Search Results (154)

Accurate attitude estimation for highly dynamic spacecraft relies on robust fusion of star-tracker and inertial measurements. However, asynchronous sensing, motion blur in star images, and delayed star-tracker outputs can significantly degrade estimation accuracy and temporal consistency. To address these challenges, this paper proposes a dual-layer factor graph optimization framework for asynchronous star-tracker/IMU fusion under highly dynamic conditions. At the lower layer, high-rate IMU measurements are combined with motion-blurred star streak observations to construct a local factor graph over the exposure interval. The proposed local fusion process reconstructs discrete star-trail points, estimates angular velocity, and selects IMU-aligned representative observations for temporally consistent association of blurred star measurements. At the upper layer, delayed attitude constraints, propagated star-vector information, and inertial rotational constraints are jointly incorporated to refine the attitude trajectory. Simulation and semi-physical experimental results demonstrate that the proposed framework achieves higher estimation accuracy, stronger robustness, and better tolerance to delayed or intermittent star-tracker observations than the comparison methods, while maintaining practical computational efficiency for near-real-time onboard implementation. Full article

Systematic trend-following strategies applied to equity markets are widely studied, yet most reported performance statistics are non-reproducible in live trading. This paper makes three contributions. First, we introduce a formal taxonomy of look-ahead bias organised around point-in-time correctness: a strategy is point-in-time correct if, for every decision time t, its information set lies in the natural filtration ${\mathcal{F}}_t$. Three bias classes—universe-membership contamination, price-data forward leakage, and stop-exit sequencing violations—are characterised as filtration breaches. Second, we formalise the average true range (ATR) trailing stop as a stochastic recurrence and codify its monotonic non-decreasing ratcheting property (Lemma 1), providing a structural per-trade loss bound. Third, we exhibit a closed-form construction (Theorem 1) of two return sequences with identical Sharpe ratios but arbitrarily divergent maximum consecutive negative-year runs, establishing investor-experience metrics as independent optimisation objectives. We complement these contributions with an 18-year empirical study (2008–2025) on the NASDAQ-100 with reconstructed point-in-time index constituency (Class I compliant) and measured residual Class II exposure, applying combinatorially symmetric cross-validation (CSCV) to a 14-configuration ATR-multiplier grid. The grid exhibits a stop-multiplier-insensitive, CAGR-flat region across $k\in [3.5,7.0]$ (CAGR 10.28–10.39%, net of Dutch progressive tax) and a uniform maximum consecutive negative-year run of 1 across all 14 configurations. The correlation-matrix eigenvalue spectrum of the grid is dominated by a single mode (${\lambda }_1=13.91$ of 14), yielding an effective independent-test count of $M_{\mathrm{eff}}=1.09$. This near-degeneracy persists in a parallel grid with the regime classifier disabled, establishing the ATR multiplier as a structurally near-redundant parameter for this strategy class. The associated PBO value of $=0.9351$ co-occurs with this near-degeneracy under the CSCV maximum-selection rule. The plateau-level performance survives Bonferroni correction for both $M=14$ and $M_{\mathrm{eff}}$. The combined evidence supports a region-based interpretation of robust strategy parameters rather than single-point optimisation. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in clinical decision-support systems, yet its adoption in low- and middle-income countries, including Jordan, remains limited and underexplored. Understanding how healthcare professionals perceive AI-assisted clinical decision-making is essential for safe and contextually appropriate implementation. This study explored healthcare professionals’ perceptions of AI-assisted clinical decision-making in Jordan, with particular attention to trust, accuracy, accountability, professional judgement, digital literacy, and health-system readiness. Medication-related safety and prescribing concerns were examined as secondary cross-cutting issues where they emerged from participants’ accounts. Methods: A qualitative study was conducted using semi-structured, in-depth interviews with 22 purposively sampled healthcare professionals from public, private, and university-affiliated healthcare institutions in Amman, Irbid, and Zarqa. Participants included physicians, nurses, pharmacists, and allied health professionals with varied specialties and levels of seniority. Data were analysed using Braun and Clarke’s reflexive thematic analysis. Member checking, peer debriefing, reflexive memos, and audit trails were used to enhance trustworthiness, and reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Results: Eight overarching themes were identified: conditional trust in AI-assisted clinical decision-making; concerns regarding accuracy and confident algorithmic errors; accountability and professional responsibility; AI as an adjunct rather than a substitute for clinical judgement; the influence of experience, specialty, and digital literacy on AI acceptance; Jordanian health-system readiness; privacy, confidentiality, and algorithmic bias; and training requirements for safe AI use. Medication-related safety emerged as a cross-cutting concern, particularly in relation to dosing, polypharmacy, drug–drug and drug–herb interactions, and the risk of over-reliance on AI-generated recommendations. Conclusions: Healthcare professionals in Jordan expressed cautious but constructive views toward AI-assisted clinical decision-making. AI was perceived as potentially useful when used to support, rather than replace, professional judgement. Participants’ accounts suggest that safe implementation depends on local validation, clear accountability frameworks, ethical data governance, interprofessional training, and careful consideration of medication-safety expertise where AI tools influence prescribing or therapeutic decisions. These findings highlight the importance of context-sensitive AI governance strategies that support trustworthy, accountable, and professionally supervised AI adoption in healthcare. Full article

Aiming at the issues of complex waveforms and low retracking accuracy in coastal satellite altimetry, this paper proposes a complete data processing workflow comprising Fully Focused Synthetic Aperture Radar (FFSAR) waveform processing, waveform classification, denoising, and retracking. Based on actual Sentinel-3A waveforms offshore of Hong Kong, a simulated dataset containing 35,409 waveforms across 17 categories was constructed. A Wave-Transformer classifier based on the Transformer architecture is proposed, achieving 89.16% accuracy with F1-scores above 78% for all categories. Differentiated strategies are adopted for different waveform types: a 3$\sigma$riterion for abnormal peaks, the Dijkstra algorithm for multi-peak waveforms, sub-waveform secondary retracking for trailing noise, and a Modified-Adaptive model for sharp waveforms. Multi-metric evaluation shows that UFSAR and FFSAR outperform PLRM in data validity, retracking success rate, and MQE. In this study, within 10 km of the coast, the Root Sum Square (RSS) of FFSAR sea surface height (SSH) is 4.31 cm lower than that of UFSAR. Validation against tide gauge data shows FFSAR achieves a correlation coefficient of 0.82 and an RMSE of 6.20 cm, superior to UFSAR (0.76, 7.55 cm) and PLRM (0.74, 9.49 cm). Full article

The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and fuel characteristics. In the Republic of Korea (hereafter, the ROK), most wildfires are caused by anthropogenic factors rather than natural ones. However, the current forest fire forecasting system being operated in the ROK does not account for anthropogenic factors. To analyze the impact of human and physical factors on wildfire occurrence, a binary logistic regression model was constructed using data from the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches, including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than that of the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, plantation forests may increase the risk of forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending forest rotation ages and converting coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access and behavior to prevent wildfires. Full article

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. Full article

Background/Objectives: Metronomic chemotherapy offers a well-tolerated option for heavily pretreated metastatic gastrointestinal cancer patients, but reliable prognostic biomarkers for patient selection are lacking. This study aimed to identify exploratory circulating cytokine signatures associated with outcomes in patients treated with metronomic chemotherapy. Methods: We analyzed plasma samples from 34 patients enrolled in the COMET trial (EudraCT 2007-000065-38), a phase II study of metronomic UFT, cyclophosphamide, and celecoxib. An 88-cytokine Luminex^® (Luminex Corporation, Austin, TX, USA) panel was measured at baseline and at four treatment timepoints. Partial least squares discriminant analysis identified candidate biomarkers, followed by systematic combinatorial analysis using Manciu’s method to construct 3-cytokine composite risk scores. Results: Twenty-one patients (61.8%) experienced progressive disease and 13 (38.2%) achieved stable disease. Six biomarkers showed significant discriminative power: IL-16, MCP-4, THBS-2, Eotaxin-1, PDGF-AB/BB, and TRAIL. Three 3-cytokine panels achieved statistically significant risk stratification (all p < 0.05), with hazard ratios for overall survival ranging from 2.59 to 6.24. For the representative IL-16 + MCP-4 + THBS-2 panel, high-risk patients showed a median PFS of 2.0 vs. 4.0 months (HR 3.24, p = 0.0046) and a median OS of 5.8 vs. 11.1 months (HR 4.19, p = 0.0010). Conclusions: This exploratory pharmacodynamic biomarker analysis identifies three 3-cytokine panels associated with prognostic risk stratification in metronomic chemotherapy for metastatic gastrointestinal cancer. As this single-arm trial cannot distinguish prognostic from predictive value, findings are hypothesis-generating. Prospective external validation is required before clinical translation, and exploration in combination with immune checkpoint inhibitors is warranted. Full article

Narrative extraction algorithms construct storylines by finding coherent paths through document collections. The Narrative Trails algorithm frames this as maximum-capacity path optimization, where path quality depends on a coherence function measuring document relationships. We introduce quantum kernels as coherence functions for narrative extraction—to the best of our knowledge, the first systematic characterisation of quantum kernel methods for storyline extraction—and compare them against classical baselines on two corpora using a multi-seed protocol. The sweep covers 93 method evaluations (54 quantum kernels across three encoder families—R_Y+CNOT-ring, IQP/ZZ-feature-map, and a projected quantum kernel—and 39 classical kernels—cosine, RBF, and the cluster-aware Narrative Trails baseline). On 11,215 human navigation paths from Wikispeedia, evaluation metrics divide into two clusters that disagree with each other: alignment-based metrics (length-normalised DTW and per-step DTW similarity) favour methods that produce long alignment-rich paths, while set-overlap metrics (Jaccard and F1) favour methods that produce shorter paths with higher article overlap. On LLM-judged coherence for Cuban news storylines, evaluated under a 12-method × 5-seed × 30-endpoint-pair × 2-judge design (Claude Sonnet 4.5 and GPT-4o, both at $T=0$ via structured tool calling), the cluster-aware classical baseline is the top method in terms of mean overall coherence; the 5-method quantum-kernel pool and the 7-method classical-kernel pool on matched projection input show no significant differences after Holm correction. Cross-task analysis reveals that LLM coherence rank correlates with alignment-cluster Wikispeedia metrics (Spearman $\rho \approx +0.70$) and anti-correlates with overlap-cluster metrics ($\rho \approx -0.62$). A closed-form theoretical analysis shows that the depth-1 R_Y+CNOT-ring kernel reduces to a classical product-of-cosines kernel order equivalent to RBF, explaining the absence of empirical separation at low depth; deeper encoders break the cancellation but exponentially concentrate kernel values, eroding inter-pair distinguishability. Our results characterise quantum coherence kernels as competitive with classical kernels on the same projected input rather than decisively superior, with the cluster-aware classical baseline retaining a modest advantage attributable to its explicit topical structure. Full article

Narrative extraction builds coherent ordered sequences of documents that trace how concepts develop over time, and is a growing area of information retrieval. In this work we focus on scientific literature, using a corpus of 3549 IEEE visualization research papers (1990–2022). A natural hypothesis is that augmenting embedding-based pathfinding with explicit domain knowledge should improve narrative quality. We present the Knowledge-Coherence Framework (KCF), which integrates structured metadata from OpenAlex into narrative extraction (building on the Narrative Trails algorithm), and conduct a systematic empirical investigation along three axes: (1) the effect of embedding model choice (MiniLM vs. SPECTER), (2) the effect of knowledge augmentation (with and without, plus sensitivity to the knowledge weight $\alpha$), and (3) the reliability of LLM-based evaluation (cross-agreement among 13 large language models). Throughout, mathematical coherence denotes the geometric mean of angular and topic similarity between consecutive documents along a path—an automatic, model-computed quantity inherited from Narrative Maps and Narrative Trails—while narrative quality refers to the LLM-judged construct. Using up to 600 evaluation pairs, we find that embedding model choice has a large effect on mathematical coherence (SPECTER: 0.94 vs. MiniLM: 0.81) and that, contrary to expectations, knowledge augmentation does not improve LLM-judged narrative quality—it slightly decreases it for both embeddings. Notably, the two notions dissociate: SPECTER produces the most mathematically coherent paths, yet MiniLM paths receive the highest LLM narrative-quality scores (5.87 vs. 5.36 out of 10). Alpha sensitivity analysis over five values ($\alpha \in \{0.0,0.3,0.5,0.7,1.0\}$, 500 pairs) confirms that LLM scores remain essentially flat while mathematical coherence steadily declines with increasing knowledge weight. Cross-model evaluation with 13 LLM judges shows high inter-model agreement (median Pearson $r=0.71$), supporting evaluation reliability. The main practical takeaways are that (i) embedding model choice, not knowledge augmentation, is the more consequential design decision, and (ii) mathematical coherence and LLM-judged narrative quality are distinct optimization targets that practitioners should not conflate. Full article

To address the challenges of over-reliance on single-source data, strong spatial heterogeneity in scenic areas, and difficulty in dynamically capturing spatial topology and heterogeneous node relationships in forestry tourism resource carrying capacity prediction, this paper constructs a carrying capacity prediction framework that integrates a multi-source data fusion algorithm with an attention mechanism and a GAT-Transformer model. This framework employs a modal-level multi-head cross-attention mechanism to conditionally weight and fuse multi-source heterogeneous data in the node and time dimensions. It adaptively allocates the contribution of each information source based on the spatiotemporal context, suppressing noise and redundant interference. A weighted spatial graph is constructed based on fusion distance, trail connectivity, and traffic similarity. Neighborhood information is aggregated through a graph attention network to characterize spatial heterogeneity. The spatially enhanced node sequence is then input into a multi-layer Transformer encoder to capture the long-term temporal dependence and periodic patterns of carrying capacity. Finally, the prediction results are output through a regression layer. Systematic experiments were conducted using two years of multi-source observation data from Wulingyuan National Forest Park. The results show that the proposed method has low prediction error and good stability, exhibiting excellent performance in temporal scale adaptation, spatial generalization, and resistance to missing data and noise. Simultaneously, the model structure is lightweight, with low inference latency, achieving a good balance between prediction accuracy, interpretability, and engineering deployment. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load. Full article

Layered side-casting backfilling performed with a trailing suction hopper dredger (TSHD) is widely used in tidal waters, but its continuous moving release can generate a time-varying far-field sediment plume that complicates both backfilling control and environmental impact assessment. To investigate how construction parameters affect far-field sediment dispersion and deposition under side-casting conditions, this study develops a two-dimensional hydrodynamic–sediment coupled numerical model with a mass-conserving moving-source term for a tidally dominated coastal area. Model performance was evaluated against field observations, yielding NRMSE/MRAE values of 0.0787/6.03% for water level, 0.2249/18.30% for current speed, 0.2344/27.10% for suspended-sediment concentration (SSC), and 0.1230/11.10% for deposition thickness; the correlation coefficient for current speed was 0.904. Based on the validated model, scenario analyses were conducted for different combinations of sailing speed and sediment concentration. The results show that far-field plume evolution exhibits pronounced stage-dependent behavior, with the largest affected footprint generally occurring during the late operational period or shortly after source termination. Within the tested parameter space, sailing speed has a stronger influence on the dispersion scale and SSC recovery duration because it controls both the release duration and source sweeping rate. Sediment concentration more directly affects deposition-related responses, including deposited thickness, lateral coverage, and along-track continuity, although its incremental effects weaken in the high-concentration range and remain coupled with sailing speed. Dimensional analysis further suggests that the relative magnitudes of source duration, advection, and settling timescales help explain the differences among scenarios. These results provide a physically based reference for parameter selection and construction planning in layered side-casting backfilling under tidal forcing. Full article

Digital transformation is reshaping construction asset compliance, but fragmented information and weak evidence trails still constrain effective management. This systematic grey literature review (2014–2025) identifies technologies supporting asset management and compliance assurance and compares adoption maturity across the United Kingdom (UK), the United States (US), Singapore, and the Gulf Cooperation Council (GCC). Using multi-channel search strategies and the AACODS appraisal (Authority, Accuracy, Coverage, Objectivity, Date, Significance), 131 records were identified; 92 full texts reviewed; 82 eligible; and 43 sources retained. Coding identified a recurring five-technology “core digital stack”: Building Information Modelling (BIM), Digital Twins (DT), Internet of Things (IoT), Artificial Intelligence/Machine Learning (AI/ML), and Blockchain (BC). Within the retained corpus, BIM and AI/ML were the most frequently referenced technologies, whereas BC was referenced more selectively and discussed mainly for tamper-evident traceability. DT and IoT were typically discussed alongside BIM, while IoT also frequently co-occurred with AI/ML in analytics-led compliance workflows. A (Region × Technology) maturity matrix suggests higher, policy-led maturity where mandates and audit-ready information align with national frameworks (UK, Singapore), and more uneven, project-led adoption in decentralised contexts (US, GCC). Overall, the findings emphasise that effective compliance relies on integrated, evidence-focused digital stacks supported by standardised information governance rather than isolated tools. Full article

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article