Seasonal variations in urban park characteristics and visitation patterns in Atlanta:A big data study using smartphone user mobility

Urban parks are essential for physical activity and health enhancement of local residents. Although previous research has explored the utilization of the urban parks, seasonal oscillations in visitation to small and medium-size urban parks have received scant attention. Utilizing long-time smartphone mobile data, this study investigated park visitation patterns in 84 Atlanta parks. In this article, seasonal variations in park visitation and the correlation between park visiting patterns and park attributes were evaluated. Using fixed-effect regression, we modeled the effects of socioeconomic, climatic, and park attributes on park visiting patterns. The results indicated that park visitor volume and park service areas were significantly influenced by seasonal variations. Facilities, pavements, and scale facilitated visitor volume, while water bodies and landscapes enhanced the park's accessibility from a distance. The landscape design of the park was a significant factor during the summer season, rather than autumn and winter, which were mostly influenced by the availability of playgrounds and recreational facilities. Seasonal patterns of resident visitation varied among parks and interacted with park qualities. This study is expected to inform future design and research of urban parks by addressing specific characteristics of parks and to provide resources in response to dynamic patterns of park access that surpass generic norms.</p

A theoretical study of parietal vortex shedding in Taylor–Culick flow via linear stability analysis

In this theoretical study, we use linear stability analysis to investigate the cause of parietal vortex shedding in Taylor–Culick flow, which is representative of the flow in solid rocket motors. We focus on the effects of the lateral-injection Reynolds number and the length-to-radius ratio of the combustion chamber. Through a comparison with pipe flow, we find that flow turning is a major contributor to parietal vortex shedding. We explore the role of amphidromic points and find that they can divide the flow field into two distinct regions, an outer region with strong perturbations and an inner region with weak perturbations. In the outer region, we find that the velocity perturbations develop advection patterns with axial (streamwise) periodicity, while the pressure perturbations induce flow gradients that enhance shear stresses. Collectively, these effects are thought to combine to induce parietal vortex shedding in solid rocket motors

Balancing accuracy and user satisfaction: the role of prompt engineering in AI-driven healthcare solutions

IntroductionThe rapid evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) has opened new possibilities for public healthcare. Effective integration of these technologies is essential to ensure precise and efficient healthcare delivery. This study explores the application of IoT-enabled, AI-driven systems for detecting and managing Dry Eye Disease (DED), emphasizing the use of prompt engineering to enhance system performance.MethodsA specialized prompt mechanism was developed utilizing OpenAI GPT-4.0 and ERNIE Bot-4.0 APIs to assess the urgency of medical attention based on 5,747 simulated patient complaints. A Bidirectional Encoder Representations from Transformers (BERT) machine learning model was employed for text classification to differentiate urgent from non-urgent cases. User satisfaction was evaluated through composite scores derived from Service Experiences (SE) and Medical Quality (MQ) assessments.ResultsThe comparison between prompted and non-prompted queries revealed a significant accuracy increase from 80.1% to 99.6%. However, this improvement was accompanied by a notable rise in response time, resulting in a decrease in SE scores (95.5 to 84.7) but a substantial increase in MQ satisfaction (73.4 to 96.7). These findings indicate a trade-off between accuracy and user satisfaction.DiscussionThe study highlights the critical role of prompt engineering in improving AI-based healthcare services. While enhanced accuracy is achievable, careful attention must be given to balancing response time and user satisfaction. Future research should optimize prompt structures, explore dynamic prompting approaches, and prioritize real-time evaluations to address the identified challenges and maximize the potential of IoT-integrated AI systems in medical applications

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmarks | OpenLane-V2 Dataset: https://github.com/OpenDriveLab/OpenLane-V

lncRNA LOC100911717-targeting GAP43-mediated sympathetic remodeling after myocardial infarction in rats

ObjectiveSympathetic remodeling after myocardial infarction (MI) is the primary cause of ventricular arrhythmias (VAs), leading to sudden cardiac death (SCD). M1-type macrophages are closely associated with inflammation and sympathetic remodeling after MI. Long noncoding RNAs (lncRNAs) are critical for the regulation of cardiovascular disease development. Therefore, this study aimed to identify the lncRNAs involved in MI and reveal a possible regulatory mechanism.Methods and resultsM0- and M1-type macrophages were selected for sequencing and screened for differentially expressed lncRNAs. The data revealed that lncRNA LOC100911717 was upregulated in M1-type macrophages but not in M0-type macrophages. In addition, the lncRNA LOC100911717 was upregulated in heart tissues after MI. Furthermore, an RNA pull-down assay revealed that lncRNA LOC100911717 could interact with growth-associated protein 43 (GAP43). Essentially, immunofluorescence assays and programmed electrical stimulation demonstrated that GAP43 expression was suppressed and VA incidence was reduced after lncRNA LOC100911717 knockdown in rat hearts using an adeno-associated virus.ConclusionsWe observed a novel relationship between lncRNA LOC100911717 and GAP43. After MI, lncRNA LOC100911717 was upregulated and GAP43 expression was enhanced, thus increasing the extent of sympathetic remodeling and the frequency of VA events. Consequently, silencing lncRNA LOC100911717 could reduce sympathetic remodeling and VAs

DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model

Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird\u27s-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps

Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery

AbstractWith the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.</jats:p

Applied machine learning in intelligent systems: knowledge graph-enhanced ophthalmic contrastive learning with “clinical profile” prompts

IntroductionThe integration of artificial intelligence (AI) into ophthalmic diagnostics has the potential to significantly enhance diagnostic accuracy and interpretability, thereby supporting clinical decision-making. However, a major challenge in AI-driven medical applications is the lack of transparency, which limits clinicians’ trust in automated recommendations. This study investigates the application of machine learning techniques by integrating knowledge graphs with contrastive learning and utilizing “clinical profile” prompts to refine the performance of the ophthalmology-specific large language model, MeEYE, which is built on the CHATGLM3-6B architecture. This approach aims to improve the model’s ability to capture clinically relevant features while enhancing both the accuracy and explainability of diagnostic predictions.MethodsThis study employs a novel methodological framework that incorporates domain-specific knowledge through knowledge graphs and enhances feature representation using contrastive learning. The MeEYE model is fine-tuned with structured clinical knowledge, enabling it to better distinguish subtle yet significant ophthalmic features. Additionally, “clinical profile” prompts are incorporated to further improve contextual understanding and diagnostic precision. The proposed method is evaluated through comprehensive performance benchmarking, including quantitative assessments and clinical case studies, to ensure its efficacy in real-world ophthalmic diagnosis.ResultsThe experimental findings demonstrate that integrating knowledge graphs and contrastive learning into the MeEYE model significantly improves both diagnostic accuracy and model interpretability. Comparative analyses against baseline models reveal that the proposed approach enhances the identification of ophthalmic conditions with higher precision and clarity. Furthermore, the model’s ability to generate transparent and clinically relevant AI recommendations is substantiated through rigorous evaluation, highlighting its potential for real-world clinical implementation.DiscussionThe results underscore the importance of explainable AI in medical diagnostics, particularly in ophthalmology, where model transparency is critical for clinical acceptance and utility. By incorporating domain-specific knowledge with advanced machine learning techniques, the proposed approach not only enhances model performance but also ensures that AI-generated insights are interpretable and reliable for clinical decision-making. These findings suggest that integrating structured medical knowledge with machine learning frameworks can address key challenges in AI-driven diagnostics, ultimately contributing to improved patient outcomes. Future research should explore the adaptability of this approach across various medical domains to further advance AI-assisted diagnostic systems

Investigating the efficacy of a novel therapeutic to mitigate traumatic brain injury : contributions of environmental exposures to overall healing.

Traumatic brain injury (TBI) is a leading cause of disability and premature death among both civilians and military. Morbidity and deaths are mainly caused by several secondary process that exacerbate brain dysfunction in the minutes to days following the primary injury when blood vessels and tissues are torn, stretched, or compressed. In previous studies, proper oxygen supply has been shown to help brain cells to grow and repair, remove the obstruction in blood flow, and alleviate brain edema to prevent secondary injury. OX-66, a novel therapeutic, potentially provides an efficient supply of oxygen to the cells. This therapeutic was investigated in this study to determine its cytotoxicity and potential mechanism of cellular repair in invitro-injured rat brain cells. The effects of exposure to polycyclic aromatic hydrocarbons (PAH) on TBI patients and the corresponding restorative influence of OX-66 were also evaluated

Changes of Fruit Abscission and Carbohydrates, Hormones, Related Gene Expression in the Fruit and Pedicel of Macadamia under Starvation Stress

In order toexplore the regulation mechanism of macadamia fruitlet abscission induced by ‘starvation stress’, a treatment of girdling and defoliation was applied to the bearing shoots of macadamia cultivar ‘H2’ at the early stage of fruit development, simulating the starvation stress induced by interrupting carbon supply to fruit. The levels of carbohydrates, hormones, and related gene expression in the different tissues (husk, seed, and pedicel) were investigated after treatment. The results showed that a severe fruit drop occurred 3~5 d after starvation stress treatment. The contents of glucose, fructose, and sucrose in both the husk and the seed were significantly decreased, as well as the fructose and sucrose in the pedicel; this large reduction occurred prior to the massive fruit shedding. Starvation stress significantly reduced the GA3 and ZR contents and enhanced the ABA level in the pedicel and the seed, whereas it did not obviously change these hormones in the husk. After treatment, IAA content decreased considerably in both the husk and seed but increased remarkably in the pedicel. In the husk, the expression of genes related to sugar metabolism and signaling (NI, HXK2, TPS, and TPP), as well as the biosynthesis of ethylene (ACO2 and ACS) and ABA (NCED1.1 and AAO3), was significantly upregulated by starvation stress, as well as the stress-responsive transcription factors (AP2/ERF, HD-ZIP12, bZIP124, and ABI5), whereas the BG gene associated with ABA accumulation and the early auxin-responsive genes (Aux/IAA22 and GH3.9) were considerably suppressed during the period of massive fruit abscission. Similar changes in the expression of all genes occurred in the pedicel, except for NI and AP2/ERF, the expression of which was significantly upregulated during the early stage of fruit shedding and downregulated during the period of severe fruit drop. These results suggest that complicated crosstalk among the sugar, IAA, and ABA signaling may be related to macadamia fruitlet abscission induced by carbohydrate starvation