Search Results (231)

Accurate maneuvering models are crucial for autonomous ships. Traditional methods such as model tests and CFD analysis require considerable time and cost to construct white-box models, while black-box models lack interpretability. Although system identification using operational data to identify white-box model parameters is efficient, standard maneuvers often lack sufficient excitation signals to yield generalizable models, and random maneuvers are impractical on full-scale ships. This study comparatively examines practical excitation signals that are easily implementable on full-scale ships to obtain informative data. Using pseudo-observation data of a KVLCC2 model ship, hydrodynamic coefficients of the MMG model are identified via a Markov Chain Monte Carlo (MCMC) method to quantify parameter uncertainty while explicitly accounting for observation noise. Among the tested cases, the linearly decreasing rudder-angle signal yielded the most balanced predictive performance for the turning and zig-zag maneuvers. In this numerical case study, the signal covered the full allowable rudder-angle range and generated motions ranging from turning to approximately straight-line behavior. The identified models also retained similar predictive accuracy under a specific synthetic perturbation introduced between the commanded and actual rudder angles. These results indicate that a linearly decreasing rudder-angle signal is a promising candidate for acquiring informative data for MMG model identification. Full article

To improve the prediction reliability of model predictive control under model mismatch and environmental disturbances, this paper proposes a prediction-error-compensated MPC method with a forgetting factor for ship trajectory tracking and collision avoidance. The proposed method is developed within a Frenet-frame virtual ship group framework. A compensation term is introduced to correct the predicted heading-output sequence in the MPC prediction horizon, and a forgetting factor is used to regulate the influence of historical prediction errors on the current compensation term. Candidate trajectories are generated in the Frenet frame and evaluated using a hierarchical cost function to select a feasible obstacle avoidance path. Simulation experiments were conducted using the MMG model of the “Yukun” ship in three types of maritime encounter scenarios (head-on encounter, crossing encounter, and overtaking encounter) and a comprehensive obstacle scenario. The simulation results show that the proposed method can generate feasible local collision avoidance trajectories in complex obstacle environments and typical encounter scenarios, and maintain good trajectory tracking performance. It provides an effective scheme for trajectory tracking and collision avoidance of underactuated ships with great theoretical and practical value. Full article

Amplification of MDM2 is the molecular hallmark of atypical lipomatous tumor/well-differentiated liposarcoma (ALT/WDL) and dedifferentiated liposarcoma (DDL). While fluorescence in situ hybridization (FISH) is widely used for diagnosis, the diagnostic and prognostic value of droplet digital PCR (ddPCR) remains poorly defined. We retrospectively analyzed 341 primary adipocytic tumors, including 85 liposarcomas (22 DDL), using ddPCR to quantify MDM2 copy number variation (CNV). Diagnostic performance was compared with FISH, and associations with clinicopathological variables and outcomes were evaluated using non-parametric tests, ROC analysis, survival analysis, and Firth’s penalized Cox models. Comparison with FISH confirmed the diagnostic utility of ddPCR for detecting MDM2 amplification, while quantitative CNV assessment provided additional prognostic information. CNV values were significantly higher in deep-seated and dedifferentiated tumors and were strongly associated with local recurrence. ROC analysis identified a threshold of 16.85 copies predicting both dedifferentiation and recurrence (AUC 0.982 and 0.942, respectively). Patients with CNV ≥ 16.85 copies had significantly shorter recurrence-free and overall survival. In multivariable analysis, high CNV remained an independent predictor of recurrence (HR 36.6, 95% CI 4.0–4914.2). These findings support ddPCR as a robust method for MDM2 assessment and suggest that quantitative MDM2 copy number may improve both diagnosis and risk stratification in liposarcoma. Full article

Acidic soil stress can limit sunflower productivity by reducing nutrient availability, impairing root development, and affecting early plant establishment, while foliar nutrient application and seed pretreatment may provide additional support under recommended nitrogen fertilization. This study evaluated the potential of selected nutrient-based products to mitigate acidic environment stress in sunflower through a field experiment and a complementary laboratory germination test. The field experiment was conducted in 2025 at Ivankovo, Croatia, on acidic soil (pH KCl 4.47), under a recommended nitrogen fertilization dose of 85 kg N/ha for sunflower production. Foliar treatments included Barrier, Bioplex, Borealg, and Bor-feed, while the control was left without additional foliar treatment. Seed yield, yield components, seed oil and protein content, and oil and protein yield were determined. In parallel, sunflower seeds were pretreated with the same products and germinated under controlled conditions in aqueous solutions with pH values ranging from 3.5 to 8.5. Germination, seedling morphology, fresh biomass, total phenolic content, antioxidant activity, and free proline content were analysed. Under field conditions, foliar treatments significantly affected plant height, head diameter, seed mass per head, seed oil and protein content, and seed yield. Barrier produced the highest seed yield (5.3 t/ha), oil yield (2.8 t/ha), and protein yield (0.8 t/ha), whereas Bor-feed had the highest seed oil content (54.36%) but the lowest seed yield (2.5 t/ha). In the laboratory experiment, average total germination was 92%, with maximum values of 96–97% depending on treatment and pH. Barrier most strongly promoted root, shoot, and total seedling length, while Borealg increased root, shoot, and total fresh mass. Biochemical responses depended on seed pretreatment and pH, with the highest antioxidant activity recorded in Bioplex at pH 7.5 and higher proline accumulation generally observed in untreated seedlings. The highest antioxidant activity was recorded in Bioplex seed pretreatment at pH 7.5 (5.34 mM Fe(II)/g FW), while the highest free proline content was observed in the control at pH 5.5 (7.87 mM/g FW) and pH 3.5 (7.79 mM/g FW), indicating a stronger stress response in untreated seedlings. The present study indicates that selected foliar products and seed pretreatments may support sunflower productivity and early growth under acidic stress conditions, with Barrier showing the most consistent positive response. Full article

To balance safety, real-time performance, and dynamic feasibility in path planning for unmanned surface vehicles (USVs) operating in complex aquatic environments, the study proposes a hybrid planning framework that integrates a multi-scale A* algorithm with the Dynamic Window Approach (DWA) under Maneuvering Modeling Group (MMG) dynamic constraints. Global path optimization incorporates target-oriented neighborhood expansion, obstacle risk constraints, and a maneuverability cost function to comprehensively evaluate path length and collision risk, thereby enhancing the efficiency of the hybrid global-local planning framework. Local trajectory prediction strictly couples the MMG USV motion model with the DWA algorithm. Real-time optimization of these dynamically constrained trajectories ultimately achieves proactive and safe evasion of dynamic obstacles. Compared with the traditional A* + DWA method, the proposed hybrid method reduces path length and computational time by 5.09% and 31.93%, effectively suppressing abrupt heading changes during dynamic obstacle avoidance. This paper provides an efficient path planning strategy that conforms to USV maneuvering constraints for autonomous navigation and dynamic obstacle avoidance. Full article

With the rapid advancement of Maritime Autonomous Surface Ships (MASSs), developing intelligent decision-making systems that ensure navigation safety in complex waters has become a core priority for the maritime industry. To address the limitations of oversimplified ship dynamics, the instability in game-based strategies, and the presence of non-compliant ships in multi-ship encounters, a novel decision-making framework is developed based on Multi-Agent Deep Reinforcement Learning (MADRL). A three-degree-of-freedom (3-DOF) manoeuvring modelling group (MMG) model is incorporated to replace conventional constant-speed assumptions. By explicitly modelling the hydrodynamic forces acting on the hull, propeller, and rudder, the proposed framework captures the intrinsic coupling between the speed and heading, thereby ensuring that the generated manoeuvres conform to the physical and operational constraints. To achieve stable decision-making in multi-ship encounters, an MATD3-based decision-making module is integrated within a Centralised Training and Distributed Execution (CTDE) paradigm. This architecture enables ships to derive robust and decentralised policies, while benefiting from global information during training. In addition, the proposed method demonstrates a promising adaptability in the investigated multi-ship encounter scenarios. Simulations are conducted across a set of encounter scenarios restricted to open waters. The results demonstrate that the proposed framework achieves safe collision avoidance performance while generating COLREGs-consistent behaviours in basic encounters involving standard power-driven vessels under open-water conditions. Full article

Mechanomyography (MMG) records mechanical vibrations generated by active muscle contraction and may provide a simple field-applicable method for detecting post-loading muscle responses. This study aimed to characterize post-loading changes in multidimensional and time–frequency MMG features of the forearm flexor–pronator muscles after a repetitive gripping exercise. Eight healthy volunteers were assessed bilaterally, yielding 16 forearms for exploratory analysis. MMG signals were recorded before loading, immediately after loading, and 24 h after loading from the volar forearm region overlying the superficial flexor–pronator muscle group, anatomically corresponding to the flexor digitorum superficialis. The loading task consisted of repeated gripping using a 30 kg grip trainer. Multiple MMG trials at each time point were averaged within each subject-arm unit. Time-domain, envelope-based, spectral, band-power, wavelet-energy, and exploratory spectrogram-derived band-power features were extracted using a reproducible Python 3.12 pipeline (Google Colaboratory environment). Changes across time points were assessed using Friedman tests, followed by Wilcoxon signed-rank tests with a multiplicity adjustment. After the false discovery rate correction, 11 of 23 conventional MMG features showed significant time effects. These features were concentrated in amplitude- and energy-related domains, including RMS, envelope, peak-to-peak amplitude, signal energy, and wavelet energy. They decreased immediately after loading and increased at 24 h. An exploratory spectrogram analysis showed similar temporal changes in the absolute band power across predefined frequency bands, whereas the relative band-power distribution remained stable. These findings suggest that a multidimensional MMG feature analysis can detect post-loading changes in the superficial forearm flexor–pronator region after a repetitive gripping exercise. Full article

Background: Accurate delineation of ductal carcinoma in situ (DCIS) remains a key challenge in surgical planning. Although contrast-enhanced mammography (CEM) improves lesion detection, its clinical role in informing individualized surgical strategies remains unclear. This study evaluated the impact of CEM on preoperative assessment and surgical planning, with particular emphasis on whether its effect varies across patient subgroups. Methods: This multicentre pilot observational cohort study included 102 patients: 51 prospective patients undergoing preoperative CEM and mammography (MMG) and 51 retrospective controls assessed with MMG alone. Imaging-derived lesion size and planned resection volume were compared with pathological size using correlation analysis, intraclass correlation coefficient (ICC), and Bland–Altman methods. Surgical outcomes were assessed, and subgroup analyses explored differences according to CEM enhancement status. Results: CEM showed improved correlation with pathological DCIS size compared with MMG (ρ = 0.54 vs. 0.37), with the strongest agreement in CEM-positive lesions (ρ = 0.67; ICC 0.745). However, its clinical impact was not uniform. At the population level, CEM did not significantly change planned resection volume or surgical thresholds. In contrast, in CEM-positive patients, CEM was associated with larger planned resections, proportional to pathological tumor burden. Reoperation rates were lower in the CEM cohort (5.9% vs. 17.6%), without statistical significance, and margin status was comparable. Conclusions: The impact of CEM on surgical planning in DCIS is heterogeneous and largely confined to patients with enhancing lesions. These findings suggest that the value of CEM may lie in its selective use, where it can refine assessment of disease extent in specific subgroups rather than in routine application. Further studies incorporating predictive approaches are needed to support risk-adapted imaging strategies. Full article

Background: With the deepening coupling of multi-microgrids (MMGs) and transportation systems in smart cities, maintaining frequency stability under extreme conditions increasingly relies on vehicle-to-grid (V2G) flexibility. However, existing V2G dispatch strategies often overlook the noticeable battery degradation caused by high-frequency regulation and the vulnerability of extensive communication networks to false data injection attacks (FDIAs), while the high-dimensional coordination of EV routing and discharging makes classical algorithms struggle to converge. Methods: To address these challenges, this study proposes a quantum-empowered degradation-aware V2G coordination framework for smart-city MMGs considering communication security and user travel demands. At the physical layer, an equivalent RC circuit-based battery degradation model and a traffic flow model are established to quantify capacity loss and travel delays. At the cyber layer, quantum key distribution (QKD) ensures unconditionally secure communication, while a quantum reinforcement learning (QRL) algorithm is developed to achieve fast convergence in high-dimensional multi-objective optimization. Results: Simulation results demonstrate that the proposed framework completely immunizes the system against FDIAs, effectively suppresses frequency fluctuations, and significantly reduces battery degradation costs while preserving user mobility. Conclusions: This framework provides a highly secure and user-friendly pathway for resilient smart-city frequency regulation. Full article

Background: Accurate recognition of upper-limb force levels is important for wearable movement monitoring and rehabilitation engineering, yet the value of combining surface electromyography (sEMG) and mechanomyography (MMG) for shoulder force classification remains incompletely characterized. Methods: Ten healthy adults performed right shoulder abduction at four maximum voluntary contraction (MVC)-normalized force levels of approximately 10%, 30%, 60%, and 90% MVC. Signals were synchronously collected from the middle deltoid at 1000 Hz and segmented using 500 ms windows with a 150 ms stride. The evaluated classifiers were logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), support vector machine with a radial basis function kernel (SVM-RBF), random forest (RF), extremely randomized trees (ET), histogram-based gradient boosting decision tree (HGBDT), and multi-layer perceptron (MLP). Models were evaluated using group-aware five-fold cross-validation at the action-trial level. Results: The dataset contained 12,231 windows from 30 action-trial groups. HGBDT achieved the best performance, with an accuracy of $0.904\pm 0.023$, macro-F1 score of $0.911\pm 0.018$, quadratic weighted Cohen’s kappa of $0.910\pm 0.038$, and mean absolute grade error of $0.137\pm 0.042$. Fusion increased macro-F1 from $0.821\pm 0.030$ for sEMG-only and $0.771\pm 0.015$ for MMG-only to $0.911\pm 0.018$. Conclusions: These internally validated findings support the complementary value of sEMG and MMG for MVC-normalized shoulder force-level classification in healthy adults. Subject-independent and patient-level validation is required before clinical rehabilitation use. Full article

Reinforced concrete frame structures (RCFSs) subjected to strong seismic excitation may enter a metastable semi-ruin state before global collapse, characterized by severe local damage, degraded stability, and high secondary collapse risk. However, systematic experimental investigations and quantitative identification techniques for this critical transitional state are still lacking in existing seismic engineering literature, forming a notable research gap for post-earthquake safety evaluation. To investigate this critical transition, a Digital Image Correlation (DIC)-assisted shaking table test was conducted on a 1/25-scale RCFS specimen derived from an earthquake-damaged exterior-corridor teaching building, using the Wolong ground motion recorded during the 2008 Wenchuan earthquake as input. DIC was employed to track the full-field evolution of cracking, through-crack development, and concrete cover spalling under incremental seismic loading. Four local damage indices—crack line density (CLD), crack propagation rate (CPR), through-crack ratio (TCR), and concrete spalling ratio (CSR)—were extracted and evaluated with the inter-story drift ratio (IDR) to quantify local-to-global degradation. The results show that visible cracks initiated at PGA = 0.3 g, while accelerated crack propagation occurred at 0.7–0.8 g, with CPR peaks of 1187.5 and 1140 mm/g, respectively. At 0.5–1.0 g, the crack number increased from 13 to 26, total crack length reached 0.443 m, CLD increased to 3.9 × 10⁻⁴, and TCR reached 37.04%. At 1.1–1.5 g, crack development approached saturation, with total crack length of 0.552 m, maximum TCR of 63.6%, and CLD of 4.8 × 10⁻⁴. Under ultimate excitation of 1.6–1.8 g, the crack number stabilized at 33–34, TCR remained around 63%, cumulative spalling area reached 1026 mm², CSR reached 0.015, and the third-floor IDR approached the 1/50 elastoplastic limit. Severe through-cracking, reinforcement exposure, concrete spalling, and residual inclination indicated the onset of the semi-ruin state. The proposed multi-index framework provides quantitative support for semi-ruin-state identification and post-earthquake secondary collapse risk assessment of RCFSs. Full article

Human muscle anatomy consists of multiple layers, each contributing to movement through complex patterns of activation. Conventional non-invasive sensing techniques, such as surface electromyography (sEMG) and mechanomyography (MMG), primarily capture aggregate muscle activity and provide limited depth-dependent information. As different movements may involve distinct combinations of superficial and deeper muscles, access to depth-dependent information could improve the discrimination of motion patterns that are difficult to distinguish using surface measurements alone. To address this limitation, we developed an optical sensor capable of depth-sensitive measurement using near-infrared light. The sensor comprises a light source and an array of photodetectors arranged at six source–detector distances (SDDs) ranging from 12 to 48 mm within a compact wearable module. Two experiments were conducted to evaluate the sensor. First, depth sensitivity was investigated using Monte Carlo simulations and phantom experiments, demonstrating distinct sensitivity profiles for different SDDs and providing preliminary evidence of depth-dependent sensing. Second, the sensor was attached to the forearm to measure signals during nine hand and wrist movements. Machine learning models were evaluated for motion classification, with Linear Discriminant Analysis (LDA) achieving the highest performance. Using all six SDD channels, an average classification accuracy of 87.5% was achieved across 10 subjects. An ablation study evaluating all 63 possible channel combinations further showed that classification performance improved systematically with the inclusion of multiple SDD channels, indicating that measurements obtained at different sensing depths provide complementary information for motion discrimination. These results demonstrate the feasibility of multi-SDD optical sensing for capturing depth-dependent physiological information and highlight its potential as a compact, non-invasive sensing approach for wearable human–machine interface applications. Full article

High penetration of wind and photovoltaic (PV) generation increases renewable uncertainty and real-time balancing pressure in active distribution networks. To address this problem, this paper proposes a two-stage robust optimization method for day-ahead and real-time scheduling of a flexibly interconnected multi-microgrid (MMG) system enabled by a flexible interconnection device (FID). The proposed framework jointly optimizes power purchase from the upper-level distribution network, micro-gas turbine output, energy storage system (ESS) operation, and FID-based bidirectional power exchange, thereby coordinating local temporal flexibility and inter-microgrid spatial flexibility. A polyhedral uncertainty set is used to model wind and PV forecast errors, and the problem is solved by the column-and-constraint generation (C&CG) algorithm. Case studies on a two-microgrid system show that, compared with independent operation under traditional robust optimization, the proposed method reduces real-time balancing cost, wind and PV curtailment, and total operating cost by 98.96%, 95.84%, and 0.59%, respectively. Sensitivity analysis further verifies the economy–robustness trade-off under different uncertainty budgets and forecast deviation levels. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

This study aimed to assess how various remineralizing agents affect the demineralized enamel calcium/phosphorus ions (Ca/P) ratio and micro-tensile bond strength (μTBS) of orthodontic adhesive modified by Sepiolite nanoparticles (Sep-NPs). In addition, rheological properties and degree of conversion (DC) of the adhesive were investigated. One hundred and forty-four human premolars underwent a cariogenic challenge to induce artificial demineralization. Based on the remineralizing agents used, the samples were divided into four categories: silver diamine fluoride (SDF), rosmarinic acid (RMA), ROCS Medical Mineral Gel System (ROCS MMG), and control. The Ca/P ratio was evaluated using energy-dispersive X-rays. Thirty samples were divided into two subgroups: unmodified adhesive and 1% Sep-infiltrated adhesive. Brackets were bonded, and the μTBS was evaluated. Scanning electron microscopy was used to evaluate the resin–bracket interface. The modified and unmodified adhesives were subjected to DC and rheological testing. The Ca/P ion ratio was highest in the ROCS-MMG group and lowest in the no-remineralization group. Group 3B (ROCS MMG + SepNPs-Orthodontic adhesive) samples displayed the highest bond strength. The lowest μTBS was observed in Group 4A (no remineralization + orthodontic adhesive). ROCS MMG conferred the greatest improvement in µTBS and Ca/P ratio before bracket bonding, followed by SDF, whereas RMA did not enhance bonding outcomes. Sep-NP incorporation at 1% improved µTBS but compromised DC and rheological properties, necessitating concentration optimization before clinical application. Full article