Search Results (137,267)

Real-time performance remains a core requirement for safety-critical robotic applications. ROS 2 has become a de facto middleware standard, while Docker is increasingly adopted for modular and portable deployment. However, embedded hardware updates often constrain Linux distributions and real-time kernel versions, while existing software stacks depend on older ROS 2 releases and legacy libraries. This mismatch forces costly porting and revalidation, motivating heterogeneous deployments that mix ROS 2 versions across host and Docker container runtimes. Yet the overheads introduced by Docker and cross-version ROS 2 communication are not well quantified in terms of real-time guarantees. This paper presents a Docker-enabled real-time framework for evaluating robotic applications in heterogeneous ROS 2 deployments. The framework integrates an RT-PREEMPT–patched Linux kernel, Dockerized ROS 2 distributions, and configurable cross-version communication pathways to enable controlled, repeatable experiments without full-stack migration. We empirically quantify Docker-induced effects on real-time execution using task periodicity, jitter, and response time, and assess ROS 2 communication using end-to-end latency under host-only, container-only, and hybrid configurations. To demonstrate practical viability, we apply the framework to an operational mobile-robot use case that integrates legacy control code with new modules, including a reinforcement-learning decision layer, within a mixed host–container ROS 2 stack. The resulting analyses provide reusable tooling and actionable guidelines for deploying deterministic ROS 2 systems under containerized heterogeneous constraints. Full article

Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Correlation Adaptive Spectral (STCCAS) inversion method, integrating four machine learning models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Experiments were conducted in Tampa Bay’s nearshore waters, using Sentinel-2 imagery and Airborne LiDAR Bathymetry (ALB) data. Core to STCCAS, the Temporal Stability Index (TSI) quantifies spectral temporal consistency, while the Normalized Difference Turbidity Index (NDTI) characterizes water turbidity, and the two indices synergistically form a dual-scale “spectral reliability-turbidity stability” evaluation system for pixel-level feature quality assessment—coupled with spectral fusion features and spatial location, they jointly realize pixel-level feature reliability weighting and dynamic filtering to build a water condition-adaptive input set. Comparative analysis of inversion performance under the original spectral features (OSFs) inversion method vs. STCCAS inversion method confirms STCCAS significantly boosts accuracy. XGBoost outperforms others, achieving a coefficient of determination (R²) of 0.93, root mean square error (RMSE) of 0.16 m, and mean absolute error (MAE) of 0.12 m. STCCAS breaks the limitations of traditional fixed feature combinations, effectively adapting to nearshore water heterogeneity. It provides a novel method for high-frequency, high-precision shallow water bathymetry inversion, with important practical value for marine environmental monitoring and resource management. Full article

Accurate and efficient screening of nitrogen-efficient rice varieties is crucial for implementing precision agriculture and achieving green and sustainable development. However, traditional screening methods rely on destructive sampling and chemical analysis, which are inefficient and costly, and thus cannot meet the requirements of large-scale breeding applications. Therefore, this study aims to develop a non-invasive, high-throughput screening method for nitrogen efficiency of rice based on unmanned aerial vehicle (UAV) hyperspectral data and machine learning algorithms. Sixty rice varieties were selected as the target, and principal component analysis (PCA) was used to reduce the dimension of seven key agronomic parameters (such as yield, nitrogen utilization rate, etc.). A comprehensive evaluation index for nitrogen utilization efficiency was constructed, and K-means clustering was used to classify the varieties into three categories: nitrogen-efficient, medium-efficient, and low-efficient varieties. On this basis, four machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN)) were used to establish a variety nitrogen efficiency classification model based on spectral indices. The results showed that the indicators constructed based on PCA and clustering could effectively distinguish different nitrogen-efficient varieties; among the four models compared, the DT model achieved the highest overall performance, with an accuracy of 0.75, precision of 0.80, and F1-score of 0.74. This study confirmed the feasibility of combining UAV hyperspectral data with decision tree models, providing a reliable technical solution for the large-scale, rapid, and non-invasive screening of nitrogen-efficient rice varieties. Full article

Domain-adaptive object detection under set-prediction paradigms remains challenging, as Hungarian matching is sensitive to domain shift and fixed pseudo-label thresholds cannot simultaneously handle class imbalance and scene variability. This paper presents SSRT-DETR, a semi-supervised, domain-adaptive framework built on the real-time detector RT-DETR. We adopt a mean teacher–student architecture with style-transferred images to jointly model source and target domains. To stabilize the assignment process during the early stages of cross-domain training, Domain-Aware Matching (DAM) is formulated to augment the Hungarian matching cost with a teacher-guided decoder-query consistency term. Leveraging the more stable EMA teacher representations, DAM guides early matching toward domain-consistent assignments and is gradually annealed to recover standard matching as training converges. In parallel, we introduce Class-/Scene-Adaptive Pseudo-Labeling (CAP) to address a key limitation of existing DAOD methods that rely on fixed or globally tuned pseudo-label thresholds, which struggle with class imbalance and scene-dependent difficulty under domain shift. CAP leverages per-class confidence statistics and multi-view consistency to adapt classification and IoU thresholds across classes and scenes, while temperature scaling and quality-weighted losses provide soft control over pseudo-label reliability. Experiments on standard benchmarks demonstrate the robustness of SSRT-DETR. On Cityscapes→Foggy Cityscapes, SSRT-DETR improves mAP@0.5 from 51.0 to 54.3. On KITTI→Cityscapes and Sim10K→Cityscapes, it achieves 67.3 AP and 64.9 AP on the car category, respectively, clearly outperforming the RT-DETR baseline while maintaining real-time efficiency. Notably, consistent gains are observed in rare categories and adverse weather scenarios, validating the effectiveness of the proposed DAM and CAP modules. Full article

Mild traumatic brain injury (mTBI) is typically associated with transient cognitive disturbance, particularly involving attention and new learning, with most patients demonstrating full recovery within weeks. Memory impairment in uncomplicated mTBI generally reflects reversible neurometabolic dysfunction and is limited to a brief period of post-traumatic amnesia and restricted retrograde loss surrounding the injury. However, a subset of patients develop persistent and disproportionate autobiographical memory disturbance that exceeds expected neuroanatomical limits and lacks structural correlates on neuroimaging. In rare but clinically challenging cases, this presentation may resemble extensive retrograde or identity-related amnesia. This review examines functional (dissociative) amnesia emerging after mTBI and proposes that concussion may act as a gateway condition facilitating the development of Functional Cognitive Disorder (FCD) in vulnerable individuals. We differentiate expected post-traumatic memory patterns from atypical selective impairment of autobiographical retrieval and clarify how distinct memory systems—episodic, autobiographical, semantic, and procedural—are differentially affected. We expand the two-hit hypothesis by integrating contemporary neurobiological evidence. The first hit comprises concussion-induced neurometabolic disturbance, glial activation, oxidative imbalance, and transient fronto-limbic dysregulation. The second hit may involve psychological stress, identity threat, maladaptive metacognitive processes, or persistent neuroinflammatory signalling, collectively resulting in functional inhibition of autobiographical memory retrieval despite preserved memory storage. Functional amnesia is conceptualised as a severe phenotype within the spectrum of functional cognitive disorder. We introduce a structured clinician-administered interview (SIFRA) to operationalise diagnostic features and support systematic assessment. This integrative framework reconciles neurological vulnerability with functional network dysregulation and provides a coherent basis for diagnosis and multidisciplinary management of persistent memory disturbance after mTBI. Full article

Haze, as a critical factor affecting regional air quality and human health, necessitates accurate remote sensing identification for pollution monitoring and climate research. This study proposes a two-stage haze mapping algorithm (THMA), based on a backpropagation neural network and a random forest model, which achieves high-precision identification of haze, clouds, and clear air using FY-4A AGRI geostationary satellite data, with small misclassification rates and high F1 scores. Through detailed comparison with CALIOP observations, THMA performs well over most regions over Asia, successfully extending the traditional binary classification task of distinguishing only clouds and clear air. Notably, the model provides good classification capability in vertically overlapping areas of broken clouds and haze, with minimal misclassification even over bright surfaces such as deserts and ice/snow. Statistical analysis for the year 2022 shows that the annual average number of haze days is 51.3 in China. This study confirms the significant complementary value of satellite remote sensing and ground-based observations for haze monitoring. Full article

Time series data find extensive applications in finance, healthcare, and industrial monitoring domains. However, analytical models targeting such data are subject to notable constraints imposed by the rigid independent and identically distributed (IID) assumption and the high cost of data annotation. Unsupervised Domain Adaptation (UDA) offers an effective remedy for these challenges, and Contrastive Learning (CL) has been widely integrated into UDA frameworks, owing to its robust feature representation and clustering capabilities. Nonetheless, existing CL-based UDA methods suffer from two key limitations: (1) fixed data augmentation strategies result in imbalanced intensity—excessive augmentation erodes sample semantics, while insufficient augmentation induces model overfitting; (2) distribution alignment strategies neglect hard samples which are the core carriers of domain shift, causing their domain adaptation signals to be overshadowed by a large number of normal samples and thus degrading alignment accuracy. To address these drawbacks, this paper proposes a time-series UDA algorithm, termed Adaptive Contrastive Learning Domain Adaptation (ACLDA), which incorporates two key components: (1) an adaptive feature enhancement module that integrates adaptive sample augmentation and CL, enabling the model to capture high-quality transferable features; (2) sample-level adaptive weights, introduced on the basis of class-level alignment via supervised CL, to emphasize the value of hard samples. Comparative experiments on multiple time-series datasets demonstrate that our ACLDA outperforms state-of-the-art domain adaptation methods in terms of average accuracy, verifying its superiority and providing a more robust solution for cross-domain time series analysis. Full article

Oral squamous cell carcinoma (OSCC) cytology involves extracting a cell sample consisting of single cells or small clusters of cells from patients’ head and neck area in order to identify abnormal morphological characteristics after staining it. This method is used to screen for early cancer and the formation of metastases within the oral cavity. OSCC diagnosis partly depends on pathologists’ skills and also laboratories’ instrumentation. The use of Raman spectroscopy could support diagnoses performed using traditional methods, providing information based on the cellular biochemical environment. Technical drawbacks related to low signal-to-noise ratios of Raman spectroscopy and the need to obtain diagnostic information within a reasonable time frame have recently led to the analysis of Raman spectra using machine learning (ML) methods in order to obtain reliable information about the correct attribution of unknown cellular spectra. So, we used Raman micro-spectroscopy combined with machine learning methods to build classification models, which allow the diagnosis of different grades of OSCC in cell samples. The Raman spectra were analysed in the 980–1800 cm⁻¹ range by focusing the laser beam onto the nucleus and the cytoplasm regions of single cells from different cell lines modelling healthy (HaCaT) and cancer (Cal-27, SAS and HSC-3) cytological samples. We considered six classification algorithms (k-Nearest Neighbours, Logistic Regression, Naïve Bayes, artificial Neural Network, Random Forest and Support Vector Machine) to classify unknown Raman spectra. We report two classification tasks: a 4-level classification, which encompasses healthy cells, two different types of cancer cells, and one type of metastatic cells, and a 3-level classification, which includes healthy cells, non-metastatic cancer cells, and metastatic cancer cells. Our findings show that both Neural Network and Support Vector Machine algorithms applied to Raman spectra measured in the cytoplasm region can achieve sensitivity, precision and F1-score values larger than 90% in the 3-groups classifications, whereas Support Vector Machine performs better in the 4-groups classification with respect to a Neural Network. These results contribute to increasing confidence in the clinical translation of ML-assisted Raman spectroscopy as a tool to support conventional cytological techniques. Full article

Machine learning (ML) algorithms may be effective at improving the HCV care cascade. One ML algorithm, developed using U.S. ambulatory electronic medical records (EMR), demonstrated the ability to identify people infected with HCV earlier than conventional testing strategies among those with indications for screening. We evaluated the potential cost-effectiveness of ML-enabled screening for the early identification of undiagnosed HCV among people in care in the U.S. An HCV natural history Markov model was developed to evaluate the cost-effectiveness of the ML algorithm-enabled screening compared to conventional testing over the training data period. Based on the training data, the ML algorithm identified patients on average 6.5 months earlier than conventional testing strategies. We compared the status quo to intervention scenarios using the ML algorithm at different recall levels (proportion of HCV patients identified, 5–100%). We identified the optimal algorithm recall level, which maximized health (measured in quality-adjusted life years, QALYs) while staying under a willingness-to-pay threshold of USD$100,000/QALY gained. ML-enabled screening was cost-effective (ICER < $100 k/QALY gained) in identifying undiagnosed HCV patients for recall levels up to 30%. The optimal recall level was 30% (Precision 0.27%), which resulted in a mean ICER of $94,022/QALY gained. ML-enabled screening for the early identification of undiagnosed HCV patients could be cost-effective in the U.S. Prospective evaluation of real-world effectiveness is warranted. Full article

Generative AI offers transformative potential for scalable, personalized, and dynamic language education, particularly in enhancing oral proficiency among young learners. However, effective deployment remains challenging due to limited resources for some languages, the need for age-appropriate content and tools, and the importance of respecting cultural relevance. In this paper, we introduce LEARN (Language Evaluation via question Answer generation from caRtooNs), a culturally grounded multilingual visual dialogue system designed to support oral proficiency in three of Singapore’s official languages: Mandarin, Bahasa Melayu, and Tamil. English, as the lingua franca, is excluded. LEARN integrates a teacher-facing module for curriculum-aligned visual question-answering task creation and a student-facing module for voice-driven adaptive dialogue, optimized for children’s speech. Unlike existing platforms, LEARN prioritizes cultural relevance and low-resource language support, helping address gaps in heritage language preservation. Pilot studies with students demonstrate significant improvements in engagement and vocabulary acquisition. Designed for classroom as well as home use, LEARN presents a scalable AI-driven language tutoring framework. Full article

Hyaluronic acid–dopamine (HA-Dopa) hydrogels have emerged as promising adhesive biomaterials for biomedical applications. However, the complex dependencies between formulation parameters and hydrogel performance pose challenges for rational material design. In this study, an interpretable machine learning framework was developed to investigate the structure–property relationships of HA-Dopa hydrogels. A dataset comprising 228 data points was collected from 37 peer-reviewed publications, representing heterogeneous experimental conditions across different research groups, and gradient boosting regression models were established to predict adhesion strength and elastic modulus, achieving test R² of 0.99 and 0.94, respectively, with stable performance across cross-validation splits. SHAP analysis revealed that HA molecular weight and dopamine substitution degree are the dominant factors governing adhesion, while mechanical properties exhibit more distributed dependence on multiple formulation parameters. The identified synergistic interactions between key features provide potential guidance for targeted formulation optimization. This work demonstrates the utility of interpretable machine learning in elucidating structure–property relationships and accelerating the development of functional hydrogels for biomedical applications. Full article

To address the limitations of static message aggregation and training instability in the existing Shared Neural Normalized Min-Sum (SNNMS) algorithm, this paper proposes A-SNNMS, an attentive deep LDPC decoding network with adaptive training. First, an attention mechanism is introduced into the variable node update phase to dynamically weight incoming messages based on their reliability, effectively suppressing noise interference. Second, a collaborative training scheme incorporating an exponential decay adaptive learning rate and L2 regularization is designed to mitigate convergence oscillation and overfitting in long-code training. Simulation results for IEEE 802.16e standard codes demonstrate that A-SNNMS achieves a net coding gain of approximately 0.4 dB over the baseline SNNMS at a Bit Error Rate (BER) of 10⁻³. Furthermore, it achieves comparable performance with only 50% of the iterations required by the baseline. In conclusion, the A-SNNMS decoder significantly improves both decoding efficiency and system robustness, offering a promising solution for high-reliability communications. Full article

This study investigates univariate multi-horizon forecasting of national electricity demand as a controlled benchmark for settings where exogenous drivers (e.g., weather and calendar variables) are unavailable or uncertain, through a comparative evaluation of representative deep learning architectures. The examined models include the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, a Temporal Convolutional Network (TCN), and the feed-forward Neural Basis Expansion Analysis for Time Series (N-BEATS) framework. All models are trained and evaluated within a unified experimental setup based on a univariate daily time series of Finnish national electricity demand covering the period from 2016 up to 2021, enabling a controlled assessment of architectural capabilities when relying solely on historical demand. Using a common preprocessing pipeline and a chronological train–validation–test split, forecasts are generated for short-, medium-, and long-term intervals (30, 90, and 365 days), and predictive performance is assessed using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The experimental results show that N-BEATS achieves the lowest RMSE across all considered horizons in the test set, while the GRU architecture attains the smallest MAE at the longest horizon and exhibits consistently strong performance overall. These findings highlight the complementary strengths of recurrent and feed-forward deep learning paradigms for modelling nonlinear structure and long-range dynamics in electricity demand time series, and provide quantitative evidence to support horizon-aware architecture selection in national electricity demand forecasting and related applied modelling contexts. Full article

Multiples often dominate semblance (velocity) spectra and bias-stacking velocity picks, degrading NMO correction and subsequent imaging. We propose FLD–PA, a practical two-stage workflow for automatic velocity-spectrum picking under strong multiple interference. First, the feature-level decoupling detector (FLD) uses an attention-enhanced, YOLOv4-style architecture to localize sparse key picks while suppressing multiple-related clutter. Second, the physics-informed Point Adjustment (PA) module refines coarse picks by enforcing lateral continuity across adjacent spectra and time consistency constraints derived from the stacked section. This refinement yields a geophysically plausible velocity trend. Experiments on two real datasets from a single offshore survey (with non-overlapping CMP/line subsets) show that FLD–PA improves PA@10px from 91.50% to 93.14% and reduces RMSE from 12.40 to 10.15 pixels compared with a YOLOv8–LSTM baseline. Under a matched-recall setting (≈81%), we tune confidence thresholds on a held-out validation subset and evaluate both methods at the same recall. FLD–PA achieves PA@10px = 93.14% with RMSE = 10.15 pixels, compared with 91.50% and 12.40 pixels for YOLOv8–LSTM. Overall, FLD–PA improves the accuracy and stability of velocity picking under strong multiple interference. However, our evaluation focuses on within-survey robustness; cross-survey generalization remains for future work. Full article

Digital twin technology (DTT) creates a virtual replica of a physical object, system, or process and uses real-time data to support monitoring, analysis, and control. Although DTT is increasingly discussed as a means to enhance supply-chain resilience, prior evidence is fragmented and lacks an integrated view across disruption stages. This study conducts a systematic literature review of 89 peer-reviewed articles on DTT and supply-chain resilience, applying relevance-based screening to retain studies with substantive theoretical and practical implications. The review indicates that DTT applications for resilience are emergent but gaining momentum, and that their contribution differs by resilience stage. Specifically, DTT capabilities support preparedness through enhanced visibility, risk sensing, and scenario testing; resistance through real-time monitoring, early warning, and evaluation of mitigation options; rebound through response coordination, recovery planning, and adaptive reconfiguration; and growth through post-disruption learning and network redesign. The synthesis also identifies key barriers to adoption, including data quality limitations, high implementation costs, shortages of specialised skills, and governance challenges, and suggests that integration with complementary digital technologies often enables more advanced functionality. Overall, the study provides a stage-based consolidation of DTT capabilities, benefits, and barriers to guide research and managerial deployment. Full article