Search Results (139)

Ocular trauma represents a threat to crew safety and mission performance in space. Microgravity, confined environments, and exposure to particulate matter, chemicals, and mechanical hazards place astronauts at risk for corneal abrasions, open-globe injuries, chemical burns, lens dislocation, retinal detachment, orbital fractures, and barotrauma. Diagnostic capabilities during spaceflight remain limited by resources, lack of specialist expertise, and communication delays with Earth. Artificial intelligence, particularly convolutional neural networks and multimodal models, may help address these gaps through image interpretation, risk stratification, and longitudinal monitoring. Convolutional neural networks can extract hierarchical features from imaging data to identify subtle structural abnormalities, while multimodal models integrate imaging with clinical and environmental parameters to generate more comprehensive assessments. Terrestrial ophthalmology studies demonstrate the potential of these approaches across optical coherence tomography, ultrasound, fundus photography, and anterior-segment imaging. This review examines how these capabilities can be matched to ocular injuries during spaceflight, compares the suitability of different approaches across injury types, and identifies pathways toward autonomous care. Particular emphasis is given to spaceflight-related imaging and physiologic changes, constrained onboard hardware, and integration into workflows that support non-expert crew members. Collectively, these applications may expand diagnostic capabilities and enable earlier, more informed management during long-duration missions. Full article

To overcome the limitations of existing three-degree-of-freedom milling stability models in representing axial cutting conditions, this study develops a stability prediction framework that accounts for both axial segmentation and the axial contact angle. A three-degree-of-freedom dynamic model of the milling system is first formulated by introducing the axial contact angle. The tool axis is then discretized, so that the cutting force coefficients can be evaluated in different axial sections and the non-uniform distribution of cutting forces along the tool can be captured more accurately. After incorporating the regenerative mechanism, the milling dynamics are expressed in the form of a linear time-delay differential equation. To enhance the numerical accuracy of the time-delay system solution, a full-discretization scheme using third-order Lagrange–Hermite interpolation is developed for constructing the state transition matrix. The stability boundary is subsequently determined based on Floquet theory, from which the stability lobe diagram is generated. The proposed model and solution procedure are validated by comparison with existing methods and by time-domain simulation. The results show that, when the spindle speed ranges from 5000 to 10,000 rpm and the axial depth of cut ranges from 0 to 8 mm, the overall variation rate of the predicted stability region is 11.19% after incorporating axial discretization and 59.88% after considering the axial contact angle. The stable and unstable cutting responses obtained from time-domain simulations are consistent with the regions predicted by the stability lobe diagram, which supports the validity of the proposed approach. Further investigation shows that, for the established three-degree-of-freedom milling model and the specified cutting parameters, the axial contact angle exerts a pronounced nonlinear effect on the stability boundary. Specifically, as the axial contact angle $\epsilon$ increases within the range $0^{°}<\epsilon \le {45}^{°}$, the stable region gradually shrinks; when $\epsilon$ increases from 45° to 90°, the stable region expands instead. These observations can provide useful guidance for selecting milling parameters and identifying stable machining conditions. Full article

Non-recurrent congestion (NRC), caused by unforeseen events such as crashes, lane closures, and adverse weather, accounts for approximately half of all delays on urban freeways, yet it remains difficult to distinguish from routine congestion at recurrent bottlenecks. This study proposes an NRC detection framework based on a Physics-Informed Neural Network (PINN) that embeds the Lighthill–Whitham–Richards (LWR) conservation law into the learning process to construct a physically consistent baseline of normal traffic states. The traffic flow physics is represented by a two-regime fundamental diagram combining the Greenshields model for free-flow conditions and the Underwood model for congested conditions, and the network is trained by minimizing a composite loss that adaptively balances the data fitting error against the LWR residual. NRC is then detected when the observed density exceeds the PINN-estimated baseline density beyond a tolerance threshold of 150%. The framework was evaluated on a 12 km segment of the Seoul Ring Expressway in Korea using six months of 15 min data collected from seventeen sensor stations. The results show that the proposed model reliably isolates NRC events from recurrent peak-period congestion. From the perspective of symmetry, the framework interprets recurrent traffic as a temporally symmetric background state governed by a conservation law, and non-recurrent congestion as a local breaking of this symmetry, which the physics-constrained residual is designed to expose. The key contribution of this study is a theoretically grounded, label-free anomaly detection approach that couples machine learning with traffic flow theory, offering traffic management centers an automated and interpretable tool for incident detection and response. Full article

Conventional resistive-type superconducting fault-current limiters (RSFCLs) rely exclusively on fault currents and temperature increases to trigger quenching, resulting in delayed fault response and excessive heat buildup under short-circuit conditions. To mitigate these limitations, this paper proposes a self-triggered, magnetic-field-excited superconducting current-limiting reactor (SCLR) integrated with a solenoidal magnet assembly. During the design and simulation phases, a segmented discretization method is employed to quantitatively characterize the gradient distribution of the external perpendicular field within the superconducting tapes and coils. This approach theoretically elucidates the mechanism by which spatially non-uniform magnetic fields influence current-limiting performance. DC short-circuit simulations show that the background magnetic field instantly reduces the critical current, rapidly transitioning the superconducting layer into a nonlinear resistive state. In contrast to the conventional topology, the proposed SCLR achieves two key performance improvements during short-circuit faults: it limits the peak fault current to just 45.9% of the value recorded with the conventional RSFCL scheme, and it reduces the maximum temperature rise by 1.4 K. The findings of this study provide a theoretical foundation and technical references for multi-field coupling modeling and structural optimization of magnetic-field-regulated current-limiting devices in DC grids. Full article

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures. Full article

In high-pier bridge construction, the quality and accuracy of embedded rebar placement are critical to ensuring structural safety and durability. However, conventional manual inspection methods are inefficient, subjective and pose significant safety risks in high-altitude operations. These methods are unable to comprehensively inspect all pier columns on a daily basis, and frequently result in delays in acceptance that necessitate rework. In order to address these challenges, the current study proposes a smart vision-based inspection framework for the automatic and high-precision quality assessment of rebar under long-distance imaging conditions. This approach allows quality inspectors to remotely predict and evaluate the embedment quality of rebars from a safe distance. Notably, this work introduces a novel dual-source coordinate fusion mechanism that integrates improved instance segmentation with corner detection for global-to-local precision enhancement, representing an original contribution to rebar placement inspection in complex high-pier scenarios. The framework integrates an improved YOLOv8-CD segmentation model and a corner detection algorithm through a dual-source coordinate fusion mechanism, achieving an integration of global rebar detection and local feature enhancement. The YOLOv8-CD model, when optimised, features the Convolutional Block Attention Module (CBAM) integrated into the backbone, with the objective of enhancing recognition accuracy for small targets. Additionally, a Dilation-Wise Residual (DWR) module has been inserted before the neck C2f layer for the purpose of strengthening multi-scale feature extraction. The process of perspective correction and pixel-to-actual-length conversion coefficienting is performed in order to achieve a millimetre-level measurement of the rebar spacing and diameter. Empirical validation through real high-pier construction scenes demonstrates that the proposed framework attains a detection accuracy of 98.82%, surpassing conventional YOLO-based and single-source methodologies. The experimental results demonstrate that this framework is able to detect objects at longer distances, and to maintain its performance when the target is at a greater distance than that which was used for training. The proposed approach is expected to provide an efficient, safe, and quantitative solution for intelligent bridge construction quality monitoring, offering valuable insights for the future development of smart construction and structural health inspection systems. Full article

Multi-Agent Path Finding (MAPF) is a core technology for logistics automation in factories and warehouses. Guidance-based approaches that reflect the structural properties of factory environments have been widely adopted for computational efficiency and execution feasibility. However, these approaches generally assume static environments in which predefined guidance policies remain valid. Therefore, unexpected obstacles can cause inter-robot collisions or deadlocks. To make nominal MAPF plans robust against execution uncertainty, prior studies have incorporated bounded execution delays into MAPF. A representative method is k Robust Multi-Agent Path Finding (kR-MAPF), which models allowable execution delay using a global robustness parameter k. However, when large obstacle-induced delays are represented by a single global robustness parameter, kR-MAPF imposes unnecessary conservatism and increases the search space. This increase in search space raises planning runtime and reduces path efficiency in large-scale robot fleet operation. This paper proposes a multi-robot path planning framework that updates guidance policies for each segment based on real-time obstacle information. The proposed framework identifies robots affected by obstacles and selectively replans their paths, thereby reducing unnecessary computation while maintaining path planning success and path efficiency. Simulation results in a $100\mathrm{m}\times 100\mathrm{m}$ factory environment with up to 100 robots demonstrate that the proposed framework maintains a 100% success rate under all tested conditions. Compared with kR-MAPF with different values of k, the proposed framework reduces planning runtime by approximately 35–79% and flowtime by approximately 7–24%. These results demonstrate that obstacle-aware selective replanning can improve both real-time performance and path efficiency in dynamic factory environments. The proposed framework provides a technical basis for stable large-scale multi-robot operation in structured industrial environments. Full article

Real-time assessment of reading comprehension remains a challenge in educational research. Traditional evaluation methods, such as questionnaires, provide delayed and retrospective measures and therefore do not capture the dynamic nature of comprehension during reading. This exploratory study investigates whether beta-band electroencephalography (EEG) activity can be used to estimate EEG-derived indicators related to reading comprehension during academic reading. The study included 40 university students who read a conceptually demanding scientific text while EEG signals were continuously recorded. Beta-band activity (13–30 Hz) was extracted from six cognition-related channels and segmented into non-overlapping 2 s windows. Principal component analysis (PCA) was applied for dimensionality reduction, followed by kernel ridge regression (KRR) for prediction. At the window level, the proposed KRR–PCA framework achieved a mean absolute error (MAE) of 5.797, a root mean square error (RMSE) of 7.783, an MAE-based accuracy of 94.2%, and an explained variance of R² = 0.275 on a held-out test set. At the participant level, aggregated predictions showed a significant correlation with questionnaire-based comprehension scores (r = 0.59), indicating that EEG-derived features captured meaningful inter-individual differences. The framework also generated time-resolved prediction profiles that reflected fluctuations in EEG-derived comprehension estimates during reading. These findings suggest that beta-band EEG contains information related to reading comprehension and may support the development of future EEG-based educational monitoring systems. Further validation using larger cohorts and time-resolved comprehension measures is needed to confirm the practical applicability of the approach. Full article

Background and Clinical Significance: Left ventricular free-wall rupture (LVFWR) is a rare but devastating mechanical complication of acute myocardial infarction (AMI), with reported in-hospital mortality approaching 90% without surgical intervention. Although its incidence has declined in the contemporary primary percutaneous coronary intervention (PCI) era, LVFWR remains an important cause of early post-infarction death, particularly after delayed reperfusion or fibrinolytic therapy. Subacute or contained “oozing” ruptures pose a unique diagnostic challenge because hemodynamic stability and nonspecific symptoms can mask the underlying catastrophe, and standard transthoracic echocardiography may fail to visualize a sealed defect. Contrast-enhanced cardiac computed tomography (CT) has emerged as a valuable adjunct in this setting, enabling early recognition and surgical planning. Case Presentation: We report a case of a 51-year-old male, a heavy smoker, with acute lateral ST-segment elevation myocardial infarction (STEMI) treated with thrombolysis at a referring hospital, followed by percutaneous coronary intervention (PCI) to the obtuse marginal branch. Despite reperfusion, he developed persistent pleuritic chest pain and a small pericardial effusion. Cardiac computed tomography (CT) demonstrated a contained (sealed) lateral-wall oozing-type left ventricular free-wall rupture (LVFWR) with thrombus sealing the defect. A multidisciplinary heart team initially opted for diligent observation with frequent echocardiography. Within the first 24 h, the pericardial effusion increased, and echocardiography showed circumferential effusion with lateral wall thickening and hematoma, prompting emergent sternotomy. Intraoperatively, a large posterolateral infarct with an oozing-type LV free-wall rupture was identified. Surgical repair was performed using interrupted pledgeted sutures, native pericardial patch, BioGlue, and an overlying Teflon patch, with intra-aortic balloon pump (IABP) support. This case demonstrates the complementary diagnostic value of multimodality imaging—echocardiography for serial monitoring of the pericardial effusion and regional wall changes, and cardiac CT for direct characterization of the contained (sealed) defect—and the timely transition from conservative to surgical management in oozing-type rupture. The patient recovered uneventfully and was discharged in stable condition. Conclusions: This case highlights the diagnostic value of multimodality imaging—particularly cardiac CT—in detecting contained (sealed) LVFWR when echocardiography is inconclusive. Early recognition and prompt surgical intervention enabled a successful outcome in this otherwise frequently fatal complication. Full article

Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis of IHD varies significantly between men and women. Women often present with atypical symptoms, and their cardiovascular risk is frequently underestimated, which leads to delayed diagnosis. Also, existing approaches face challenges in subtle early-stage abnormalities, single-lead ECG presentation, and the limited interpretability of deep learning models. These cause significant challenges to the accurate diagnosis of IHD. To address these, this study proposes a gender-aware framework, Gender-Aware Morphology Encoder Network (GAMENet), for early ischemic heart disease detection using 12-lead ECG signals with clinical metadata. A novel GAMENet is developed using the PTB-XL database. The Adaptive Morphology Deviation Encoder (AMDE) through Morphology Segment Extraction (MSEG-R) using R-Peak anchoring, isolates clinically relevant waveform components (P-wave, QRS complex, ST-segment, and T-wave) from the preprocessed ECG signals. The feature vector of morphology features is passed through dense layers with dropout regularization and a SoftMax classifier. Statistical and comparative analysis ensures that the proposed framework enables accurate IHD classification and improved interpretability. Full article

In present recommendation system research, most approaches rely on analyzing the consumption behavior of large numbers of users to generate recommendations. However, this strategy requires extensive computational resources and often leads to considerable delays in producing recommendation results, which negatively affect the user experience. To overcome these limitations, we developed an innovative segmented data-based recommendation method for user region optimization, offering an effective alternative to traditional big-data recommendation strategies. The developed method divides the data into multiple smaller segments according to user regions and then performs specialized analysis within each segment. This segmentation substantially reduces computational time while simultaneously improving the relevance and accuracy of recommendations. By lowering computational complexity, the system is able to respond more rapidly to user requests, making more efficient use of computational resources without compromising recommendation quality. Through this segmented computation approach, the system shows faster response speeds and maintains high recommendation performance. Ultimately, the method provides new insights into optimizing recommendation systems and highlights promising directions for future improvements. Full article

Polar motion (PM), a key component of Earth orientation parameters (EOPs), is essential for high-precision satellite orbit determination and deep-space navigation. However, delays in data acquisition and processing limit its availability for real-time applications, necessitating the development of prediction models based on historical observations. Common approaches include least squares extrapolation (LS), autoregressive (AR) models, and their combination (LS + AR), often enhanced by effective angular momentum (EAM) from Earth’s fluid components. This study examines an EAM + LS + AR method for PM prediction, systematically evaluating how different EAM forecast horizons (1–10 days) affect 90-day prediction accuracy for both PM X and Y components. A segmented optimization strategy is proposed and validated against International Earth Rotation and Reference Systems Service (IERS) official predictions using the IERS EOP 14 C04 product. Key findings include: (a) Adjusting the EAM horizon substantially reduces prediction errors. Segmented prediction improves PM X accuracy by 20–30% (1–60 days) and 10–20% (61–90 days) relative to IERS rapid products, while PM Y short-term accuracy improves by 20–40% (1–15 days). (b) The influence of EAM horizon on long-term PM Y prediction gradually weakens, with errors converging to approximately 8 mas by day 90. (c) For 1–10-day forecasts, optimal horizons follow a systematic pattern: day m predictions achieve the highest accuracy using an (m−1)-day EAM horizon, while a 10-day horizon is optimal for long-term forecasts. (d) The proposed method shows clear advantages over IERS forecasts, with 83.5% of PM X predictions (1–90 days) and 50.78% of PM Y predictions (1–15 days), outperforming IERS daily products during the 2024 test period. Full article

Timely landslide detection and rapid qualitative assessment are fundamental to effective warning systems, hazard management, and risk mitigation. Yet, current practices that rely on on-site surveys and manual expert assessment remain risky, costly, and time-consuming. These limitations result in substantial delays between the event and the availability of actionable information. This study proposes a hybrid, multi-model framework that fuses RGB remote-sensing imagery with geospatial layers to enable timely landslide detection and actionable reporting. The pipeline couples an enhanced SegFormer (denoted as SDF-SegFormer-B2) model for landslide localization, a feature extraction technique for per-slide geo-attribute computation, and a lightweight instruction-tuned LLM (Mistral-7B-Instruct-v0.3) for structured, expert-style reporting. Although a few previous studies have explored landslide captioning, to our knowledge this is the first framework designed to generate structured technical reports enriched with terrain-context interpretation and qualitative intervention-priority indicators. Experiments use 26,758 georeferenced RGB tiles (64 × 64) with 3 m of spatial resolution from PlanetScope satellite imagery over Emilia–Romagna, Italy, with 68,592 annotated landslide boxes collected after the May 2023 rainfall events (~200 mm in 48 h on 1–3 May; 200–250 mm in 48 h on 16–17 May). The proposed SDF-SegFormer-B2 segmentation model achieved a precision of 85.54%, recall of 72.31%, and an F1-score of 78.39% on the unseen test dataset. To evaluate the quality of the generated landslide reports, 100 images were selected for domain-expert assessment. Among these, 58% of the reports were rated as “Very Good,” 30% as “Good,” 8% as “Acceptable,” and 4% as “Poor.” When considering only reports with complete and accurate inputs, 81.48% were rated “Very Good,” and 96.30% were rated either “Good” or “Very Good.” By integrating complementary models and modalities, the proposed approach automates localization-to-reporting and enables the generation of terrain-aware landslide summaries that may support preliminary decision-making and rapid post-disaster screening. Full article

Under the dual pressures of heritage conservation and tourism growth, improving inclusive access to cultural tourism opportunities in historic urban areas has become an urgent planning issue under Sustainable Development Goal 11 and the Historic Urban Landscape approach. Taking the central urban area of Suzhou, China, as a case study, this study evaluates time-budgeted walkable accessibility, spatial equity, local mismatch, and accessibility-generating conditions from a 15 min city perspective. An integrated analytical framework was developed by combining kernel density analysis, GIS-based network accessibility modelling, Lorenz–Gini equity assessment, bivariate Local Indicators of Spatial Association (LISA), and XGBoost–SHAP interpretation. The results show that cultural tourism opportunities exhibit a clear core polarisation–peripheral attenuation pattern. Within the 15 min threshold, Gusu District records SACR and AAR values of 80.18% and 95.23%, respectively, indicating a pronounced historic-core accessibility advantage. Accommodation-tier differences do not form a simple monotonic relationship with accessibility, but are shaped by the spatial embedding of different accommodation-market segments within the cultural tourism opportunity field. HL units, namely high-tier accommodation near low accessibility, emerge as priority diagnostic areas of local mismatch, while delayed accessibility beyond 30 min becomes particularly evident among elderly visitors. The SHAP interpretation further indicates that leisure-strolling attractions show a more balanced supply–accommodation structure, whereas commercial–cultural mixed and heritage-core attractions are more strongly supply-led. By linking accessibility measurement, equity assessment, local mismatch diagnosis, and mechanism-based explanation, this study provides an operational basis for zonal and typology-oriented optimisation of cultural tourism accessibility in historic urban areas. Full article

Background: Heat shock protein 100 (HSP100) is a key molecular chaperone that maintains intracellular proteostasis and enhances plant tolerance. However, the HSP100 gene family in soybean (Glycine max) has not been systematically characterized. Methods: In this study, we performed genome-wide identification and comprehensive analysis of the GmHSP100 gene family and analyzed their phylogeny, genomic distribution, synteny, protein structures, subcellular localization, promoter cis-elements, and expression patterns under heat and salt stresses via bioinformatics approaches and quantitative real-time PCR (qRT-PCR) validation. Results: Thirteen GmHSP100 members were identified, which were classified into CLPB, CLPC and CLPD subfamilies. Segmental and whole-genome duplications primarily drove the expansion of this gene family. All encoded proteins possessed conserved AAA+ ATPase domains, with distinct motifs across subfamilies. Most proteins localized to the cytoplasm, while CLPC and CLPD targeted chloroplasts and GmCLPB4 localized to mitochondria. Promoter analysis identified numerous elements associated with light, hormone and stress responses. Expression profiling showed strong tissue specificity and time-dependent stress-treatment induction. Heat stress triggered rapid and strong upregulation of the GmHSP100s, whereas salt stress salt stress induced their relatively delayed and sustained expression. Conclusions: These findings reveal the evolutionary conservation and diversification of the GmHSP100 gene family in soybean, providing a foundational framework for understanding the functions of GmHSP100 in stress adaptation. Full article