Search Results (12,138)

Urban forest parks offer valuable spaces for walking activities that benefit both physical and mental health. However, trails in current park designs are often underutilised, and the scene layout does not fully meet the preferences of walkers. Therefore, understanding the connection between scene characteristics and walking preferences is essential. This study aimed to develop an ensemble protocol to assess the role of scene characteristics in walking preferences, using Shanghai Gongqing Forest Park as an illustrative example. A walking preference heat map was created using a combination of crowdsourced GPS data. The scene characteristics were quantified using panoramic photographs, drone orthophotos, computer vision, and deep learning techniques. Taking spatial dependence into account, the key findings include the following: (1) From an overhead view, the shortest paths, waterbody density, and recreational facility selection positively influenced walking preferences, while secondary asphalt trails had a negative effect. (2) At the eye level, aesthetically pleasing landscape elements, such as flowers and bridges, attracted more pedestrians, while closed trails were less favoured. (3) Eye-level features explained 43.5% of the variation in walking preference, with a stronger influence on walking preference compared to 22.4% for overhead features. (4) Natural elements were generally more significant than artificial ones; the feature ranking of significant impact was flowers > NACHr1000 > visual perception > water body density > bridge > SVF > retail > entertainment > asphalt. This study proposes a flexible protocol that provides urban forest park managers and planners with practical tools to create a more walker-friendly environment and more accurate trail alignment, as well as a solid empirical basis for assessing the use of urban forest parks. Full article

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the “fuzzy iceberg lattice” and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a ‘fuzzy min-max basis algorithm’ to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice’s ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules. Full article

Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represented in many African languages. While state-of-the-art Afrocentric pre-trained language models (PLMs) have been developed for various natural language processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain largely unexplored. In this study, we propose a novel approach that combines Afrocentric PLMs with XAI techniques for sentiment analysis. We demonstrate the effectiveness of incorporating attention mechanisms and visualization techniques in improving the transparency, trustworthiness, and decision-making capabilities of transformer-based models when making sentiment predictions. To validate our approach, we employ the SAfriSenti corpus, a multilingual sentiment dataset for South African under-resourced languages, and perform a series of sentiment analysis experiments. These experiments enable comprehensive evaluations, comparing the performance of Afrocentric models against mainstream PLMs. Our results show that the Afro-XLMR model outperforms all other models, achieving an average F1-score of 71.04% across five tested languages, and the lowest error rate among the evaluated models. Additionally, we enhance the interpretability and explainability of the Afro-XLMR model using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These XAI techniques ensure that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in AI-driven NLP technologies, particularly in the context of African languages. Full article

Background/Objectives: Facial asymmetry is gaining an increasing diagnostic interest in many clinical contexts. Several three-dimensional surface-based methods have been proposed for its assessment; however, they might provide non-equivalent data. Since there is a lack of comparative studies in these terms, this study aims to compare three methods for assessing the asymmetry of the face and facial thirds, thus addressing whether the potential differences can be considered clinically acceptable or not. Methods: Two ‘maxillofacial’ methods based on the trigeminal nerve distribution and one ‘orthodontic’ method based on reference horizontal planes were used to identify the facial thirds on 3D facial models of 80 Italian healthy adults to calculate the asymmetry of the face, and the upper, middle, and lower thirds of the face differently selected by each method. As a measure of asymmetry, the Root Mean Square value was calculated through a mirroring surface-based registration. Intra- and inter-operator reliability was verified for each method. Differences and interchangeability between the methods were tested, respectively, by two-way repeated measures ANOVA (Analysis of Variance) and Bland–Altman and Similarity Percentage model analysis. Additionally, the time required to perform each method was assessed. Results: All methods demonstrated excellent intra- and inter-operator reliability. While the ANOVA analysis found significant differences (p < 0.001) for the majority of facial Regions of Interest between each method, the Bland–Altman analysis revealed that the differences were clinically acceptable (<0.50 mm) for all facial regions between the trigeminal methods, and for the face and the upper third of the face between the orthodontic method, which was revealed to be faster, and the trigeminal ones. The additional similarity percentage model provided visual support for the complete interchangeability of the two trigeminal methods, as evidenced by the lower Coefficient of Variation value. Conclusions: There is no best method for assessing facial asymmetry that applies to all types of clinical settings, as we have shown that different methods may not be completely interchangeable. However, we suggest that the methods based on the trigeminal subdivision can be used interchangeably in contexts where the morpho-functional analysis of maxillofacial regions with different embryological origins is considered. Thus, the clinical setting imposes the choice of one method over another and, as we have pointed out, the consequent comparison of data with those obtained with methods whose interchangeability has been demonstrated. Full article

Although imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has further expanded the impact of imbalanced data, so studying imbalanced data classification is of practical significance. We propose an image oversampling algorithm based on the influence function and sample weights. Our scheme not only synthesizes high-quality minority class samples but also preserves the original features and information of minority class images. To address the lack of visually reasonable features in SMOTE when synthesizing images, we improve the pre-training model by removing the pooling layer and the fully connected layer in the model, extracting the important features of the image by convolving the image, executing SMOTE interpolation operation on the extracted important features to derive the synthesized image features, and inputting the features into a DCGAN network generator, which maps these features into the high-dimensional image space to generate a realistic image. To verify that our scheme can synthesize high-quality images and thus improve classification accuracy, we conduct experiments on the processed CIFAR10, CIFAR100, and ImageNet-LT datasets. Full article

The plasmasphere within Earth’s magnetosphere plays a crucial role in space physics, with its electron density distribution being pivotal and strongly influenced by solar activity. Very Low Frequency (VLF) waves, including whistlers, provide valuable insights into this distribution, making the study of their propagation through the plasmasphere essential for predicting space weather impacts on various technologies. In this study, we evaluate the performance of different deep learning model sizes for lightning whistler detection using the YOLO (You Only Look Once) architecture. To achieve this, we transformed the entirety of raw data from the Arase (ERG) Satellite for August 2017 into 2736 images, which were then used to train the models. Our approach involves exposing the models to spectrogram diagrams—visual representations of the frequency content of signals—derived from the Arase Satellite’s WFC (WaveForm Capture) subsystem, with a focus on analyzing whistler-mode plasma waves. We experimented with various model sizes, adjusting epochs, and conducted performance analysis using a partial set of labeled data. The testing phase confirmed the effectiveness of the models, with YOLOv5n emerging as the optimal choice due to its compact size (3.7 MB) and impressive detection speed, making it suitable for resource-constrained applications. Despite challenges such as image quality and the detection of smaller whistlers, YOLOv5n demonstrated commendable accuracy in identifying scenarios with simple shapes, thereby contributing to a deeper understanding of whistlers’ impact on Earth’s magnetosphere and fulfilling the core objectives of this study. Full article

The emerging usage of digitalized histopathological images is leading to a novel possibility for data analysis. With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. This enables algorithms to count, measure, or evaluate those areas when trained properly. To achieve suitable training, datasets must be annotated and curated by users in programs like QuPath. The extraction of this data for artificial intelligence algorithms is still rather tedious and needs to be saved on a local hard drive. We developed a toolkit for integration into existing pipelines and tools, like U-net, for the on-the-fly extraction of annotation tiles from existing QuPath projects. The tiles can be directly used as input for artificial intelligence algorithms, and the results are directly transferred back to QuPath for visual inspection. With the toolkit, we created a convenient way to incorporate QuPath into existing AI workflows. Full article

Vitamin D insufficiency and deficiency are highly prevalent in patients with chronic kidney disease (CKD), and their pharmacokinetics are not well described. The primary study objective was to develop a population pharmacokinetic model of oral cholecalciferol (VitD₃) and its three major metabolites, 25-hydroxyvitamin D₃ (25D₃), 1,25-dihydroxyvitamin D₃ (1,25D₃), and 24,25-dihydroxyvitamin D₃ (24,25D₃), in CKD patients with vitamin D insufficiency and deficiency. CKD subjects (n = 29) were administered one dose of oral VitD₃ (5000 I.U.), and nonlinear mixed effects modeling was used to describe the pharmacokinetics of VitD₃ and its metabolites. The simultaneous fit of a two-compartment model for VitD₃ and a one-compartment model for each metabolite represented the observed data. A proportional error model explained the residual variability for each compound. No assessed covariate significantly affected the pharmacokinetics of VitD₃ and metabolites. Visual predictive plots demonstrated the adequate fit of the pharmacokinetic data of VitD₃ and metabolites. This is the first reported population pharmacokinetic modeling of VitD₃ and metabolites and has the potential to inform targeted dose individualization strategies for therapy in the CKD population. Based on the simulation, doses of 600 International Unit (I.U.)/day to 1000 I.U./day for 6 months are recommended to obtain the target 25D₃ concentration of between 30 and 60 ng/mL. These simulation findings could potentially contribute to the development of personalized dosage regimens for vitamin D treatment in patients with CKD. Full article

This study analyzes valuable information (image feature information) from pixel-based images to systematize the form generation process utilizing this information. Information in architecture is mainly used as an analytical tool for functional design optimization. However, the Fourth Industrial Revolution and advances in information and communication technology have positioned information as a core value-creating tool. Consequently, there is a growing need to develop methodologies for effectively applying information to architectural design generation. This study combines digital technology with images, which directly influence architectural design, transforming them into numerical information. By systematizing the three-dimensional form generation process using low-level image feature information, it analyzes the relationship between the characteristics of valuable information and the generated forms. This approach helps identify the interrelationships between the characteristics of valuable information and the generated forms, the complementarity needed to mitigate the limitations of valuable information, and the potential for architectural applications of the three-dimensional form generation methodology utilizing image information. This approach reduces labor and enhances architectural aesthetics by instantly generating diverse early design alternatives. Such research can provide a more logical basis for avoiding intuitive judgments during the design decision-making process. It can also lay the groundwork for information-centric design systems in architecture. Full article

Background and Objectives: The objective of the study is to determine whether there is a difference in physical therapy outcomes in patients with cervical and/or lumbar pain who have insomnia compared to patients without insomnia during a two-week period of active treatment under the conditions of routine clinical practice. Materials and Methods: The study population consisted of two groups of subjects with chronic back pain, a group with insomnia (“case”) with a total of 38 subjects and a control group without insomnia (“control”) with a total of 41 subjects, who filled out a set of measurement questionnaires: the McGill Pain Questionnaire and its short form (SF-MPQ), the Insomnia Severity Index (ISI) and the European Quality of Life Questionnaire of Life (Euro Qol; EQ-5D). Determination of the biomarkers of structural damage to the nervous tissue, neurofilament polypeptide (NEF—neurofilament polypeptide), neuron-specific enolase (NSE—neuron-specific enolase) and protein S100B was performed by measuring their concentrations in the blood using the ELISA method (enzyme immunosorbent assay). Statistical analysis of the collected data included a descriptive analysis, hypothesis testing methods and univariable and multivariable regression models. Results: At the end of the treatment visits, the level of pain remained higher in some subjects of the experimental group, but the statistical significance of the baseline difference disappeared because of the higher relative treatment response in the controls. Measured with a visual analogue scale, the treatment improved the patients’ quality of life much more in experimental than control subjects, as is proven by the statistically significant difference for the percent change from baseline (~31% vs. ~14%). At baseline, all three neurotropic biomarkers had significantly higher serum values in the subjects of the experimental group than in the control patients, which suggested more damage to the neuronal structures. During the treatment course, their serum concentrations decreased, from 36% to 95%, but for S100B, unlike NES and NEF, there was no statistically significant difference between the study groups at the end of the treatment visits. Conclusions: The results of the study have immediate scientific and practical significance because they contribute to new knowledge about the place and role of insomnia in patients with cervical and/or lumbar pain who are treated with physical medicine methods in the conditions of routine clinical practice. The treatment of insomnia should be an indispensable part of therapeutic treatment for patients with back pain. Full article

Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32. Full article

Background: Front-of-package labeling (FOPL) is shown to support healthier consumer choices. Many countries have adopted different FOPL systems. Objective: This study explored perceptions and understanding of three FOPLs and identified features that could enhance their effectiveness in Kenya. Methods: A qualitative study was conducted across four Kenyan counties—Nairobi, Mombasa, Garissa, and Kisumu. Data from 12 focus group discussions with 137 adults of diverse socio-demographic backgrounds were analysed. Participants evaluated three FOPLs: Red and Green (RG) Octagon, Red and Green Octagon with icons and text (RGI), and Black Octagon Warning Label (WL). The FGDs assessed visibility and memorability, comprehension, potential effectiveness, and cultural relevance of each label. NVivo version 14.0 was used for thematic analysis. Results: Kenyan consumers had mixed perceptions of the proposed FOPLs. The black Octagon WL was found to be the most visible and memorable due to its bright colours. Although the RG and RGI symbols were visually engaging, some participants reported confusion with the colour meanings. The WL was also more readily understood due to its text. Overall, WL was preferred for its potential to influence purchasing decisions, while all three FOPLs were considered culturally suitable. Conclusions: The Black Octagon Warning Label was the most visible and comprehensible of the three FOPLs and shows promise in influencing consumer behaviour in Kenya. While RG and RGI symbols are appealing, their colour scheme could reduce their effectiveness. Educating consumers on FOPLs could enhance their impact in reducing unhealthy food purchases. Full article

Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift. To tackle this problem, this paper introduces a state estimation algorithm that integrates an invariant extended Kalman filter (InEKF) with a disturbance observer, aiming to estimate the motion state of quadruped robots on non-stationary terrains. Firstly, foot-end slippage is modeled as a deviation in body velocity and explicitly included in the state equations, allowing for a more precise representation of how slippage affects the state. Secondly, the state update process integrates both foot-end velocity and position observations to improve the overall accuracy and comprehensiveness of the estimation. Lastly, a foot-end contact probability model, coupled with an adaptive covariance adjustment strategy, is employed to dynamically modulate the influence of the observations. These enhancements significantly improve the filter’s robustness and the accuracy of state estimation in non-stationary terrain scenarios. Experiments conducted with the Jueying Mini quadruped robot on various non-stationary terrains show that the enhanced InEKF method offers notable advantages over traditional filters in compensating for foot-end slippage and adapting to different terrains. Full article

Regular on-site inspection is crucial for promptly detecting faults in water supply networks (WSNs) and auxiliary facilities, significantly reducing leakage risks. However, the fragmentation of information and the separation between virtual and physical networks pose challenges, increasing the cognitive load on inspectors. Furthermore, due to the lack of real-time computation in current research, the effectiveness in detecting anomalies, such as leaks, is limited, hindering its ability to provide immediate and direct-decision support for inspectors. To address these issues, this research proposes a mixed reality (MR) inspection method that integrates multi-source information, combining building information modeling (BIM), Internet of Things (IoT), monitoring data, and numerical simulation technologies. This approach aims to achieve in situ visualization and real-time computational capabilities. The effectiveness of the proposed method is demonstrated through case studies, with user feedback confirming its feasibility. The results indicate improvements in inspection task performance, work efficiency, and standardization compared to traditional mobile terminal-based methods. Full article

Underwater vision data facilitate a variety of underwater operations, including underwater ecosystem monitoring, topographical mapping, mariculture, and marine resource exploration. Conventional laser-based underwater imaging systems with complex system architecture rely on high-cost laser systems with high power, and software-based methods can not enrich the physical information captured by cameras. In this manuscript, a low-cost modulated laser-based imaging system is proposed with a spot in the shape of a square ring to eliminate the overlap between the illumination light path and the imaging path, which could reduce the negative effect of backscatter on the imaging process and enhance imaging quality. The imaging system is able to achieve underwater imaging at long distance (e.g., 10 m) with turbidity in the range of 2.49 to 7.82 NTUs, and the adjustable divergence angle of the laser tubes enables the flexibility of the proposed system to image on the basis of application requirements, such as the overall view or partial detail information of targets. Compared with a conventional underwater imaging camera (DS-2XC6244F, Hikvision, Hangzhou, China), the developed system could provide better imaging performance regarding visual effects and quantitative evaluation (e.g., UCIQUE and IE). Through integration with the CycleGAN-based method, the imaging results can be further improved, with the UCIQUE increased by 0.4. The proposed low-cost imaging system with a compact system structure and low consumption of energy could be equipped with platforms, such as underwater robots and AUVs, to facilitate real-world underwater applications. Full article