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Alzheimer's disease is the most common cause of dementia. It is an increasingly serious global health problem and has a significant impact on individuals and society. However, the precise cause of Alzheimer's disease is still unknown. In this study, 11,748 Web-of-Science-indexed manuscripts regarding Alzheimer's disease, all published from 2015 to 2019, and their 693,938 references were analyzed. A document co-citation network map was drawn using CiteSpace software. Research frontiers and development trends were determined by retrieving subject headings with apparent changing word frequency trends, which can be used to forecast future research developments in Alzheimer's disease.

As the popularity of project‐based learning grows, so does the importance of understanding how this instructional approach can support students’ learning and development. The authors describe a project‐based approach to literacy and social studies instruction that research has shown to be effective. Key characteristics of the approach and illustrations of how those characteristics are enacted in a project‐based learning geography unit are identified. In the unit, students develop informational reading and persuasive writing skills and learn key social studies content and skills by engaging in the development of brochures about their local community for an authentic audience. The authors also describe how educators can navigate common challenges that can arise when transitioning to a project‐based approach.

This study explores the segmentation algorithm of item text data, especially of single long length data in health examination. In the specific implementation, a large amount of historical health examination data is analysed. Using the method of character statistics, the connection tightness values TABs between two adjacent characters are calculated. Three parameters, the candidate number N, the best position BP, and balance weight BW are set. The total segmentation indexes SIs are calculated, thus determined the segmentation position Pos. The optimal parameter values are determined by the method of information measurement. Experimental results show that the accuracy rate is 78.6% and reaches 82.9% in the most frequently appeared text item. The complexity of the algorithm is O(n). Using no existing domain knowledge, it is very simple and fast. By executed repeatedly, it is convenient to obtain the characteristics of each single item of text data, furthermore, to distinguish respective express preference of different physicians to the same item. The assumption is verified that without professional domain knowledge, a large amount of historical data can provide valuable clues for the text understanding. The results of this research are being applied and verified in the following research works in the field of health examination.

Along with crystallized intelligence (Gc), domain-specific knowledge (Gkn) is an important ability within the nomological net of acquired knowledge. Although Gkn has been shown to predict important life outcomes, only a few standardized tests measuring Gkn exist, especially for the adult population. Complicating things, Gkn tests from different cultural circles cannot simply be translated as they need to be culture specific. Hence, this study aimed to develop a Gkn test culturally sensitive to a German population and to provide initial evidence for the resulting scores’ psychometric quality. Existing Gkn tests often mirror a school curriculum. We aimed to operationalize Gkn not solely based upon a typical curriculum to investigate a research question regarding the curriculum dependence of the resulting Gkn structure. A set of newly developed items from a broad range of knowledge categories was presented online to 1450 participants divided into a high (fluid intelligence, Gf) Gf (n = 415) and an unselected Gf subsample (n = 1035). Results support the notion of a hierarchical model comparable to the one curriculum-based tests scores have, with one factor at the top and three narrower factors below (Humanities, Science, Civics) for which each can be divided into smaller knowledge facets. Besides this initial evidence regarding structural validity, the scale scores’ reliability estimates are reported, and criterion validity-related evidence based on a known-groups design is provided. Results indicate the psychometric quality of the scores and are discussed.

Research summary: The literature on technological alliances emphasizes that search for knowledge drives alliance formation. However, in conceptualizing technological knowledge, prior work on alliances has not made a distinction between domain knowledge—knowledge that firms possess in distinct technological domains—and architectural knowledge—knowledge that firms possess about how to combine elements from different technological domains. We argue that firms seek partners that are similar in domain knowledge to deepen their knowledge, and partners that are dissimilar in architectural knowledge to broaden their knowledge. Our results indicate that the likelihood of alliance formation increases when two firms are similar in domain knowledge and dissimilar in architectural knowledge. Further, our results show that these effects are positively moderated by the degree of decomposability of a firm's knowledge base. Managerial summary: In dynamic environments, companies need to continually deepen and broaden their technological knowledge, and they often look for alliance partners who can provide them that knowledge. For knowledge deepening, companies are more likely to form alliances with those companies that have expertise in similar technological fields. For knowledge broadening, they are more likely to form alliances with those companies that have expertise in the same technological fields, but have different recipes for combining knowledge from those fields. Furthermore, a company with a modular knowledge base is more likely to seek a partner that has expertise in similar technological fields or whose recipes for combining knowledge from different technological fields are different from the recipes it has.

Although seasonality is an old tourism phenomenon, the advent of global hypermobilities and the changing features of the tourism industry requires a considerable revisit to the longstanding assumption of tourism. This study provokes further research on various seasonality aspects implicated in the discussion based on the broad range of the literature review and the subsequent findings. The article explores the current seasonality research in tourism and hospitality and proposes a typology and literature-based agenda for future scholarly investigations. Results provide an overview of seasonality research in tourism and hospitality by examining key themes in theory, methodology, research topics, and geographical features. We underscore the importance of theoretical and conceptual development and a comprehensive approach to seasonality research in tourism and hospitality.

Domain knowledge graph has become a research topic in the era of artificial intelligence. Knowledge representation is the key step to construct domain knowledge graph. There have been quite a few well-established general knowledge graphs. However, there are still gaps on the domain knowledge graph construction. The research introduces the related concepts of the knowledge representation and analyzes knowledge representation of knowledge graphs by category, which includes some classical general knowledge graphs and several typical domain knowledge graphs. The paper also discusses the development of knowledge representation in accordance with the difference of entities, relationships and properties. It also presents the unsolved problems and future research trends in the knowledge representation of domain knowledge graph study.

Purpose – In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue. Design/methodology/approach – In order to generate a knowledge sample that represents truly a certain domain knowledge, the authors can use sampling algorithms. In this paper, the authors present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball and Represent algorithm) and investigate which structural properties of the domain knowledge sample are preserved after sampling process is conducted. Findings – The samples that the authors got using these algorithms are compared and the authors have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to find the best one. Originality/value – This approach is original as the authors could not find any similar work that uses graph sampling methods for student modeling.

[This corrects the article DOI: 10.3389/fimmu.2025.1633989.].

Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions. The graphical structure of such models can be estimated by structure learning algorithms, domain knowledge, or a combination of both. Various knowledge approaches have been proposed in the literature that enables us to specify prior knowledge that constrains or guides these algorithms. This paper introduces some novel, and also describes some existing, knowledge-based approaches that enable us to combine structure learning with knowledge obtained from heterogeneous sources. We investigate the impact of these approaches on structure learning across different algorithms, case studies and settings that we might encounter in practice. Each approach is assessed in terms of effectiveness and efficiency, including graphical accuracy, model fitting, complexity, and runtime; making this the first paper that provides a comparative evaluation of a wide range of knowledge approaches for structure learning. Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data. While the overall results show that knowledge becomes less important with big data due to higher learning accuracy rendering knowledge less important, some of the knowledge approaches are found to be more important with big data. Amongst the main conclusions is the observation that reduced search space obtained from knowledge does not always imply reduced computational complexity, perhaps because the relationships implied by the data and knowledge are in tension.