Integration Of Knowledge Research Articles

Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions

Large language models (LLMs), like ChatGPT, Google’s Bard, and Anthropic’s Claude, showcase remarkable natural language processing capabilities. Evaluating their proficiency in specialized domains such as neurophysiology is crucial in understanding their utility in research, education, and clinical applications. This study aims to assess and compare the effectiveness of Large Language Models (LLMs) in answering neurophysiology questions in both English and Persian (Farsi) covering a range of topics and cognitive levels. Twenty questions covering four topics (general, sensory system, motor system, and integrative) and two cognitive levels (lower-order and higher-order) were posed to the LLMs. Physiologists scored the essay-style answers on a scale of 0–5 points. Statistical analysis compared the scores across different levels such as model, language, topic, and cognitive levels. Performing qualitative analysis identified reasoning gaps. In general, the models demonstrated good performance (mean score = 3.87/5), with no significant difference between language or cognitive levels. The performance was the strongest in the motor system (mean = 4.41) while the weakest was observed in integrative topics (mean = 3.35). Detailed qualitative analysis uncovered deficiencies in reasoning, discerning priorities, and knowledge integrating. This study offers valuable insights into LLMs’ capabilities and limitations in the field of neurophysiology. The models demonstrate proficiency in general questions but face challenges in advanced reasoning and knowledge integration. Targeted training could address gaps in knowledge and causal reasoning. As LLMs evolve, rigorous domain-specific assessments will be crucial for evaluating advancements in their performance.

Nesting habitat characteristics and predation patterns in the European pond turtle Emys orbicularis (L., 1758): Implications for management and conservation measures

Integrating knowledge regarding habitat characteristics and animal behaviour into conservation programs has the potential to impact the results of management and conservation efforts. This study aimed to explore the relationship between spatial and environmental data on the nesting activity of the European pond turtle, Emys orbicularis. Additionally, it sought to analyse the abundance, activity pattern, and behaviour of potential nest predators of E. orbicularis. Natural predated nests of the European pond turtle were utilized to characterise nesting habitat features, while artificial nests and camera traps were deployed to identify potential predators and their behaviour. Artificial nests and camera traps were established in areas with evidence of track detection or historical observations indicating past instances of nest predation. The nest site distribution of the European pond turtle showed a positive relationship with north-south oriented canals, the presence of vegetative components on embankments (shrubs, grass, emergent vegetation), finer-grained soil components (silt and clay), and soil moisture. Camera trapping of artificial nests revealed mammals (Meles meles and Vulpes vulpes) and a bird (Pica pica) as predators engaging in digging and destroying behaviour. P. pica detection was notably higher in mowed vegetation areas. Results indicate that natural nest distribution and predation on artificial nests may be influenced by their distribution concerning human-controlled vegetation and foraging activities of common nest predators. Protecting nesting sites in predator-frequented habitats, combined with landscape management targeting vegetation control along embankments, could mitigate nest predation and enhance hatchling recruitment.

Hydrogeomorphological approach for flood analyses at high- detailed scale: Narrow rivers with broad complex alluvial plains

Fluvial flood risk management requires precise knowledge of flooding processes; thus, there is a need to carry out a detailed hydrological, hydraulic, and geomorphologic analysis to understand floods and the factors that influence them. Although the integration of geomorphological methods and hydrological-hydraulic modelling is known to be crucial for flood risk analysis, it remains a challenge to apply these approaches to the understanding of floods in small and medium-sized catchments, focusing on complex fluvial systems that have been highly modified. In this work, a hydrogeomorphological methodology was developed to analyse fluvial floods in narrow rivers with broad complex alluvial plains. The procedure comprises different phases that combine hydrologic-hydraulic modelling and a geomorphological break-down. Critically, this allows for a detailed analysis of existing relationships among erosional and depositional fluvial landforms and hydraulic parameters, comprising geometric sections, discharges, depths, flood extents and, therefore, flood hazards. The approach involves the analysis of specific flood events beforehand and the study of fluvial floods associated with different probabilities of occurrence afterwards. The information employed in the present study incorporates high- detailed inputs comprising hourly time series of hydrometeorological data, LiDAR data to extract high-precision terrain information, fluvial geomorphic maps and flood event extents based on Sentinel-2 imagery. We demonstrate this methodology in the Carrión River, located in the province of Palencia, Spain, whose characteristics include a wide complex alluvial plain, reaches highly modified by anthropic interventions and recurrent flood episodes. This hydrogeomorphological approach allowed the integration of geomorphological knowledge and hydrologic-hydraulic modelling in flood analysis. As a result, it led to a deeper comprehension of the processes of inundation, which would be challenging to thoroughly understand without incorporating a holistic method, an essential factor for defining effective flood management strategies.

Collective knowledge brokering in social enterprises: developing an immersion-based model using Schein’s organisational cone

ABSTRACT Knowledge acquisition and integration across boundaries is essential for developing innovative solutions in social enterprises (SEs). The vital role of knowledge brokers in this process has been sparsely discussed. This study examined knowledge brokering roles for effective solution development in SEs. We qualitatively analysed four Indian SEs using analytical techniques of grounded theory and the Gioia method. Brokering emerged as a collective process involving multiple agents who spanned for, translated and integrated two knowledge sets: expert and contextual. Correspondingly, two brokering roles emerged: boundary spanners and translators, which were further subdivided into primary and secondary. SEs displayed varying brokering configurations, which we explain by developing one of the first models of collective knowledge brokering using Schein’s organisational cone (1971) and immersion. SEs can employ our model to identify and groom agents into specific brokering roles, and develop beneficial brokering configurations, for enhanced effectiveness.

What qualitative systems mapping is and what it could be: integrating and visualizing diverse knowledge of complex problems

AbstractResearchers in sustainability science deal with increasingly complex problems that cross administrative, geographical, disciplinary, and sectoral boundaries, and are characterized by high stakes and deep uncertainties. This in turn creates methodological challenges to frame, structure, and solve complex problems in science and practice. There is a long tradition in visualizing systems as diagrams, and concept and cognitive maps, but there is insufficient differentiation and comparison between these methods and no clear umbrella term has yet been established. Against this background, we systematically review three foundational methods from different academic disciplines—causal diagrams, concept mapping, and cognitive mapping. Comparing and contrasting them, we facilitate a coherent understanding of qualitative systems mapping (QSM) as an umbrella term. We then proceed to explore the evident intersections between these methods to showcase some of the inter- and transdisciplinary opportunities and challenges crystallizing in integrated QSM approaches. Finally, we share case study insights from the food–water–biodiversity nexus in Austria and elaborate on some of the methodological nuances to data integration in QSM. Overall, with this overview paper, we lay the groundwork for a systematic, transparent, and yet flexible development and application of QSM methods to support mixed-methods research design and clear case study documentation, as well as fostering effective inter- and transdisciplinary communication in sustainability science. Further research needs to explore these QSM applications in depth across alternative sustainability science contexts, particularly with respect to efficient and rigorous protocols for knowledge and data integration vis-a-vis complex problems and transdisciplinary research processes.

When Eros Drives Artistic Research

The purpose of this text is to relate artistic research to the search for knowledge driven by Eros. It is necessary to focus on one way of researching -in the arts- to see if its objectives and its epistemological contributions are beyond the requirements of producing proven knowledge. The artistic process that is driven by amorousness could take its epistemological nuclei -the intuitions that are expressed on every artwork- and develop them conceptually on the surface itself of the work of art, resulting in a place that reunites intuitions and concepts to resume the integration of knowledge. 

Healthcare social network research and the ECHO model™: Exploring a community of practice to support cultural brokers and transfer cultural knowledge

BackgroundProject ECHO® networks at Children’s Health Queensland Hospital and Health Service (CHQHHS) are communities of practice designed to mitigate services and systems fragmentation by building collaborative partnerships addressing priority child and youth health needs. Aboriginal and Torres Strait Islander people experience the negative impacts of fragmentation in addition to historical challenges of absent or culturally inappropriate health services. Access to culturally safe and responsive services can be improved by engaging Aboriginal and Torres Strait Islander Health Workers and similar roles in an online community of practice, supporting the integration of cultural and clinical knowledge and self-determination of Aboriginal and Torres Strait Islander consumers in decisions affecting their health.Analysing professional support networks and knowledge sharing patterns helps identify enablers and barriers to partnerships. Using social network research, the multilevel network inclusive of ECHO network members and their colleagues was studied to identify interdisciplinary and cross-sector advice exchange patterns, explore the position of cultural brokers and identify common relational tendencies.MethodsSocial network theories and methods informed the collection of network data and analysis of advice-seeking relationships among ECHO network members and their nominees. Registered members from two ECHO networks were invited to complete the Qualtrics survey. Networks analysed comprised 398 professionals from mainstream health, Aboriginal and Torres Strait Islander Community Controlled Health Organisation, education, disability and child safety service settings.ResultsBrokers were well represented, both those who hold knowledge brokerage positions as well as cultural brokers who incorporate clinical and cultural knowledge enabling holistic care for Aboriginal and Torres Strait Islander patients (38 individuals, 17% of network). Professionals who occupy brokerage positions outside the ECHO network tend to be more connected with co-members within the network.ConclusionsThis study is the first application of contemporary social network theories and methods to investigate an ECHO network. The findings highlight the connectivity afforded by brokers, enabling the coordination and collaboration necessary for effective care integration. Inclusion of cultural brokers in an ECHO network provides sustained peer group support while also cultivating relationships that facilitate the integration of cultural and clinical knowledge.

Evaluating Transformers and Linguistic Features integration for Author Profiling tasks in Spanish

Author profiling consists of extracting their demographic and psychographic information by examining their writings. This information can then be used to improve the reader experience and to detect bots or propagators of hoaxes and/or hate speech. Therefore, author profiling can be applied to build more robust and efficient Knowledge-Based Systems for tasks such as content moderation, user profiling, and information retrieval. Author profiling is typically performed automatically as a document classification task. Recently, language models based on transformers have also proven to be quite effective in this task. However, the size and heterogeneity of novel language models, makes it necessary to evaluate them in context. The contributions we make in this paper are four-fold: First, we evaluate which language models are best suited to perform author profiling in Spanish. These experiments include basic, distilled, and multilingual models. Second, we evaluate how feature integration can improve performance for this task. We evaluate two distinct strategies: knowledge integration and ensemble learning. Third, we evaluate the ability of linguistic features to improve the interpretability of the results. Fourth, we evaluate the performance of each language model in terms of memory, training, and inference times. Our results indicate that the use of lightweight models can indeed achieve similar performance to heavy models and that multilingual models are actually less effective than models trained with one language. Finally, we confirm that the best models and strategies for integrating features ultimately depend on the context of the task.

ENHANCING REFLECTIVE CAPACITIES IN EXERCISE PHYSIOLOGY: BRIDGING EDUCATION AND LIFELONG PROFESSIONAL LEARNING

INTRODUCTION & OBJECTIVES Essential to both students and experienced exercise physiology practitioners, reflective practice is a cornerstone of lifelong learning and improved client care. In clinical settings reflection, characterised by awareness, is the critical analysis of experience and requires integration into higher education curriculum. METHODOLOGY Final-year exercise physiology students (N = 21) and 5 clinical exercise physiology supervisors completed a reflective practice learning activity. The stepped process of SPROUT was utilised, via classroom teaching and 14 weeks of clinical practicum. SPROUT represents the Situation, Past experiences, Read and refer, Other influences, Understanding, and Taking it forward. Teaching modes included group discussion, clinical situations, and student-led practice along with the completion of written reflections. Each student responded to the Reflection in Learning Scale (RLS) and further questions about their confidence. Written scripts were analysed for meaning and each ranked. Supervisors responded to open-ended questions during a focus group. FINDINGS Student scores for the RLS revealed higher scores for planning, knowledge integration and mental processing and lower for interactions with knowledge, mindful summarising and coping with negative emotions. 75% of students indicated that they were confident with reflective practice and that SPROUT, however time-consuming, had been helpful. Written scripts showed a range of developing abilities, from novice requiring more depth to those more advanced who demonstrated meaningful engagement and solution-orientated reflective ability. Clinical supervisors supported the importance of reflective practice and were positive about the application of the stepped approach of SPROUT. CONCLUSIONS The SPROUT framework supported reflective practice learning, benefiting both students and qualified practitioners, facilitating not only increased confidence but also a more profound understanding of clinical encounters. The structured approach of SPROUT proved beneficial in guiding reflective thinking. This research underscores the importance of reflective practice in exercise physiology, endorsing its role in ongoing professional development.

TPACK competence of mathematics education students in designing constructivist learning

The problem and the aim of the study. This study examines mathematics education students' TPACK (Technological Pedagogical Content Knowledge) and constructivist learning skills. The major issue is mathematical education students' ability to integrate technology, pedagogical expertise, and mathematical content knowledge in a constructivist learning setting. The study topics include how well students employ constructivist ideas in learning design, how technology supports mathematics learning, and what variables impact their TPACK abilities. By studying it more, this research may help enhance the mathematics education curriculum and teacher training to meet 21st-century education needs. Research methods. Qualitative case study research is used in this work. 23 students of the mathematics education program at Universitas Negeri Surabaya were selected. After observing students in constructivist mathematics learning design activities, in-depth interviews were conducted to understand better their views on TPACK and the constructivist approach, and their learning plans were analyzed. Students' TPACK abilities in constructivist learning will be assessed using thematic and triangulated data analysis. The research findings may help explain the problems and opportunities of constructing a mathematics education curriculum, particularly when merging TPACK and constructivist techniques. Results. The average TPACK competency of math education students is 78.4%, according to studies. According to the TPACK component analysis, students scored 85.2% on the Technology Knowledge Test (TK), 79.6% on the Pedagogical Knowledge Test (PK), and 75.8% on the Content Knowledge Test (CK). However, CK component proficiency varied, suggesting mathematical content knowledge should be improved. In-depth interviews showed that students with formal educational technology training or courses had higher TPACK ratings (M = 82.1%) than those without (M = 76.3%). These data show that formal education and training improve TPACK abilities. To optimize the constructivist method, the CK component must be improved. This research shows that technology integration and mathematics subject mastery are crucial to constructivist learning curricula and training programs for mathematics education students. Conclusion. The constructivist learning TPACK skills of mathematics education students are examined in this study. Students demonstrated technology integration, instructional competency, and mathematical topic knowledge with an average TPACK score of 78.4%. According to TPACK component analysis, pupils have good technical skills (TK) but varied mathematics topic knowledge. TPACK scores are higher for technology education students, demonstrating the role of formal learning in competency. These findings show that CK needs to include constructivism better. In conclusion, comprehensive training and mathematical content additions can improve students' TPACK skills, enabling constructivist technology usage. These insights may assist in improving mathematics curriculum and training.

Effect of implementing demonstration method on students' understanding and practical skills in Milling Machining Course

Mechanical engineering education plays a very important role in providing students with the knowledge and skills to become competent professionals in the industrialised world. This learning process involves the integration of theoretical knowledge and practical skills, and the demonstration method naturally helps to combine both aspects. The demonstration method has proved to be a promising approach to improving students' understanding and practical skills in the subject of milling machining techniques.  This study aims to improve the understanding and practical skills of Mechanical Engineering Class XI students at SMK Negeri 5 Padang through the application of the demonstration method. Using a quasi-experimental research design, the results showed that the use of the demonstration method significantly improved students' understanding and skills. The evaluation by a team of validators also showed that the method was valid in terms of materials, methods and instruments. There was a difference and an increase in scores between the control class and the experimental class. In the written comprehension test, the control class scored an average of 65, while the experimental class scored an average of 80.62. In the practical skills test using the evaluation sheet, the control class achieved an average of 48.87, while the experimental class achieved an average of 74.78. This indicates a positive influence and change in the students' understanding and practical skills after implementing the demonstration method.

Anomaly Detection in Blockchain Using Machine Learning

Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques.  The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.

Knowledge Entrainment in Large-Scale Transformation Projects:The Evidence-Based Strategy and the Innovation-Based Strategy

Prior research has indicated that knowledge integration is essential for succeeding with large-scale transformation (LST) projects. However, research has largely ignored the processual properties of knowledge integration. This article embarks on the notion of knowledge entrainment and demonstrates the criticality of the processual nature of knowledge integration. Through a comparative qualitative case study of two LST projects in the healthcare sector, two knowledge entrainment strategies were identified (the evidence-based strategy and the innovation-based strategy). Data analysis uncovered triggers, practices, and outcomes of the two knowledge entrainment strategies. Additionally, the central role of temporary project management offices (PMOs) for arranging knowledge entrainment within the LST projects is demonstrated.

Improving Indigenous health equity within the emergency department: a global review of interventions.

Indigenous health equity interventions situated within emergency care settings remain underexplored, despite their potential to influence patient care satisfaction and empowerment. This study aimed to systematically review and identify Indigenous equity interventions and their outcomes within acute care settings, which can potentially be utilized to improve equity within Canadian healthcare for Indigenous patients. A database search was completed of Medline, PubMed, Embase, Google Scholar, Scopus and CINAHL from inception to April 2023. For inclusion in the review, articles were interventional and encompassed program descriptions, evaluations, or theoretical frameworks within acute care settings for Indigenous patients. We evaluated the methodological quality using both the Joanna Briggs Institute checklist and the Ways Tried and True framework. Our literature search generated 122 publications. 11 articles were selected for full-text review, with five included in the final analysis. Two focusing on Canadian First Nations populations and three on Aboriginal Australians. The main intervention strategies included cultural safety training, integration of Indigenous knowledge into care models, optimizing waiting-room environments, and emphasizing sustainable evaluation methodologies. The quality of the interventions was varied, with the most promising studies including Indigenous perspectives and partnerships with local Indigenous organizations. Acute care settings, serving as the primary point of access to health care for many Indigenous populations, are well-positioned to implement health equity interventions such as cultural safety training, Indigenous knowledge integration, and optimization of waiting room environments, combined with sustainable evaluation methods. Participatory discussions with Indigenous communities are needed to advance this area of research and determine which interventions are relevant and appropriate for their local context.

Application of Interdisciplinary Knowledge Integration in English Blended Education

Blended education in English combines traditional face-to-face teaching methods with online learning components, offering students a dynamic and flexible educational experience. It integrates technology into classroom instruction, providing access to digital resources, interactive learning platforms, and multimedia content. This approach allows for personalized learning experiences tailored to individual student needs while promoting collaboration, critical thinking, and digital literacy skills. Blended education in English fosters a conducive learning environment that combines the benefits of both traditional and online learning, enhancing student engagement and outcomes in English language acquisition and comprehension. This paper explores the potential of Cooperative Optimized Blended Education (COBE) to enhance interdisciplinary knowledge integration within English-blended education settings. Drawing upon theoretical frameworks such as game theory and optimization strategies, alongside empirical analyses and simulation results, the study elucidates the effectiveness of COBE in fostering collaborative learning outcomes. Through the utilization of digital resources, personalized learning paths, collaborative projects, blended assessment methods, and teacher training programs, COBE offers a dynamic and engaging approach to pedagogical innovation. Empirical findings and student performance data underscore the significance of COBE in promoting student engagement, knowledge retention, and interdisciplinary understanding. Furthermore, the paper highlights the importance of continuous monitoring and assessment to support student academic achievement within COBE. OBE resulted in a mean increase of 10% in student engagement, a 15% improvement in knowledge retention, and a 20% enhancement in interdisciplinary understanding. Furthermore, the paper emphasizes continuous monitoring and assessment to support student academic achievement within COBE.

An agent architecture for expressive spatial knowledge and reasoning in land use modeling and simulations

This paper presents a new cognitive agent design approach integrating spatial knowledge representation and reasoning in agent-based modeling dedicated to land use simulations. A deep motivation for our agent-centric contribution is the ever-increasing development of spatially explicit agent simulation platforms. We build on this technological evolution and topology theory to endow the agent with human’s spatial representation and reasoning following a Belief–Desire–Intention architecture. A pilot implementation of the methodology with simulation experiments on a hunting model was carried out in GAMA platform to assess agent performances. Simulations display a consistent trend of animal population dynamics and also confirm a high model sensitivity to the integration of spatial knowledge and reasoning in agent-based models of human actor. These results demonstrate a successful implementation and the importance of spatial dimension in the expressive power and the validity of agent-based models. Future research efforts should therefore emphasize on designing human knowledge representation and incorporating learning abilities to improve models efficiency.

Designing learning experiences using serious games: innovative village case study

The increasing interest in Learning Experience Design (LXD) has consolidated this field as a new way to transform educational practices. Thanks to its interdisciplinary nature which is mainly rooted in the close relation between human–computer interaction, user experience design and the learning sciences, LXD is a field that demands frameworks to design and develop products that can grow into services, to create interactive learning environments able to provide improvement-driven analytics and to guarantee a significant and satisfactory experience, designed to achieve learning outcomes. Innovative Village serious video game (IVVG) is a service-oriented product within an entrepreneurship and innovation system of platforms developed as an abilities-focused learning environment, and that builds a case study for LXD. This research aims to contribute to the consolidation of the emergent field of Learning Experience Design by providing a case around the Entrepreneurship and Innovation area from EAFIT University in Medellin, a learning system that comprises several service-oriented products; by being one of the products that constitute this system, and as a serious video game, Innovative Village has proven to be a key player in facilitating the learning outcomes and the knowledge integration that stem from the learning environment of the Interactive Design program, where the students’ learning experiences take place. First, related theoretical concepts and historical data will be analyzed to provide background information, then the case study will be addressed focusing on the materials, methods, and results. The study shows that the video game encourages collaborative behavior between players, as perceived by a significant proportion of participants. The research establishes a link between this perception and the role of creaticides in the game. Learning Experience Design (LXD) is about creating products that link the learning process with key competences. The game “Innovative Village” exemplifies this approach and provides insights into design, use, and required competencies. It also presents a framework for designing user-centered learning experiences that incorporate assessments to enhance the learning process. This framework is applicable from the early stages and can be tested in real learning environments.

Ethnobotanical Significance, Phytopharmacology, and Toxicological Profile of Limonia acidissima L. (Rutaceae): A Review.

Medicinal plants play a crucial role in traditional medicine, where they are extensively employed by traditional physicians for treating a wide array of ailments. Limonia acidissima (Linn), commonly known as the Wood apple and belonging to the Rutaceae family, is widely cultivated in countries, such as Pakistan, India, and Sri Lanka. The various parts of Limonia acidissima, including its roots, leaves, fruits, stem, and even the whole plant, have been traditionally used in the treatment of numerous conditions, such as constipation, diarrhea, dysentery, skin diseases, asthma, astringent, diabetes, jaundice, and dyspepsia and act as diuretic agents, cardiotonic, cardiac stimulant, and hepato-protectant. Extensive research has revealed that Limonia acidissima possesses a rich chemical composition, including quinones, lignans, flavonoids, sterols, coumarins, alkaloids, triterpenoids, phenolic acids, and volatile oils, present in its stem, leaves, fruits, stem bark, and roots. These chemical constituents contribute to its diverse therapeutic properties. In recent years, numerous studies have explored the ethanopharmacological properties of Limonia acidissima extracts, highlighting their anticancer, antidiabetic, anti-diarrheal, antimicrobial, antiulcer, wound healing, antioxidant, hepatoprotective, antibacterial, antifungal, larvicidal, neuroprotective, antispermatogenic, antihistaminic, diuretic, and adsorbent activities. This comprehensive review focuses on the traditional uses, biological activities, and phytoconstituents isolated from different parts of Limonia acidissima Linn. The gathered information provides valuable insights into the therapeutic potential of this plant, serving as a foundation for further research and the development of novel pharmaceuticals. The integration of traditional knowledge with scientific evidence enhances the understanding of the medicinal properties of Limonia acidissima, fostering its utilization in modern medicine and healthcare systems.

Sustainable development through frugal innovation: the role of leadership, entrepreneurial bricolage and knowledge diversity

AbstractFrugal innovation through frugal functionality, frugal cost, and frugal ecosystems that reduce goods to their essential features has emerged as a highly effective method to improve sustainable development across societies, in developed countries as well as in developing countries. Drawing on social exchange theory and effectuation theory, this study examines the indirect effect of diversified knowledge sharing on the relationship of sustainability-oriented leadership with frugal innovation and the conditional role of entrepreneurial bricolage. The study utilized data collected from 223 representatives of small and medium enterprises in Poland. Partial least squares structural equation modelling was used to test the proposed hypotheses. The results confirmed that sustainability-oriented leadership indirectly and significantly affects frugal functionality and costs through both internal and external diversified knowledge sharing. Yet, internal diversified knowledge sharing does not mediate the sustainability-oriented leadership–frugal ecosystem relationship. The findings show that the sustainability-oriented leadership–external diversified knowledge sharing relationship amplifies with increasing values of entrepreneurial bricolage. However, the sustainability-oriented leadership–internal diversified knowledge sharing relationship does not change in the presence of entrepreneurial bricolage. This pioneering study explores the interactions between sustainability-oriented leadership, entrepreneurial bricolage, and internal as well as external diversified knowledge sharing with regards to frugal innovation. It uncovers the underlying processes of frugal innovation and improved frugal innovation performance, facilitates the integration of knowledge, recognises successful approaches to leadership, creates initiatives for leadership development, and offers actionable insights for organisations aiming to cultivate frugal innovation.

GPT for medical entity recognition in Spanish

AbstractIn recent years, there has been a remarkable surge in the development of Natural Language Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER). Models such as BERT have demonstrated exceptional performance, leveraging annotated corpora for accurate entity identification. However, the question arises: Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR). This study utilized a dataset of Spanish EHRs related to breast cancer and implemented both a traditional NER method using BERT, and a contemporary approach that combines few shot learning and integration of external knowledge, driven by LLMs using GPT, to structure the data. The analysis involved a comprehensive pipeline that included these methods. Key performance metrics, such as precision, recall, and F-score, were used to evaluate the effectiveness of each method. This comparative approach aimed to highlight the strengths and limitations of each method in the context of structuring Spanish EHRs efficiently and accurately.The comparative analysis undertaken in this article demonstrates that both the traditional BERT-based NER method and the few-shot LLM-driven approach, augmented with external knowledge, provide comparable levels of precision in metrics such as precision, recall, and F score when applied to Spanish EHR. Contrary to expectations, the LLM-driven approach, which necessitates minimal data annotation, performs on par with BERT’s capability to discern complex medical terminologies and contextual nuances within the EHRs. The results of this study highlight a notable advance in the field of NER for Spanish EHRs, with the few shot approach driven by LLM, enhanced by external knowledge, slightly edging out the traditional BERT-based method in overall effectiveness. GPT’s superiority in F-score and its minimal reliance on extensive data annotation underscore its potential in medical data processing.