Search Results (52)

Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the International Roughness Index (IRI) of asphalt pavements in cold climates. A dataset only for cold regions was constructed from the Long-Term Pavement Performance (LTPP) database, integrating multiple variables related to climate, structure, materials, traffic, and constructions. The BO-NGBoost model was evaluated against conventional deterministic models, including artificial neural networks, random forest, and XGBoost. Results show that BO-NGBoost achieved the highest predictive accuracy (R² = 0.897, RMSE = 0.184, MAE = 0.107) while also providing uncertainty quantification for risk-based maintenance planning. BO-NGBoost effectively captures long-term deterioration trends and reflects increasing uncertainty with pavement age. SHAP analysis reveals that initial IRI, pavement age, layer thicknesses, and precipitation are key factors, with freeze–thaw cycles and moisture infiltration driving faster degradation in cold climates. This research contributes a scalable and interpretable framework that advances pavement deterioration modeling from deterministic to probabilistic paradigms and provides practical value for more uncertainty-aware infrastructure decision-making. Full article

Motivation: Benign paroxysmal positional vertigo (BPPV) is characterized by torsional nystagmus induced by changes in head position, where accurate quantitative assessment of subtle torsional eye movements is essential for precise diagnosis. Conventional videonystagmography (VNG) techniques face challenges in accurately capturing the rotational components of pupil movements, and existing automated methods typically exhibit limited performance in identifying torsional nystagmus. Methodology: The objective of this study was to develop an automated system capable of accurately and quantitatively detecting torsional nystagmus. We introduce the Torsion Transformer model, designed to directly estimate torsion angles from iris images. This model employs a self-supervised learning framework comprising two main components: a Decoder module, which learns rotational transformations from image data, and a Finder module, which subsequently estimates the torsion angle. The resulting torsion angle data, represented as time-series, are then analyzed using a 1-dimensional convolutional neural network (1D-CNN) classifier to detect the presence of nystagmus. The performance of the proposed method was evaluated using video recordings from 127 patients diagnosed with BPPV. Findings: Our Torsion Transformer model demonstrated robust performance, achieving a sensitivity of 89.99%, specificity of 86.36%, an F1-score of 88.82%, and an area under the receiver operating characteristic curve (AUROC) of 87.93%. These results indicate that the proposed model effectively quantifies torsional nystagmus, with performance levels comparable to established methods for detecting horizontal and vertical nystagmus. Thus, the Torsion Transformer shows considerable promise as a clinical decision support tool in the diagnosis of BPPV. Key Findings: Technical performance improvement in torsional nystagmus detection; System to support clinical decision-making for healthcare professionals. Full article

Horse iris recognition is a non-invasive identification method with great potential for precise management in intelligent horse farms. However, horses’ natural vigilance often leads to stress and resistance when exposed to close-range infrared cameras. This behavior makes it challenging to capture clear iris images, thereby reducing recognition performance. This paper addresses the challenge of generating high-resolution iris images from existing low-resolution counterparts. To this end, we propose a novel hybrid-architecture image super-resolution (SR) network. Central to our approach is the design of Paired Asymmetric Transformer Block (PATB), which incorporates Contextual Query Generator (CQG) to efficiently capture contextual information and model global feature interactions. Furthermore, we introduce an Efficient Residual Dense Block (ERDB), specifically engineered to effectively extract finer-grained local features inherent in the image data. By integrating PATB and ERDB, our network achieves superior fusion of global contextual awareness and local detail information, thereby significantly enhancing the reconstruction quality of horse iris images. Experimental evaluations on our self-constructed dataset of horse irises demonstrate the effectiveness of the proposed method. In terms of standard image quality metrics, it achieves the PSNR of 30.5988 dB and SSIM of 0.8552. Moreover, in terms of identity-recognition performance, the method achieves Precision, Recall, and F1-Score of 81.48%, 74.38%, and 77.77%, respectively. This study provides a useful contribution to digital horse farm management and supports the ongoing development of smart animal husbandry. Full article

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used International Roughness Index (IRI) has attracted much attention due to its importance in pavement maintenance planning. This study focuses on predicting future IRI values using traditional regression models and neural networks, specifically Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, on two highway sections, each analyzed in two experimental setups. The models consider only traffic and structural road characteristics as variables. The results show that the LSTM method provides significantly lower prediction errors for both highway sections, indicating better performance in capturing roughness trends over time. These results confirm that ANNs are a useful tool for engineers by predicting future IRI values, as they help to extend pavement life and reduce overall maintenance costs. The integration of machine learning into pavement evaluation is a promising step forward in ongoing efforts to optimize pavement management. Full article

The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development. Full article

This study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes a nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60–800 km by integrating geomagnetic indices (AE, SYM-H) and solar activity parameters (F10.7). Utilizing global TEC grid data (spatiotemporal resolution: 1 h/5.625° × 2.8125°) provided by the International GNSS Service (IGS), a Multilayer Perceptron (MLP) model was developed, taking spatiotemporal coordinates, altitude, and space environment parameters as inputs to predict logarithmic electron density ln(Ne). Experimental validation against COSMIC-2 radio occultation observations in 2019 demonstrates the model’s capability to capture ionospheric vertical structures, with a prediction performance significantly outperforming the International Reference Ionosphere model IRI-2020: root mean square error (RMSE) decreased by 34.16%, and the coefficient of determination (R²) increased by 28.45%. This method overcomes the reliance of traditional electron density inversion on costly radar or satellite observations, enabling high-spatiotemporal-resolution global ionospheric profile reconstruction using widely available GNSS-TEC data. It provides a novel tool for space weather warning and shortwave communication optimization. Current limitations include insufficient physical interpretability and prediction uncertainty in GNSS-sparse regions, which could be mitigated in future work through the integration of physical constraints and multi-source data assimilation. Full article

With advancements in hardware, high-quality head-mounted display (HMD) devices are being developed by numerous companies, driving increased consumer interest in AR, VR, and MR applications. This proliferation of HMD devices opens up possibilities for a wide range of applications beyond entertainment. Most commercially available HMD devices are equipped with internal inward-facing cameras to record the periocular areas. Given the nature of these devices and captured data, many applications such as biometric authentication and gaze analysis become feasible. To effectively explore the potential of HMDs for these diverse use-cases and to enhance the corresponding techniques, it is essential to have an HMD dataset that captures realistic scenarios. In this work, we present a new dataset of periocular videos acquired using a virtual reality headset called VRBiom. The VRBiom, targeted at biometric applications, consists of 900 short videos acquired from 25 individuals recorded in the NIR spectrum. These 10 s long videos have been captured using the internal tracking cameras of Meta Quest Pro at 72 FPS. To encompass real-world variations, the dataset includes recordings under three gaze conditions: steady, moving, and partially closed eyes. We have also ensured an equal split of recordings without and with glasses to facilitate the analysis of eye-wear. These videos, characterized by non-frontal views of the eye and relatively low spatial resolutions ($400\times 400$), can be instrumental in advancing state-of-the-art research across various biometric applications. The VRBiom dataset can be utilized to evaluate, train, or adapt models for biometric use-cases such as iris and/or periocular recognition and associated sub-tasks such as detection and semantic segmentation. In addition to data from real individuals, we have included around 1100 presentation attacks constructed from 92 PA instruments. These PAIs fall into six categories constructed through combinations of print attacks (real and synthetic identities), fake 3D eyeballs, plastic eyes, and various types of masks and mannequins. These PA videos, combined with genuine (bona fide) data, can be utilized to address concerns related to spoofing, which is a significant threat if these devices are to be used for authentication. The VRBiom dataset is publicly available for research purposes related to biometric applications only. Full article

Scatterplots can reveal important data relationships, but their visual complexity can make pattern identification challenging. Systematic analytical approaches help structure interpretation by dividing scatterplots into meaningful regions. This paper introduces the region partitioning framework (RCF), a systematic method for dividing scatterplots into interpretable regions using k × k grids, in order to enhance visual data analysis and quantify structural changes through transformation metrics. RCF partitions the x and y dimensions into k × k grids (e.g., 4 × 4 or 16 regions), balancing granularity and readability. Each partition is labeled using an R(p, q) notation, where p and q indicate the position along each axis. Two perspectives are supported: the absolute mode, based on raw values (e.g., “very short, narrow”), and the relative mode, based on min–max normalization (e.g., “short relative to population”). I propose a set of transformation metrics—density, net flow, relative change ratio, and redistribution index—to quantify how data structures change between modes. The framework is demonstrated using both the Iris dataset and a subset of the airquality dataset, showing how RCF captures clustering behavior, reveals outlier effects, and exposes normalization-induced redistributions. Full article

Self-Organizing Map (SOM) neural networks can project complex, high-dimensional data onto a two-dimensional plane for data visualization, enabling an intuitive understanding of the distribution and symmetric structures of such data, thereby facilitating the clustering and anomaly detection of complex high-dimensional data. However, this algorithm is sensitive to the initial weight matrix and suffers from insufficient feature extraction. To address these issues, this paper proposes an improved SOM based on virtual winning neurons (virtual-winner SOMs, vwSOMs). In this method, the principal component analysis (PCA) is utilized to generate the initial weight matrix, allowing the weights to better capture the main features of the data and thereby enhance clustering performance. Subsequently, when new input sample data are mapped to the output layer, multiple neurons with a high similarity in the weight matrix are selected to calculate a virtual winning neuron, which is then used to update the weight matrix to comprehensively represent the input data features within a minimal error range, thus improving the algorithm’s robustness. Multiple datasets were used to analyze the clustering performance of vwSOM. On the Iris dataset, the S is 0.5262, the F1 value is 0.93, the ACC value is 0.9412, and the VA is 0.0012, and the experimental result with the Wine dataset shows that the S is 0.5255, the F1 value is 0.93, the ACC value is 0.9401, and the VA is 0.0014. Finally, to further demonstrate the performance of the algorithm, we use the more complex Waveform dataset; the S is 0.5101, the F1 value is 0.88, the ACC value is 0.8931, and the VA is 0.0033. All the experimental results show that the proposed algorithm can significantly improve clustering accuracy and have better stability, and its algorithm complexity can meet the requirements for real-time data processing. Full article

Gaze tracking and estimation are essential for understanding human behavior and enhancing human–computer interactions. This study introduces an innovative, cost-effective solution for real-time gaze tracking using a standard webcam, providing a practical alternative to conventional methods that rely on expensive infrared (IR) cameras. Traditional approaches, such as Pupil Center Corneal Reflection (PCCR), require IR cameras to capture corneal reflections and iris glints, demanding high-resolution images and controlled environments. In contrast, the proposed method utilizes a convolutional neural network (CNN) trained on webcam-captured images to achieve precise gaze estimation. The developed deep learning model achieves a mean squared error (MSE) of 0.0112 and an accuracy of 90.98% through a novel trajectory-based accuracy evaluation system. This system involves an animation of a ball moving across the screen, with the user’s gaze following the ball’s motion. Accuracy is determined by calculating the proportion of gaze points falling within a predefined threshold based on the ball’s radius, ensuring a comprehensive evaluation of the system’s performance across all screen regions. Data collection is both simplified and effective, capturing images of the user’s right eye while they focus on the screen. Additionally, the system includes advanced gaze analysis tools, such as heat maps, gaze fixation tracking, and blink rate monitoring, which are all integrated into an intuitive user interface. The robustness of this approach is further enhanced by incorporating Google’s Mediapipe model for facial landmark detection, improving accuracy and reliability. The evaluation results demonstrate that the proposed method delivers high-accuracy gaze prediction without the need for expensive equipment, making it a practical and accessible solution for diverse applications in human–computer interactions and behavioral research. Full article

Between 2022 and 2023, four systematic fish surveys were carried out in the Poyang Lake basin (PLB), capturing 49,192 fish (7017 kg) and identifying 120 species from 10 orders, 21 families, and 70 genera. Cypriniformes were the most dominant, accounting for 79 species. The spring and autumn surveys collected 25,734 and 23,458 individuals, respectively, with corresponding biomasses of 3978 kg and 3038 kg. Dominant species (IRI > 1000) in the study area included Hemiculter leucisculus, Megalobrama skolkovii, Hypophthalmichthys molitrix, and Aristichthys nobilis. Additionally, critically endangered species such as Ochetobius elongatus, Myxocyprinus asiaticus, and Acipenser sinensis as well as exotic species like Cirrhinus mrigala and euryhaline species like Cynoglossus gracilis and Hyporhamphus intermedius were observed. Hierarchical clustering grouped the survey stations into three distinct areas (PYS, XBMS, and XBUS), with the ANOSIM analysis showing highly significant differences (R = 0.893, p < 0.01). Redundancy analysis (RDA) indicated that in spring, total phosphorus (TP) and temperature were the main factors influencing variability (80.50%), while in autumn, temperature, oil, and pH were the key factors (75.20%). This study emphasizes the predictable changes in fish community composition caused by environmental gradients and highlights the need for ongoing monitoring to effectively manage and protect the ecosystem, particularly in the post-fishing ban period. Full article

The Segment Anything Model (SAM) has made breakthroughs in the domain of image segmentation, attaining high-quality segmentation results using input prompts like points and bounding boxes. However, utilizing a pretrained SAM model for iris segmentation has not achieved the desired results. This is mainly due to the substantial disparity between natural images and iris images. To address this issue, we have developed SAM-Iris. First, we designed an innovative plug-and-play adapter called IrisAdapter. This adapter allows us to effectively learn features from iris images without the need to comprehensively update the model parameters while avoiding the problem of knowledge forgetting. Subsequently, to overcome the shortcomings of the pretrained Vision Transformer (ViT) encoder in capturing local detail information, we introduced a Convolutional Neural Network (CNN) branch that works in parallel with it. This design enables the model to capture fine local features of iris images. Furthermore, we adopted a Cross-Branch Attention mechanism module, which not only promotes information exchange between the ViT and CNN branches but also enables the ViT branch to integrate and utilize local information more effectively. Subsequently, we adapted SAM for iris image segmentation by incorporating a broader set of input instructions, which included bounding boxes, points, and masks. In the CASIA.v4-distance dataset, the E1, F1, mIoU, and Acc of our model are 0.34, 95.15%, 90.88%, and 96.49%; in the UBIRIS.v2 dataset, the E1, F1, mIoU, and Acc are 0.79, 94.08%, 88.94%, and 94.97%; in the MICHE dataset, E1, F1, mIoU, and Acc were 0.67, 93.62%, 88.66%, and 95.03%. In summary, this study has improved the accuracy of iris segmentation through a series of innovative methods and strategies, opening up new horizons and directions for large-model-based iris-segmentation algorithms. Full article

This study proposes a biometric authentication method using infrared (IR)-based periocular images captured in virtual reality (VR) environments with head-mounted displays (HMDs). The widespread application of VR technology highlights the growing need for robust user authentication in immersive environments. To address this, the study introduces a novel periocular biometric authentication system optimized for HMD usage. Ensuring reliable authentication in VR environments necessitates overcoming significant challenges, including flicker noise and infrared reflection. Flicker noise, caused by alternating current (AC)-powered lighting, produces banding artifacts in images captured by rolling-shutter cameras, obstructing biometric feature extraction. Additionally, IR reflection generates strong light glare on the iris surface, degrading image quality and negatively impacting the model’s generalization performance and authentication accuracy. This study utilized the AffectiVR dataset, which includes noisy images, to address these challenges. In the preprocessing phase, iris reflections were removed, reducing the Equal Error Rate (EER) from 6.73% to 5.52%. Furthermore, incorporating a Squeeze-and-Excitation (SE) block to mitigate flicker noise and enhance model robustness resulted in a final EER of 6.39%. Although the SE block slightly increased the EER, it significantly improved the model’s ability to suppress noise and focus on critical periocular features, ensuring enhanced robustness in challenging VR environments. Heatmap analysis revealed that the proposed model effectively utilized periocular features, such as the skin around the eyes and eye contours, compared to prior approaches. This study establishes a crucial groundwork for advancing robust biometric authentication systems capable of overcoming noise challenges in next-generation immersive platforms. Full article

Background/Objectives: In contrast to traditional biometric modalities, such as facial recognition, fingerprints, and iris scans or even DNA, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual’s skeletal structure, the ribcage of the chest, lungs, and heart, chest X-rays have emerged as a focal point for identification and verification, especially in the forensic field, even in scenarios where the human body damaged or disfigured. Discriminative feature embedding is essential for large-scale image verification, especially in applying chest X-ray radiographs for identity identification and verification. This study introduced a self-residual attention-based convolutional neural network (SRAN) aimed at effective feature embedding, capturing long-range dependencies and emphasizing critical spatial features in chest X-rays. This method offers a novel approach to person identification and verification through chest X-ray categorization, relevant for biometric applications and patient care, particularly when traditional biometric modalities are ineffective. Method: The SRAN architecture integrated a self-channel and self-spatial attention module to minimize channel redundancy and enhance significant spatial elements. The attention modules worked by dynamically aggregating feature maps across channel and spatial dimensions to enhance feature differentiation. For the network backbone, a self-residual attention block (SRAB) was implemented within a ResNet50 framework, forming a Siamese network trained with triplet loss to improve feature embedding for identity identification and verification. Results: By leveraging the NIH ChestX-ray14 and CheXpert datasets, our method demonstrated notable improvements in accuracy for identity verification and identification based on chest X-ray images. This approach effectively captured the detailed anatomical characteristics of individuals, including skeletal structure, ribcage, lungs, and heart, highlighting chest X-rays as a viable biometric tool even in cases of body damage or disfigurement. Conclusions: The proposed SRAN with self-residual attention provided a promising solution for biometric identification through chest X-ray imaging, showcasing its potential for accurate and reliable identity verification where traditional biometric approaches may fall short, especially in postmortem cases or forensic investigations. This methodology could play a transformative role in both biometric security and healthcare applications, offering a robust alternative modality for identity verification. Full article

The occupational environment may affect one’s psychophysical health by leveraging both external workplace stressors and individual psychological responses. We developed a comprehensive questionnaire to assess occupational stress among postgraduate medical trainees, investigating both situational and personal aspects. Exploratory factor analysis was used to identify the constructs captured by the questionnaire, and reliability was assessed by estimating Cronbach’s alpha. Construct-specific scores were computed, and their correlation with established pre-validated scales (criterion validation) was assessed. Four factors—“stress”, “coping”, “empathy”, and “trauma”—explained 50% of data variability and demonstrated satisfactory overall internal consistency (Cronbach’s alpha = 0.76). Significant correlations were found between the “stress” score and the “emotional exhaustion” component of the Maslach Burnout Inventory (MBI) (r = −0.76), the “coping” score and the “positive attitudes” component of the Coping Orientation to Problems Experienced Inventory (COPE) (r = 0.46), and the “empathy” score with the “empathic concern” (r = 0.52), “fantasy” (r = 0.41), and “perspective taking” (r = 0.45) components of the Interpersonal Reactivity Index (IRI). No significant differences in scores were found in terms of gender or medical specialization. This study suggests that the SCOPE questionnaire may be a promising tool for assessing workplace stress and psychological responses among medical residents. Full article