits of lean “red meat” have been questioned in recent years, with dietary guidelines and upper limits being introduced for human intake. Animal breeding indirectly reduces the total and heme iron (the redness generator) content in meat, due to covariation with priority breeding goals. Two micronutrients play important roles in the human diet and are derived largely from meat (selenium) and milk (iodine) if the amount provided and absorbed is sufficient and predictable. The iodine content can be highly dependent on the use of novel (more sustainable) feeds. The micronutrients discussed in this study (Fe, Se, I, and vitamin D) highlight opportunities for the utilization of breeding and feeding knowledge to adjust their levels to procure meat with a high nutrient density. The gaps in micronutrient levels in humans must be addressed by navigating within approved animal feeding levels. Animal scientists must recognize the nutritional impact of breeding and feeding and advertise them. In addition, human nutritionists must acknowledge the existing and potential changes in animal production to meet the dietary guidelines. Sustainable food production within the “One Health” concept can only be achieved through cooperation.

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-shot Learning Paradigm

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot learning (ZS), which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine's abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

How can we translate research papers into legislative drafts? A methodological framework of the Translational Legal Approach to Knowledge Utilisation

ABSTRACT One important factor in knowledge utilisation in policy is adapting research papers to better fit policy-makers’ needs. Translating research papers from a variety of genres and types, books to journal articles, into legislative drafts can be understood as a form of adaptation. This was named as the Translational Legal Approach to Knowledge Utilisation (TLA). This article addresses the methodological need to construct a framework for TLA. It proposes a methodological framework which includes definitions, translation heuristic, guiding problems, stages and translation procedures, various translation modes (legislative techniques with different levels of specificity) and various translation forms (legislative genres with different legal status, functions, and other characteristics). By looking at cases from Polish and EU laws and standards, this article proposes a methodology for researchers to better align their research conclusions and recommendations to policy-makers’ needs.

Dual-stream dynamic graph structure network for document-level relation extraction

Extracting structured information from unstructured text is crucial for knowledge management and utilization, which is the goal of document-level relation extraction. Existing graph-based methods face issues with information confusion and integration, limiting the reasoning capabilities of the model. To tackle this problem, a dual-stream dynamic graph structural network is proposed to model documents from various perspectives. Leveraging the richness of document information, a static document heterogeneous graph is constructed. A dynamic heterogeneous document graph is then induced based on this foundation to facilitate global information aggregation for entity representation learning. Additionally, the static document graph is decomposed into multi-level static semantic graphs, and multi-layer dynamic semantic graphs are further induced, explicitly segregating information from different levels. Information from different streams is effectively integrated via an information integrator. To mitigate the interference of noise during the reasoning process, a noise regularization mechanism is also designed. The experimental results on three extensively utilized publicly accessible datasets for document-level relation extraction demonstrate that our model achieves F1 scores of 62.56%, 71.1%, and 86.9% on the DocRED, CDR, and GDA datasets, respectively, significantly outperforming the baselines. Further analysis also demonstrates the effectiveness of the model in multi-entity scenarios.