Alzheimer's disease (AD) is a severe degenerative neurological disease which leads to progressive cognitive decline and memory impairment. We have affirmed amyloid-β (Aβ) plaques and tau tangles as the primary pathological hallmarks for a long time. However, many therapeutic approaches have found out that both Aβ and tau alone have achieved little success. Increasing evidence indicates that the neuroinflammation, which primarily related with microglia and astrocytes, takes the central role of causing and exacerbating the disease. Microglia and astrocytes not only respond to Aβ and tau but also control their accumulation, propagation, and associated neurotoxicity. They are also influenced by a kind ofrisky gene related to late-onset AD calls ApoE, which can further modulate them by regulating lipid metabolism and glial responses. This review synthesizes current knowledge on the roles of microglia, astrocytes, Aβ, tau, and ApoE in AD, emphasizing their interconnected contributions to pathogenesis and therapeutic opportunities.

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This study used information entropy as the core mathematical tool and focused on six typical global cultural scales: the Chinese pentatonic scale, the Indian 22-shruti scale, the Arabic 17 tone and other rhythmic scales, the European natural major scale, the Japanese mode scale, and the Indian pentatonic scale. By quantifying the uncertainty of interval distribution, the mathematical laws behind different cultural music aesthetics have been revealed. This study first constructed a frequency system for each scale and conducted interval statistics. Then, the Shannon entropy formula was used to calculate the entropy value, and the entire process was automatically calculated through Python programming. Finally, the deep logic of entropy value differences was explained in combination with cultural background. The results indicate that entropy is highly correlated with the number of core notes and the diversity of intervals in the scale: low entropy scales (2.0-2.8 bits) correspond to the cultural aesthetics of regular harmony, while high entropy scales (3.1-3.7 bits) relate to improvisation and diverse music styles, providing a quantitative analysis framework for cross-cultural music research.

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Blood oxygen saturation is a critical physiological parameter for evaluating human cardiopulmonary function and blood oxygen-carrying capacity. Non-invasive pulse oximeters based on the principle of photoplethysmography (PPG) have been widely used in clinical monitoring and home health care due to their simple operation and rapid response. This paper reviews the core technologies of pulse oximeters, focusing on the operating principles of their photoelectric sensor modules, hardware circuit design, key performance indicators, and the challenges they face. The article discusses in detail the differences between transmissive and reflective sensors, as well as the design of key circuits in signal processing such as amplification, filtering, and analog-to-digital conversion. Furthermore, it points out the impact of factors such as motion artifacts, low perfusion, and skin color differences on measurement accuracy. Finally, the future development of oximeters towards intelligence, multi-functionality, and high robustness is prospected. This paper aims to provide a theoretical reference for the design optimization and performance improvement of oximeters.

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Urban Transportation Network Design (TND) is a highly complex system engineering. It has always been regarded as a very difficult problem in urban planning and transportation. This study tries to sort out and summarize the existing research system and development process in this field. From the perspective of mathematical modeling, the multi-objective property, nonlinear characteristics, uncertainty and NP-hard computational complexity of the TND problem are explained one by one, and a series of solution difficulties derived from them also appear. Classical optimization methods, such as linear programming, integer programming, bi-level programming models and so on, have advantages in solving structured problems. But their limitations in large-scale dynamic situations are also pointed out. In comparison, new intelligent and simulation optimization methods, such as heuristic algorithms, data-driven modeling strategies and simulation-optimization coupling frameworks, provide more potential ways to solve complex traffic network design problems. The current mainstream research paradigm can be summarized as the progressive process of "theoretical analysis—optimization solution—simulation verification". Different tools work together in this process to deepen the understanding of the problem and improve the solution efficiency. Network planning with dynamic and multiple uncertainties still faces many unsolved difficulties. Big data technology, artificial intelligence methods and the interdisciplinary integration may become the key driving forces for the future breakthrough in the TND field. This review aims to provide basic theoretical reference and framework support for the follow-up related research.

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