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Abstract Background This study aimed to assess the public knowledge regarding Alzheimer’s Disease (AD) in Zhuhai, China, focusing on identifying knowledge gaps and the influence of demographic and health factors. Methods A cross-sectional study was conducted in Zhuhai, China, from October to November 2022. A total of 1986 residents from 18 communities were selected employing stratified multi-stage equi-proportional sampling. Questionnaires covering general information and the Alzheimer’s Disease Knowledge Scale (ADKS) were investigated face-to-face. Ordinal multiclass logistic regression was applied to assess the relationship between AD awareness and demographic and health characteristics. Results The average ADKS score was 18.5 (SD = 3.36) in Zhuhai. The lowest awareness rates were observed in the “Symptoms” and “Caregiving” subdomains of ADKS, with rates of 51.01% and 43.78%, respectively. The correct rates for the 30 ADKS questions ranged from 16.62 to 92.6%, showing a bimodal pattern with clusters around 80% and 20%. Women (OR = 1.203, 95% CI: 1.009–1.435), individuals aged 60 years or older (OR = 2.073, 95% CI: 1.467–2.932), those living in urban areas (OR = 1.361, 95% CI: 1.117–1.662), higher average monthly household income per capita (OR = 1.641, 95% CI: 1.297–2.082), and without any neurological or mental disorders (OR = 1.810, 95% CI: 1.323–2.478) were more likely to have higher levels of awareness about Alzheimer’s disease. Conclusions Adults in Zhuhai show a limited knowledge of AD, particularly in the ‘Symptoms’ and ‘Caregiving’ subdomains. Upcoming health campaigns must focus on bridging the knowledge gaps in different subdomains of AD, especially among subgroups with lower awareness, as identified in our study.

The proliferation of Internet of Things (IoT) devices has resulted in a massive increase in data generation, necessitating robust solutions for real-time data processing and analysis. The integration of Unmanned Aerial Vehicles (UAVs) with MEC systems presents a promising enhancement, providing dynamic, mobile edge computing capabilities that can adapt to changing conditions and demands. However, efficiently managing the task offloading and trajectory planning of UAVs in such scenarios poses significant challenges, particularly in maximizing coverage while minimizing time and energy consumption. In this context, this paper proposes a novel UAV-based MEC model utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm to optimize task scheduling and UAV trajectory in real-time. Our model enables UAVs to function as mobile edge servers, dynamically processing and routing tasks between terminal devices and edge servers based on real-time device demands. By incorporating a sophisticated reward function that prioritizes the minimization of system costs-including energy, time, and throughput maximization-our approach not only enhances the operational efficiency of UAV-assisted MEC systems but also improves the quality and responsiveness of edge computing services. After training the MADDPG model in the simulated environment, the experiments show that our approach significantly improved the performance of UAV-assisted MEC systems in real-time scenarios, converging after 50 learning episodes and achieving 90% task completion rate.

Abstract Background Polycystic ovary syndrome (PCOS) is an endocrine-related follicular developmental disorder that affects 50 %-70 % of reproductive-aged women diagnosed with ovulation-related infertility. Abnormal proliferation and apoptosis of granulosa cells (GCs) are thought to be the critical factors leading to abnormal maturation of follicles. It has been shown that microRNAs (miRNAs) exert a significant influence in the pathogenesis of PCOS; however, the relationship between miRNA, PCOS, and GC apoptosis is not entirely understood. Methods To clarify the effect of miR-194 in PCOS, CCK-8, Ki67 staining, AO/EB, and flow cytometry assays were used to assess cell growth, proliferation, and apoptosis in KGN cells, which were artificially stimulated to overexpress miR-194. Luciferase reporter assays and rescue experiments were used to elucidate the mechanism underlying miR-194 in PCOS. Results miR-194 expression was significantly up-regulated in rat models of PCOS and the ovarian GCs of PCOS patients. miR-194 suppression promoted KGN cell growth and proliferation. miR-194 overexpression also induced cell apoptosis, while miR-194 downregulation had an opposite effect. Furthermore, up-regulating heparin-binding EGF-like growth factor (HB-EGF) expression rescued the pro-apoptotic effects of miR-194 upregulation on KGN cells. Conclusions miR-194 is increased in PCOS granulosa cell and may function as a novel biomarker and therapeutic target for KGN cells via HB-EGF regulation.

Abstract Several formulae for computing the direct coupling efficiencies from laser diodes to diffused channel waveguides, based on mathematic models for approximating the corresponding laser beams and the waveguiding modes, have been derived in this paper. Then, the authors utilize a simple shift-or-shrink (SOS) algorithm to seek their optimal coupling conditions, including the incident positions of the laser beams and the corresponding horizontal and vertical beam widths. The simulation results are presented and compared.

The theory of spatial thinking is relevant to the learning and teaching of many academic domains. One promising method to facilitate learners' higher-order thinking is to utilize a web map mind tool to assist learners in applying spatial thinking to cooperative problem solving. In this study, an environment is designed based on the theory of spatial thinking and developed to help learners engage in project-based learning that utilizes the proposed web map mind tool for tour planning. The participants were 66 college students, and a quasi-experimental research design was employed. This study examined the differences in the learning performance and project performance between participants using the web map mind tool (the experimental group) and ordinary Internet search engines (the control group). The results indicate the experimental group performed significantly better than the control group in terms of both learning performance and project performance. In addition, we found that the students in the two groups held a positive learning attitude toward the project activity of tour planning. In terms of students' attitude toward the use of a mind tool, this study only conducted a survey for the experimental group. The results indicated that learners in the experimental group held a positive attitude toward the use of the web map mind tool. [ABSTRACT FROM AUTHOR]