Currently, K12 education information technology (IT) equipment in China has entered a “peak period of renewal and replacement”. However, due to the lack of unified technical standards and a scientific cost estimation model, schools face blind purchasing and significant waste of funds. This study focuses on balancing the “effect of equipment upgrade and funding”. Through literature review, field research (covering 100 K12 schools in 10 provinces across the eastern, central, and western regions), statistical analysis, and case validation, this study conducts research on the formulation of technical standards and the construction of cost estimation models. For the first time, this study establishes differentiated technical standards for “basic equipment” and “smart equipment”, specifying parameters, safety, and compatibility requirements for basic equipment (e.g., resolution of interactive intelligent blackboards ≥ 4K) and smart equipment (e.g., AI grading accuracy ≥ 98%). Based on a three-dimensional framework of “equipment type, school size, and regional economic level”, a cost estimation model is constructed using multiple regression analysis, with a verified error rate of ≤ 5%. Ultimately, a “standard + model + toolkit” implementation plan is formed. Practice has shown that this plan can increase equipment utilization to over 80% and achieve a funding savings rate of 25%.

This paper focuses on quantifying the application boundaries of AI in financial transactions, identifying the cross-institutional risk spillover and transmission patterns, and constructing a multi-dimensional governance framework. Based on a dataset of 986 daily observations from 123 product accounts with a total asset value of 360 billion yuan from 2020 to 2024, combined with in-depth interviews with 15 leading asset management (AM) AI executives and comparative case studies, we develop a two-dimensional “scene-fit-risk tolerance” boundary quantification model and an “AI-securities firm-bank-asset management” risk transmission chain model. We propose the “3% boundary rule” and a three-tier governance framework of “technology-process-regulation.” Empirically validated by a century-old insurance asset management company, this framework reduced the AI transaction risk loss rate from 0.85% to 0.18% while maintaining a 35% improvement in transaction efficiency. It effectively addresses the core pain points of large-scale asset management institutions, such as blind AI application, concealed risk transmission, and an incomplete governance system, providing a solution with both theoretical support and practical value for the industry.

Addressing the industry pain points of “difficult multi-platform data integration, low marketing prediction accuracy, and high technological access costs” in the brand marketing information systems of small and medium-sized enterprises (SMEs), this paper develops two core technologies. First, a multi-source data automatic adaptation middleware is proposed, which, through a “preset template library + dynamic field mapping + incremental synchronization” design, enables one-click access to data from over 10 mainstream marketing platforms. The adaptation efficiency is improved by 14 times compared to traditional solutions, with a synchronization error rate of less than 0.5%. Second, an improved LSTM marketing prediction model is introduced, which incorporates entropy weight method to calculate industry feature weights, addressing the poor cross-industry adaptability of traditional models. This model achieves a prediction accuracy of 92% in the fast-moving consumer goods, catering, and retail industries.

The convergence of reinforcement learning and knowledge tracing represents a pivotal development in the evolution of adaptive learning systems, uniting two previously distinct paradigms of educational intelligence: the inferential modeling of cognition and the optimization of pedagogical decision-making through interaction. This paper presents a theoretical exploration of this synthesis as both a computational and epistemological transformation. It argues that reinforcement learning endows adaptive systems with the capacity for goal-directed agency, while knowledge tracing provides the means to perceive and model the learner’s latent cognitive states. Their integration produces a recursive feedback loop in which perception, reasoning, and action co-evolve, enabling systems to learn how to teach through interaction with learners. Drawing on cognitive theory, complexity science, and the philosophy of education, the study situates the RL–KT paradigm within a broader shift from reactive to anticipatory models of adaptivity. The framework embodies a form of computational pedagogy that mirrors the reflective equilibrium of human teaching, wherein diagnostic inference and prescriptive decision-making are inseparably linked. The paper develops a comprehensive account of this convergence across multiple dimensions: the theoretical foundations of cognitive modeling and control; the architecture and dynamics of RL–KT integration; the conceptual and ethical implications for co-agency between human and artificial learners; and the methodological potential of simulation-based inquiry in computational education. The analysis concludes that RL–KT systems represent a new ontology of adaptive intelligence—self-organizing, intentional, and epistemically aware. They redefine the relationship between learning and teaching, dissolving the hierarchical distinction between teacher and student to establish a continuum of co-learning. In this paradigm, education becomes a living dialogue between human and artificial cognition, a process through which both systems evolve through mutual adaptation. The study positions the RL–KT convergence not merely as a technical innovation but as a philosophical reimagining of pedagogy, cognition, and the future of learning.

This study addresses the durability issues of barcode substrates for photovoltaic (PV) modules under extreme conditions such as high temperature, high humidity, and intense ultraviolet (UV) radiation. A three-layer structured barcode substrate with high-temperature resistance and anti-aging properties was developed, and the die-cutting process was optimized. The three-layer structure consists of a polyethylene terephthalate (PET) substrate layer, a polyimide (PI) anti-aging layer, and a wear-resistant coating. The synergistic effect of these layers significantly enhances the substrate’s temperature resistance and anti-aging performance. Experimental results indicate that after 100 thermal cycles, the tensile strength retention rate of the three-layer structured substrate reaches 92%, much higher than the 65% of traditional PET substrate. After 1000 hours of UV irradiation, the color difference (ΔE) is only 1.5, compared to the 4.0 of traditional PET substrate. Following the optimization of the die-cutting process, the burr rate was reduced from 8% to 0.5%, and the material utilization rate increased from 82% to 95%. In the pilot application at Mingyang Smart and the long-term testing in desert power stations, the three-layer structured barcode substrate demonstrated excellent performance, with a barcode integrity rate of 98%, significantly higher than the 70% of traditional PET substrate. This study provides significant technical support for the development of barcode substrates for PV modules, promoting the high-quality development of the PV industry.

With the advent of the digital age, brand marketing faces unprecedented opportunities and challenges. Traditional brand marketing informationization models can no longer meet the rapidly changing market demands and the diversity of consumer behaviors. This paper explores the intelligent transformation path of brand marketing informationization, analyzing the challenges faced by current brand marketing informationization in data management, technology application, and organizational structure, while also pointing out the innovative opportunities brought by intelligent technologies. Through the case analysis of the BrandScan system, this paper proposes data-driven precision marketing strategies, intelligent technology application strategies, cross-channel integration strategies, and talent and organizational management strategies for transformation. It also elaborates on the implementation steps of intelligent transformation, including formulating transformation plans, infrastructure construction, data management and application system construction, technology application and business process optimization, and talent and organizational culture building. The research results show that intelligent transformation can significantly improve the efficiency and effectiveness of brand marketing and enhance brand competitiveness. This paper aims to provide theoretical guidance and practical references for the future development of brand marketing informationization, promoting innovation and development in the digital age.

The current multimodal sentiment recognition methods fall short in handling dynamic changes in modal weights and modeling modal consistency. Specifically, when processing the MELD dataset, multiple rounds of structured processing and feature optimization were not conducted; meanwhile, the Word2Vec similarity-based sentiment lexicon expansion strategy still falls short in terms of semantic consistency and emotional accuracy. Additionally, in the original experimental setup, the model relied solely on cross-entropy loss for training, overlooking the uncertainties and inconsistencies in information fusion across modalities. Therefore, this project proposes a multimodal question-answering sentiment recognition method based on user preferences. By introducing a multimodal attention mechanism guided by sentiment preferences and sentiment prototypes in a three-dimensional sentiment representation space (Valence-Arousal-Dominance, VAD), the method enhances multimodal information fusion and modeling capabilities for modal consistency. Furthermore, an extended sentiment lexicon strategy and context-dependent modeling mechanism are designed to improve the accuracy and stability of dialogue sentiment recognition. The project conducted systematic ablation and comparative experiments on the standard multimodal dialogue sentiment dataset MELD, demonstrating that the proposed method outperforms existing representative models in accuracy, precision, and F1 score, validating its effectiveness and potential for application in multimodal question-answering sentiment recognition tasks.

With the increasing global demand for energy-saving and efficient lighting, LED lighting technology, as one of the most promising lighting technologies in the 21st century, is rapidly changing the landscape of the traditional lighting industry. Mester LED Ltd., as a global leading provider of LED lighting solutions, is committed to promoting the development and application of LED lighting technology through continuous technological innovation. This paper in-depth studies the innovative achievements of Mester Optoelectronics in LED lighting technology, including its patented technologies, new product development, and successful cases in different application scenarios. By analyzing the company’s technological innovations in high efficiency, energy saving, intelligent control, and reliability, the paper explores how these innovations significantly enhance the performance and market competitiveness of the products and positively drive the development of the entire industry. The research results show that Mester Optoelectronics has not only achieved remarkable success in technological innovation but also applied these technologies to actual products to meet the needs of different customers, providing valuable experience and references for other companies in the industry. This paper provides a new perspective for understanding the innovation path and market application effects of LED lighting technology and offers beneficial insights for the future development direction of LED lighting technology.

The Interpersonal Whole Brain Model of Care™ (IWBMC™) addresses the increasing prevalence of neurodevelopmental disorders through an innovative blend of science and compassionate care. Developed by Amy O’Dell and applied at Jacob’s Ladder School, this model includes seven interconnected elements: Spirit & Will, Neurodevelopmental Methodology, Learning Style, Emotional-Relational-Behavioral Assessments, Physiological Factors, Social Structure, and Whole Person Integration. Rooted in interpersonal neurobiology and positive psychology, the IWBMC™ formulates personalized care plans within a therapeutic ecosystem anchored by hope, truth, and love, recognizing each client not merely as their diagnosis but as “a soul ready to be cared for.”

This paper examines the seasonal variations in light pollution levels in Guangzhou, China, and identifies the key factors driving these fluctuations. It highlights the impact of urban infrastructure, industrial activity, weather conditions, and socioeconomic factors on light pollution levels throughout the year. Seasonal changes in daylight duration, temperature, and rainfall patterns are explored in relation to their influence on the city’s light pollution. The paper also discusses the ecological and health implications of light pollution, focusing on the disruption it causes to wildlife and nocturnal species, as well as the associated risks to human health, including sleep disorders and chronic diseases. Technological solutions, such as the adoption of energy-efficient LED lighting, and regulatory efforts to mitigate light pollution are also considered. Finally, the study outlines potential strategies for managing light pollution in Guangzhou and other rapidly growing urban areas.