This article focuses on artificial intelligence in educational technology, starting with an introduction to educational technology, an interdisciplinary field of study that covers the design, development, utilization, and evaluation of technology and digital tools in educational Settings. A detailed description of its definition in an academic context - a multidisciplinary field of computer science and cognitive science that deals with the development of computational systems that exhibit intelligent behaviour, describing its areas of coverage and scope of application. It then introduces the benefits of AI in education technology, specifically addressing personalized learning, adaptive learning systems, automated scoring and feedback, virtual tutors and chatbots, data analytics, as well as content recommendations and natural language processing, accessibility and inclusion. Then it introduces the main concepts of AI personalized learning: AI personalized learning uses the power of artificial intelligence to meet the unique needs and preferences of individual learners, as well as its key principles and main characteristics. Adaptive learning systems harness the power of artificial intelligence and data analytics to tailor the learning experience to each student's needs and abilities, as well as its core principles and strengths. It also introduces the main operating principles of automated grading and feedback: it uses artificial intelligence and computer algorithms to evaluate and evaluate student assignments, tests and exams without the direct involvement of human graders, and its associated benefits. Secondly, the main concepts of virtual tutor are introduced: Virtual tutor is a computer-based system that uses artificial intelligence and machine learning algorithms to provide students with personalized and interactive educational support and its key characteristics and main advantages, and then the nature and benefits of chatbots and help in the field of education. The final conclusion summarizes the benefits and future challenges of integrating AI into educational technology.

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

Neurodegenerative diseases (NDDs) are a class of cognitive and motor disorders that include Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), and others. They are caused by lesions in cells and tissues of the central nervous system, resulting in corresponding dysfunctions and consequent decline in cognitive and motor functions. Neural tissues are extremely vulnerable to oxidative stress, which plays critical biological roles in NDDs. Aromatic compounds are found extensively in natural plants and have substantial effects of anti-oxidative stress damage, which not only have a wide range of research applications in cosmetics, foods, etc., but are also frequently utilized in the treatment of various central nervous system diseases. This review summarizes the relevant oxidative stress mechanisms in NDDs (AD, PD, HD, and ALS) and reviews aromatic compounds such as polyphenols, terpenoids, and flavonoids that can be used in the management of neurodegenerative diseases, as well as their specific mechanisms of antioxidant action. This review will serve as a reference for future experimental studies on neurodegenerative illnesses while also offering fresh insights into clinical therapy.

Aiming at the APF algorithm in the collision-free path planning and LQR algorithm tracking process, there are problems such as unreachable target points, local optimization, low safety coefficient of autonomous lane change collision avoidance tracking control and poor stability, this paper proposes an optimized APF method, which solves the defects of the traditional artificial potential field method by adding the distance adjustment factor, the safety distance protection model and the sub-target virtual potential field, and at the same time generates the autonomous driving car collision-free path. The path tracking control system is designed based on feed-forward control and DLQR controller, in which the vehicle dynamics state space model is used as the basis, the model is linearized and discretized, and the corresponding constraint variables are set to minimize the control command objective cost function under the constraints, and the objective of the DLQR controller.The objective is to accurately determine the steering angle in order to achieve path tracking and eliminate the lateral error between the reference path and the actual path caused by external interference.Finally, the simulink model and CarSim parameter configuration joint simulation platform are built, and different obstacle scenarios and different speeds are selected for collision-free path planning and tracking motion simulation research and analysis, which verifies the accuracy and practicability of the algorithm, and shows more obvious advantages in obstacle avoidance ability, path smoothing degree, and collision avoidance safety stability. The ideas proposed in this paper are expected to be further extended and popularized to actual path planning problems, which has important practical significance and engineering value.

Four previously undescribed highly oxygenated diterpenoids (1–4), zeylleucapenoids A–D, characterized by halimane and labdane skeletons, were isolated from the aerial parts of Leucas zeylanica. Their structures were elucidated primarily by NMR experiments. The absolute configuration of 1 was established by theoretical ECD calculations and X-ray crystallographic analysis, whereas those for 2−4 were assigned by theoretical ORD calculations. Zeylleucapenoid D (4), with an IC₅₀ value of 38.45 μM in RAW264.7 macrophages and nontoxic activity for zebrafish embryo, and obviously inhibited pro-inflammatory cytokines TNF-α and IL-6 in a dose-dependent manner. Compound 4 inhibited of the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Furthermore, molecular docking analysis indicated the possible mechanism of action for 4 may be bind to targets by hydrogen- and hydrophobic- bond interactions.