Against the backdrop of rapid digitalization and intelligent transformation, the deep coupling between information and geographic space is driving information geography to emerge as a critical frontier at the intersection of geography and information science. This study systematically reviews the origin and development of information geography, clarifies its fundamental concepts and core connotations, and reveals the interactions among information flows, spatial structures, and social behaviors. Building on existing research, it proposes a theoretical framework encompassing four dimensions: cognitive foundation, spatial structure, information behavior, and technological support. By integrating key technologies such as geographic information systems (GIS), remote sensing, big data mining, and artificial intelligence, the paper summarizes methodological innovations in data acquisition, processing, and analysis. It further highlights recent advances in applications, including the spatial evolution of information dissemination, digital city construction, socio-economic spatial analysis, and the integration of virtual and physical spaces, demonstrating the practical value of information geography in urban governance, ecological monitoring, and regional coordinated development. Meanwhile, it addresses critical challenges such as insufficient interdisciplinary collaboration mechanisms, inadequate data privacy protection, and limitations in algorithm interpretability and generalization, offering potential strategies to overcome these issues. Finally, it outlines future research priorities and development trends, aiming to provide insights and references for advancing theoretical refinement and technological innovation in this field.

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Photosynthesis is the core process of plant growth and biomass accumulation, directly determining the yield potential of crops. This paper systematically explores the relationship between photosynthetic efficiency and crop yield to provide a scientific basis for improving agricultural productivity. First, the fundamental principles of photosynthesis are explained, and the concept and measurement methods of photosynthetic efficiency are defined. Second, the key mechanisms through which photosynthetic efficiency affects crop yield are analyzed, including light energy capture efficiency, carbon assimilation rate, and the structural and functional regulation of photosynthetic organs. Third, major strategies for improving photosynthetic efficiency are reviewed, such as genetic improvement (e.g., genetic engineering and variety breeding), precision agricultural management (e.g., optimizing light conditions and fertilization techniques), and environmental regulation measures (e.g., greenhouse and shading technologies). Through typical case studies, the practical effects of high photosynthetic efficiency varieties in enhancing yield and resource use efficiency are further revealed. Finally, future research directions are proposed, emphasizing the critical role of photosynthetic efficiency enhancement technologies in addressing global food security and achieving sustainable agricultural development. This study provides both theoretical support and practical guidance for a deeper understanding of the relationship between photosynthetic efficiency and crop yield.

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The widespread application of artificial intelligence technologies in higher education is profoundly reshaping traditional teaching models and learning modes, and investigating college students’ acceptance of artificial intelligence tools and the associated mechanisms underlying their learning behavior responses is of substantial theoretical and practical significance for advancing the digital transformation of higher education. This study aims to elucidate the mechanisms through which the adoption of artificial intelligence tools influences college students’ learning engagement and learning outcomes. Grounded in the technology acceptance model, a conceptual framework incorporating perceived usefulness, perceived ease of use, learning engagement, and learning outcomes was constructed. Questionnaire survey data were collected from 812 undergraduate students across three universities, and empirical analyses were conducted using reliability tests, exploratory factor analysis, and hierarchical regression analysis. The results indicate that both perceived usefulness and perceived ease of use exert significant positive effects on learning engagement, and that learning engagement mediates the relationships between perceived usefulness, perceived ease of use, and learning outcomes. In addition, significant differences were observed in artificial intelligence tool usage behaviors across students from different disciplinary backgrounds and academic years. These findings suggest that enhancing students’ perceptions of the usefulness and ease of use of artificial intelligence tools constitutes a critical pathway for strengthening learning engagement and improving learning outcomes.

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Against the backdrop of the deep embedding of digital technologies in educational settings, university students’ knowledge acquisition behaviors are increasingly mediated by algorithm-centered digital platforms, including search engines, social media, video applications, and online education systems. Traditional teacher-led and linearly structured learning pathways are being reconfigured by platform-driven recommendation mechanisms. Drawing on questionnaire survey data and in-depth interviews with undergraduate students at a comprehensive university, this study systematically examines how algorithmic recommendations shape students’ knowledge selection, the construction of learning pathways, and their judgments of knowledge authority. The findings indicate that platform recommendation mechanisms enhance the immediacy and fragmentation of knowledge acquisition, fostering tendencies toward “cognitive echo chambers” and “path dependence,” which in turn constrain knowledge diversity and critical thinking. At the same time, students’ trust in content from informal platforms has increased markedly, leading to a partial erosion of traditional academic authority. This study contributes in three main respects: first, it introduces a perspective from the sociology of algorithms to construct an analytical framework of interactions among technology, knowledge, and power; second, it centers on learners’ subjective experiences to reveal how algorithmic logics reshape cognitive structures and learning behaviors at the micro level; and third, it proposes the concept of a “platformized learning ecology,” offering theoretical support and practical implications for information literacy education and curricular reform in higher education. Overall, this research extends sociological understandings of learning mechanisms in the digital era and provides empirical evidence and strategic insights for educational governance amid the digital transformation of universities.

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