Knowledge Model Research Articles

Physical science teachers’ perspectives on professional development and use of digital technology

This paper examines physical science teachers’ views and experiences on professional devel-opment and digital technology integration for teaching content specific. Using a qualitative ap-proach and case study design embedded in the technological pedagogical content knowledge (TPACK) model and concerns-based adoption model (CBAM), this article reports on how physi-cal science teachers from technologically resourced schools perceive their preparation to use technology during professional development. Four participants with more than three years teaching experience of physical science were selected for data collection. Data was collected through semi-structured interviews and was thematically analyzed. The key findings of the study indicate that inadequate digital technology professional development leads to teachers’ unpreparedness which tremendously influences the extent of using technology for teaching in physical science content. The paper recommends the need for ongoing professional develop-ment of physical science teachers on the use of digital technology to teach specific content. Based on the existing literature, the principles of effective professional development can be used to narrow inequalities in developing countries such as Lesotho. The paper recommends future research to investigate the maximum effectiveness of technology integration in other subjects as to improve teaching and learning in developing countries such as Lesotho.

Spillover Effects of Paid Functions on Physicians' Unpaid Knowledge Activities: Quasi-Experimental Approach.

To promote sustained contributions by physicians to online health care communities, these platforms have introduced a content payment model that offers economic incentives for physicians' online knowledge activities. However, the impact of these paid features on unpaid knowledge activities remains unexplored. This study investigated how the introduction of economic incentives in online medical communities affects physicians' unpaid knowledge activities in the community. The data for this study were obtained from the Haodf Online platform in China, which has implemented paid scenarios for its science popularization function, providing economic benefits to physicians. The dataset, which comprises panel data, includes 7453 physicians who participated in both paid and unpaid knowledge contributions on the website. This study examined the impact of paid knowledge activities on physicians' free knowledge contributions, focusing on dimensions including knowledge quantity, quality, and diversity. To address the timing discrepancies in physicians' participation in paid activities, we used a quasi-experimental design that combined the approach of propensity score matching and multi-period difference in differences. In the balance test results of the propensity score matching, the absolute values of the SDs of all matching variables were mostly <5% after matching, ensuring the accuracy of the results obtained from the difference in differences method. This study found that participation in paid knowledge activities had a positive spillover effect on physicians' free knowledge contributions, which manifested in the increase in post quantity (473.1%; P<.001), article length (108%; P=.009), function word frequency (0.6%; P=.001), causal word frequency (0.2%; P<.001), and content information entropy (6.6%; P=.006). The paid function led to a decrease in the consistency between titles and content (-115.5%; P<.001). The findings of this study contribute to the existing literature on the impact of economic incentives in the medical context. For the platform, providing economic incentives to physicians can have positive significance in promoting the development of the platform's knowledge ecosystem and can effectively encourage physicians to contribute to both paid and free knowledge activities. This study provides a valuable reference for the platform to introduce a paid knowledge model, which is beneficial to the sustainable development of the platform.

LCC-HVDC Link Performance Amelioration by Dynamic Fuzzy PI Controller

Recently, HVDC connections have experienced significant development in their integration into electrical systems.In this work, we considered reducing the effect of short circuits in HVDC links by using Intelligence artificial method that operate dynamically in real time. In particular, the limit values of the current at the start of the short circuit and switching failure. Thus, we have focused on improving performance during short circuits using one of the methods of artificial intelligence, which is fuzzy logic, which does not require in-depth knowledge and complete mathematical modeling of the system to be improved, such as nonlinear systems. This has been represented by proposing a fuzzy-based intelligent controller that controls the characteristics of the traditional LCC-HVDC controller so that it dynamically changes as needed. The results were very satisfactory for single-phase faults and also for three-phase faults.

The Knowledge and Health Belief Model of Osteoporosis Prevention Among Females in Saudi Arabia.

Background Osteoporosis is the most common bone disease in humans, representing a major public health problem affectingwomen more commonly. The prevalence of osteoporosis in Saudi Arabia is high among females. Thus,the knowledge and Health Belief Model (HBM) of osteoporosis, which can be used to gain an understanding of health behaviors and reasons for non-complianceto osteoprotective recommendations, is necessary. This study aimed to investigate theknowledge and constructs of the HBM for osteoporosis prevention among females in Saudi Arabia. Methodology This cross-sectional study investigated the knowledge and HBM which includes perceived susceptibility and severity, perceived benefits, barriers, self-efficacy, and clues to action for preventing osteoporosis among females in Saudi Arabia. The study population consisted of females living in Saudi Arabia aged 18 years or more and not having osteoporosis. The convenient consecutive non-probability sampling technique was used to recruit 406 females aged 18 years or more using an online form. In addition to content validity and Cronbach's alpha for overall reliability, Bloom's cut-off scale was utilized to evaluate the knowledge level and HBM. Results The study revealed a moderate level of knowledge of all the constructs of HBM except that of perceived barriers which was low. All the constructs of the HBM were significantly and positively correlated with knowledge level except perceived barriers which was significantly and negatively correlated with knowledge level. Many participants received cues to act, such as not being able to afford treatment or not having anybody to take care of them while they were sick. Conclusions The Saudi female population's general knowledge of osteoporosis and HBM was moderate,while that of perceived barriers was low.

Building a Digital Twin for a Ground Heat Exchanger

AbstractThis research investigates the development of a digital twin (DT) for ground heat exchangers (GHEs) and its potential to enhance the efficiency and sustainability of shallow geothermal energy systems. It introduces an innovative approach for building a GHE‐DT that connects the physical and digital systems to monitor key parameters, predict issues, and optimize energy efficiency. The process involves several phases including implicit knowledge codification, data‐driven analysis, model construction, and system design. The study emphasizes real‐time monitoring of the effective parameters: ground temperature and fluid conditions (flow rate, temperature, and pressure). The GHE‐DT's digital system mainly comprises three sections, namely, data storage, mathematical modeling, and data‐driven modeling. The role of the presented mathematical model is to simulate the GHE's behavior and assess its performance characteristics, such as the heat exchanger's effectiveness and efficiency. Additionally, the data‐driven model used in the proposed DT utilizes formal concept analysis and relation concept analysis to identify connections and associations among parameters for a better understanding of the GHE functioning. The GHE‐DT provides useful services including trend analysis, problem prediction, and correlation analysis. These services provide engineers and operators with the opportunity to increase dependability, save maintenance costs, and optimize GHE performance.

Transition of care: toward tailored transition of adolescents with osteoporosis and metabolic bone diseases to adult care through an integrated, people-centered, disease-specific health services framework

BackgroundThe Egyptian Academy of Bone and Muscle Health in collaboration with the Egyptian College of Pediatric Rheumatology have identified two indicators to ensure optimum health outcomes among children: one is developmentally and nutritionally appropriate healthcare, and the second is the availability of an effective transition of care model from pediatric care to adult health services. Currently, there is no national guidelines on transition of care for children and adolescents with osteoporosis and metabolic bone disease. To close this gap, this work was carried out to develop the Egyptian guidelines for transition model of care.ResultsThe final recommendation included 12 specific domains. These included the following: targeted population, transition age, transition readiness, patient-targeted care, and disease-specific knowledge, patient education, and optimum model for the transition of care, transition referral, management approach, high quality of care, documentation, and recent service approaches as well as standards and quality indicators. All the overarching principles and most voters agreed with the 12 amended recommendations.ConclusionTransitional care is the term used to describe services that seek to bridge this care gap. Based on the best available data and professional judgment, these consensus-based recommendations guide ways to achieve optimal outcomes in transitional care for adolescents with osteoporosis and metabolic bone disorders. Though focussed on transition of care of bone and muscle health, this article can be the bases for transition services for children with other chronic conditions. This could be used as a blueprint for additional national or internationally healthcare transition policies.

Research on the influence of teachers' innovative behavior on organizational development in Chengdu-Chongqing area of China

In the context of the Big Data era, especially with the emergence of AI, various industries are facing enormous challenges. The advent of big data has also allowed humans to access and deeply use system data in a wider range, exploring knowledge and information that had never been seen before. Knowledge and information resources are crucial factors in promoting social and economic development. At the same time, innovative behavior is an essential skill closely linked to AI and big data in the 21st century, and it is one of the most important human resources. With the rapid development and widespread application of AI and big data technologies, how teachers in traditional education can avoid being replaced by machines, effectively stimulate their innovative behavior, improve their work performance, and contribute to organizational development has become a core issue that needs to be urgently addressed in the current era of rapidly acquired knowledge and breaking traditional education models. In the rapidly developing Chengdu-Chongqing economic zone, as one of the four poles of China's economic development, universities serve as a hub for talent cultivation, scientific and technological innovation, and industrial upgrading. The importance of the innovative behavior of Chengdu-Chongqing region's university teachers for organizational development is evident. Based on this, this article provides suggestions and explorations.

A quantitative model of technological pedagogical content knowledge (TPACK) based on graphical calculation

Technological Pedagogical Content Knowledge (TPACK) is a theoretical framework used to assess information teaching. However, its application and popularity in practice is limited due to usability and accuracy issues. This study presents a quantitative model called TPACK-Venn, which is based on the graphical calculation of a Venn diagram determined by survey results. The model was tested through a teaching experiment in which students are invited to create micro-courses. Self-evaluations and objective evaluations were conducted independently. A total of 112 micro-courses and their corresponding subjective self-evaluations, as well as 224 objective evaluations, were collected as the control group. After screening, the remaining 96 scores and TPACK modeling values of micro-courses were standardized. The standardized values have an inter-rater reliability of over 80%. It is important to note that the TPACK-Venn model adjusts the significance of the normal distribution of the survey results. As the normal distribution has widespread applicability in teaching guidance. This study presents a highly efficient and practical model for applying the TPACK theoretical framework.

A Blockchain Semantic-based Approach for Secure and Traceable Agri-Food Supply Chain

Ensuring food security is crucial for maintaining food quality and enhancing consumer services by guaranteeing both safety and satisfaction. However, traditional methods to ensure food security are often susceptible to various forms of fraud and require significant processing overhead, making them inefficient for the evolving demands of modern food supply chains. To address these shortcomings, blockchain technology has emerged as a robust and efficient solution to enhance food security. This paper presents a novel lightweight blockchain-based signature mechanism designed for the rapid detection of food fraud. It also includes a domain-specific ontology to serve as a structured knowledge model, allowing systematic analysis and detection of different types of fraud within the food supply chain. This approach uses smart contracts built on lightweight blockchain technology to initiate and manage transactions related to food fraud. Then, semantic rules are applied to detect and identify fraudulent activities. Once fraud is detected, associated transactions are encrypted and tracked, ensuring visibility and traceability among consortium members. Experimental results based on large-scale transaction data demonstrated ~7.5× speed improvement over iterative search algorithms while maintaining high transaction traceability and significantly reducing storage costs.

Cognitive Models for Machine Theory of Mind

AbstractSome of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence.

Can You Tolerate Influencer Marketing? An Empirical Investigation of Live Streaming Viewership Reduction related to Influencer Marketing

Live streaming viewership has grown rapidly in recent years, driven by its unique features of bidirectional, real-time interaction and personalized content. These qualities have positioned live streaming as a major venue for influencer marketing, where brands collaborate with influencers to promote products to engaged viewers. However, this study suggests that influencer marketing within live streaming may lead to a decline in viewership as viewers experience resistance toward sponsored content. Building on the persuasion knowledge model, we analyze this phenomenon using streaming data from Twitch.tv and apply propensity score matching (PSM) to assess viewership trends. Our findings reveal a significant decline in viewership associated with influencer marketing. We also identify strategies that streamers can employ to mitigate this negative impact. First, moderate levels of real-time interaction between streamers and viewers help alleviate viewership reduction. Second, streamers who diversify their content topics experience less viewership declines. Lastly, minimizing negative content reduces the adverse effect on viewership. These findings contribute to the literature on influencer marketing and live streaming, offering practical insights for firms and streamers aiming to enhance audience engagement.

Implementing the TPACK model to enhance learning outcomes of future biology teachers for the digital era

This research is motivated by the rapid development of technology, which has impacted learning in preparing prospective teachers for the digital era. One of the essential competencies required for this preparation is the ability to implement the TPACK model in the learning process to improve the learning outcomes of prospective biology teacher students. This study aims to analyze the effect of implementing the Technological Pedagogical Content Knowledge (TPACK) model on the learning outcomes of prospective biology teacher students in preparing them as educators in the digital era. TPACK is a framework that integrates technology, pedagogy, and content to create more effective and interactive learning experiences. The research method used is quasi-experimental, employing a non-equivalent control group design. The results show a significant improvement in the learning outcomes of students in the experimental group compared to the control group. The implementation of TPACK also enhances students' technological skills, which are crucial for their preparation as teachers in the digital era. This study concludes that the application of TPACK not only has a positive impact on cognitive learning outcomes but also fosters a positive attitude towards the use of technology and improves students' psychomotor skills.

Do large language models “understand” their knowledge?

AbstractLarge language models (LLMs) are often criticized for lacking true “understanding” and the ability to “reason” with their knowledge, being seen merely as autocomplete engines. I suggest that this assessment might be missing a nuanced insight. LLMs do develop a kind of empirical “understanding” that is “geometry”‐like, which is adequate for many applications. However, this “geometric” understanding, built from incomplete and noisy data, makes them unreliable, difficult to generalize, and lacking in inference capabilities and explanations. To overcome these limitations, LLMs should be integrated with an “algebraic” representation of knowledge that includes symbolic AI elements used in expert systems. This integration aims to create large knowledge models (LKMs) grounded in first principles that can reason and explain, mimicking human expert capabilities. Furthermore, we need a conceptual breakthrough, such as the transformation from Newtonian mechanics to statistical mechanics, to create a new science of LLMs.

Resilience agility in tourism and hospitality: Empirical research using 3D modelling

The paper presents an empirical application of the integrative model of resilience knowledge in tourism, the ReKo-Model. This study employs a quantitative method to analyse seven dimensions of resilience, with a specific focus on three areas of measurement: awareness, current implementation, and strategic cooperation. The research included obtaining virtual expert evaluations from 62 managers in the hotel industry in Germany. The interpretation of empirical findings is facilitated through the use of visualization techniques. 3D models allow for cluster and gap-analysis, providing insight into the implementation of ReKo-factors and dimensions of resilience agility. By examining how hotel-managers adapt their behaviour, the research reveals that applying resilience theory in tourism and hospitality can have transformative effects. This helps bridge strategic and expertise gaps for a more comprehensive approach to resilience agility. The research supports using a deductive approach and consolidation strategy to build resilience agility in the tourism and hospitality industry.

Impact of the Digital Revolution on Indigenous Technical Knowledge of Land Managers in Kole District, Northern Uganda

Purpose: Considering the various challenges and the evidence of the dwindling agricultural productivity among a good number of farmers in Uganda, the focus of this paper was on how Ugandan farmers can produce higher yields for the exploding national and regional population. Methodology: A desk review method was adopted on selected definitions and earlier models of indigenous technical knowledge are discussed as a basis gauging the consequence of digital revolution on indigenous technical knowledge. Findings: The outcomes suggest that as a result of advancement in technology, there has been an increasing shift; that the traditional conservation wisdom in the cultural and artistic elements of communities in the development and management of biodiversity and its strategies and policies has no proper application in these modern times. Likewise, traditional knowledge systems in the cultures of people do not have any correlation with the scientific conservation models used in modern farming societies. Unique Contribution to Theory, Practice and Policy: It is encouraged that an increase in the number of agricultural workers, involvement of higher institutions of learning and proper acknowledgement of land managers for their unique indigenous technical skills should be prioritized by the government of Uganda. With global connectivity, technological advancement and data driven decision making, an improvement in efficiency of production and way of thinking of land managers will culminate in the best resource utilization of agricultural resources in Kole district.

Machines think, but do we? Embracing AI in flat organizations: Challenges of technological change in women-owned manufacturing firms

Drawing on the combined strengths of Conservation of Resources (COR) theory and Information Processing Theory (IPT), we explored the relationship between technological turbulence and knowledge hiding, with employees’ awareness of AI as a key mediating factor. We also examined the dual moderating effects of hierarchical flatness in an organization and change leadership to test the theoretical synergy of COR and IPT. Data were collected from 315 women-owned manufacturing firms and analyzed through PLS-SEM. The findings reveal that employees’ AI awareness mediates the link between technological turbulence and knowledge-hiding behaviors. A flatter organizational structure aids in spreading AI awareness among employees, while effective change leadership serves as a crucial moderator, reducing knowledge hiding by influencing employee behavior. The survey highlights that increased AI awareness can lead to fears about AI's impact on job security, driving knowledge hiding. However, skilled leadership can mitigate this by promoting innovation and encouraging collaboration with external partners. This study provides original insights by developing and empirically validating a model of knowledge hiding in response to technological turbulence in women-owned manufacturing sectors in Punjab, Pakistan. It deepens the understanding of knowledge management by examining the interplay between organizational flatness and AI integration, areas often overlooked in existing literature.

Self-distillation with model averaging

Knowledge distillation (KD) and model averaging (MA) are prominent techniques for enhancing the efficiency and effectiveness of deep neural networks (DNNs). MA produces an average model by averaging multiple checkpoints along the trajectory of the stochastic gradient descent (SGD), which tends to converge toward the flatter side of the local minimum (Fig. 2). However, MA operates offline and does not influence the specific local minimum to which the network converges. By contrast, KD transfers knowledge from a teacher model to a student model, guiding the student toward a better local minimum. Despite this, KD can still result in a poor convergence location within the minimum if the teacher over/under-regularizes the student model's behavior. By combining KD and MA, we aim to leverage KD for a favorable local minimum and MA for a robust convergence location within the local minimum. However, our results revealed that these two techniques were incompatible. This study empirically analyzed the causes of this incompatibility and proposed an annealing knowledge distillation (AKD) scheme to address it. Building on MA and AKD, we introduced a general self-distillation framework referred to as self-distillation with model averaging (SDMA). To the best of our knowledge, SDMA is the first general distillation framework that aims to achieve a favorable local minimum and a robust convergence location. Extensive experiments demonstrated that SDMA significantly improved the generalization performance of DNNs.

OKLM: Open Knowledge and Learner Model Using Educational Big Data

OKLM: Open Knowledge and Learner Model Using Educational Big Data

Open Knowledge and Learner Model: Mathematical Representation and Applications as Learning Support Foundation in EFL

Open Knowledge and Learner Model: Mathematical Representation and Applications as Learning Support Foundation in EFL

Women's empowerment model in treatment of pregnant women at risk of anemia in Indonesia: Literature review

Anemia in pregnancy has remained a major problem in Indonesia over the past decade. The women's empowerment model in the health sector can be a solution to the problem of pregnant women who are at risk of anemia and towards accelerating the SDGs achievement target in the third "Decade of Action" goal in 2030. Objective: to find out the evidence based on the women's empowerment model in handling pregnant women at risk of anemia in Indonesia. Method: Literature review search usingarticle 2014-2024 withdatabase (pubmed, sciencedirect, and google scholar) relevant to the keywords used maternity; obstetrics and pregnancy; women's empowerment; prevention; anemia. There were 10 articles used in this study which will then be extracted data, as well as compiled, summarized and reported the results. Results: The study showed that the women's empowerment model of knowledge, attitudes, family and community roles, diet, health services and health worker professionalism is effective in handling mothers at risk of anemia.Conclusion: The importance of p supportgovernment andmidwife in givingquality midwifery services, access to credible sources of information, and encouraging the involvement of women, families and communities to realize women's health and well-being globally.