Search Results (1,788)

Suspension docking systems offer significant application potential in autonomous underwater docking operations because of their deployment and recovery convenience. This study investigated the interaction between an axisymmetric, underactuated autonomous underwater vehicle (AUV) and a suspended guiding hood docking device (DOCK). The effects of collision velocity, collision location, collision angle, mass, and moment of inertia on the post-collision kinematic states of both bodies are analyzed. Previous studies have typically determined AUV parameters using empirical formulas, whereas few have clearly described the calibration procedure for the hydrodynamic drag coefficients of a suspended guiding hood DOCK. In this study, the hydrodynamic coefficients of both the AUV and the DOCK were determined using STAR-CCM+ and embedded into the ADAMS built-in functions to construct a physically more realistic simulation model. Subsequently, water tank experiments were conducted for suspension docking collisions. The validity of the simulation model was verified by comparing the kinematic states of the DOCK and AUV observed from the simulations and experiments. Based on the established model, the docking dynamics under various operating conditions were simulated. The simulation results indicate that the AUV mass should not exceed twice the mass of the DOCK, and the moment of inertia of the DOCK should be maximized. The risk of suspension docking failure increases significantly when the mooring line length exceeds 40 m, and the negative buoyancy of the DOCK should be at least 300 N. These findings provide critical guidance for improving the success rate of suspension docking operations. Full article

This paper presents a model-based parameter estimation approach for high-voltage circuit breaker operating mechanisms using the dynamic model and stroke curves. First, an equivalent dynamic model of the mechanism is established, and its fidelity is validated through comparison with a virtual prototype model built in ADAMS, confirming the validity of the modeling assumptions. Then, the parameter estimation task is formulated as an optimization problem that minimizes the discrepancy between model predictions and actual stroke curves, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is adopted to search for the optimal parameter set. Subsequently, to ensure estimation reliability, a thorough identifiability analysis is conducted, including sensitivity and correlation analyses, which collectively indicate that the key parameters are well identifiable. Afterward, experimental tests are performed on a 252 kV circuit breaker under various operating conditions, and the results demonstrate that the parameter identification errors are consistently below 6.3%. Finally, we compare the proposed method with three representative data-driven classifiers on the same dataset for a classification task. The comparison shows that our method achieves higher accuracy than others. Furthermore, the proposed method yields physically interpretable parameters directly linked to mechanical components, providing valuable information for condition assessment and maintenance planning in engineering applications. Full article

Inspired by the energy-efficient resonance strategy of the human elbow joint during periodic arm swing, this paper investigates the energy-saving motion and performance of a robotic variable stiffness joint. A modular stiffness adjustment mechanism with continuously adjustable stiffness based on Archimedean spiral grooves is proposed. A co-simulation model using MATLAB (R2022b)/ADAMS (2020) is established, and dynamic equations are derived to reveal the correlation between resonance/anti-resonance frequencies and joint rotational stiffness. Mimicking the biological principle of stiffness-frequency matching, an energy-saving controller leveraging the resonance effect is designed, which includes a motor energy consumption model to quantify losses and an optimization strategy to match the joint rotational stiffness with the load anti-resonance frequency. Simulation results demonstrate that in variable stiffness mode, aligning the system anti-resonance frequency with the task trajectory frequency significantly reduces joint energy consumption, validating the bioinspired approach. In contrast, the high-stiffness (rigid) mode leads to a surge in system energy consumption. Full article

This article develops a systematic reinterpretation of Amartya Sen’s capability approach as a human-centered renewal of the theory of justice grounded in Adam Smith’s moral philosophy. The central claim is that the epistemological foundation of Sen’s theory does not primarily depends on Kantian normativism mediated by Rawls’s theory of justice as fairness, but rather on The Theory of Moral Sentiments, particularly it depends on the concepts of “sympathy”, “impartial spectator”, and justice interpreted as “a social practice”. Through a genealogical reconstruction that brings Aristotle, Adam Smith, and Amartya Sen into dialog, the paper argues that the capability approach can be interpreted as an alternative tradition to Enlightenment theories of justice, because oriented toward human person flourishing and the public evaluation of real freedoms. By overcoming the limits of classical liberal egalitarianism and transcendental institutionalism, the article shows that Sen’s formalization of extended sympathy within social choice theory enables a comparative, pluralistic, and non-dogmatic conception of justice, capable of assessing real social states without recourse to transcendental foundations. In line with the thematic focus of the Special Issue Adam Smith’s Philosophy and Modern Moral Economics, the paper highlights the implications of this interpretation for contemporary moral economics, economic justice, human development, and the role of markets in pluralistic democratic societies. Full article

Accurate, automated delineation of adult diffuse gliomas from multi-parametric magnetic resonance imaging (mpMRI) is central to quantitative neuro-oncology. Volumetric 3D networks dominate the BraTS leaderboard but require expensive GPUs, long training cycles, and provide diminishing returns relative to their compute budget. Slice-wise 2D models, by contrast, discard inter-slice context that is informative for thin tumor rims and small enhancing foci. We introduce GDNet, a 2.5D multimodal MRI segmentation framework for adult glioma evaluated on the BraTS 2024 cohort. GDNet consumes a stack of three adjacent axial slices from the four standard BraTS modalities (T1, T1ce, T2, FLAIR) as a 12-channel input to a compact U-shaped encoder–decoder with Group Normalization and predicts whole tumor (WT), tumor core (TC), and enhancing tumor (ET) masks for the central slice. The training pipeline pairs the 2.5D backbone with: (i) Exponential Moving Average (EMA) of model weights with decay 0.999, (ii) mixed tumor-aware slice sampling (p_tumor = 0.50), (iii) a compound Cross-Entropy + Soft-Dice loss, and (iv) AdamW with warm-up plus cosine annealing under Automatic Mixed Precision. We performed a systematic, step-by-step ablation covering a 2D baseline, EMA + mixed sampling, tumor-centered crop fine-tuning, a GDNet-inspired architectural integration, a region-aware loss, 3-slice and 5-slice 2.5D inputs, and connected-component post-processing, and we report multi-seed results to quantify reproducibility. On the held-out BraTS 2024 test partition, the final 3-slice 2.5D GDNet achieved positive-only Dice scores of 0.791 ± 0.000 (WT), 0.736 ± 0.003 (TC), 0.654 ± 0.004 (ET), and a mean foreground positive-only Dice of 0.820 ± 0.000 across seeds; the all-slice mean foreground Dice exceeded 0.927 ± 0.000. Validation positive-only scores were 0.805 ± 0.002 (WT), 0.757 ± 0.004 (TC), 0.683 ± 0.009 (ET). The inter-seed standard deviation was small for every region (≤0.01 Dice points), indicating low inter-seed variance across the two seeds evaluated; with only two seeds, we regard this as preliminary evidence of training stability rather than a strong reproducibility claim. The ablation isolated EMA + mixed tumor sampling and the 2.5D context window as the dominant sources of improvement; notably, a GDNet-style architectural integration with a region-aware loss did not outperform the simpler 2.5D U-Net on positive-only WT/TC/ET, and light post-processing improved only all-slice Dice. A failure-mode audit found that the residual catastrophic predictions are concentrated on a small minority of diffuse, infiltrative tumors with mass effect. Conclusions: Carefully engineered training strategies, tumor-aware sampling, EMA stabilization, and a modest 2.5D context window recover a substantial fraction of the accuracy of much heavier 3D networks at a fraction of the compute, are reproducible across seeds, and outperform a heavier GDNet-inspired architectural variant on the same data. GDNet is therefore a practical and, pending external validation, potentially clinically deployable framework for multimodal glioma segmentation on workstation-class GPU hardware. Full article

This paper introduces a fractal–fractional SEIR epidemic model based on the Atangana–Baleanu derivative in the Caputo sense augmented by fractal scaling. The fractional order $\alpha \in (0,1]$ captures memory effects while the fractal dimension $\beta \in (0,1]$ accounts for irregular contact networks. We prove global existence, uniqueness, positivity, and boundedness of solutions via fixed-point arguments and establish global Ulam–Hyers stability. An adapted second-order Adams–Bashforth–Moulton predictor-corrector scheme with explicit weights is derived and verified. Numerical simulations across representative $(\alpha ,\beta )$ pairs reveal that decreasing either parameter delays epidemic peaks, reduces peak intensity (with $\beta$ exerting a stronger damping effect), prolongs tails, and induces irregular oscillations—features absent from classical or pure-fractional SEIR models. These results provide a rigorous and reproducible framework for forecasting emerging infections in heterogeneous populations and carry direct implications for targeted public health interventions. Full article

Adam Smith’s system of natural liberty aimed at the happiness and virtue of humankind. Yet the Scottish philosopher also recognised that the system’s internal dynamics could render it unsustainable unless the state intervened to preserve sociability and justice through the education of both the working and the middle and upper classes. This article argues that the educational programme envisaged in The Wealth of Nations entailed the triumph of a pure and rational religion, understood as the conviction that the Supreme Being valued and rewarded virtue alone, thereby reinforcing the sense of duty, together with an awareness of belonging to an impartially conceived, divinely ordered system, fostering humility. Full article

This work presents a practical C++23 implementation of a physics-informed neural network (PINN) for a fractional-order damped oscillator. A fully connected network outputs displacement and velocity, so the governing dynamics are enforced through a compact state-space residual involving first and second time derivatives. Integer-order derivatives are obtained via automatic differentiation, which removes finite-difference noise and preserves smooth, consistent gradients during training. The history-dependent fractional damping term is incorporated using the classical L1 discretization on a uniform time grid, which makes each residual evaluation depend on the entire predicted solution history and naturally captures memory effects. The training objective combines the squared residual norms at collocation points with a strongly weighted initial-condition penalty to control drift and stabilize early iterations. Gradients of the complete objective with respect to all network parameters are computed using reverse-mode automatic differentiation in CppAD (20260000.0) by constructing a scalar loss function of a flat parameter vector, enabling efficient gradient-based optimization. Parameters are updated with the Adam algorithm using bias correction and double-precision moment accumulation for numerical robustness. This implementation includes deterministic parameter packing, explicit size checks, and lightweight diagnostics of boundary values during training, improving reproducibility and debuggability. Overall, the code provides an end-to-end baseline for PINN-based simulation of fractional-order oscillatory systems and can be readily extended to include external forcing, alternative loss weight schedules, and parameter identification from measurement data. Full article

This study investigates a polymorphic underwater vehicle designed to combine long-range cruising with stable underwater operation, reducing dependence on surface support vessels. By introducing a foldable polymorphic structure, the vehicle can switch configurations, including serial and parallel. However, underwater environments often contain obstacles, and the vehicle may collide with them during the folding process. To prevent collisions between the vehicle and surrounding obstacles during the folding process, this paper investigates the motion envelope of the vehicle and examines how motion parameters and mass distribution influence the motion envelope. In this work, the polymorphic underwater vehicle is modeled as a multibody system operating under a neutrally buoyant condition. Based on space robot modeling methodologies and the linear and angular momentum theorems, the equations of motion of the polymorphic underwater vehicle are derived and verified using the Adams software 2020. In summary, the present study establishes a clear relationship between motion parameters, mass distribution, hydrodynamic effects, and the resulting motion envelope of a polymorphic underwater vehicle. The results show that the attitude of the vehicle during the folding process is uniquely determined by the joint angles, and a larger relative speed between the outer and inner folding motions produces a more compact attitude during the folding process. Mass distribution further influences the motion envelope of the vehicle: concentrating mass toward the center of the vehicle shifts the overall motion envelope upward, whereas concentrating mass toward both ends of the vehicle shifts it downward. In addition, hydrodynamic forces introduce an upward velocity component of the vehicle in the vertical direction during the folding process, which leads to an upward shift in the overall center of mass of the vehicle. Full article

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

Background/Objectives: Intranasal tumors are common malignancies in dogs, characterized by locally aggressive behavior and clinical signs such as epistaxis, nasal discharge, and facial deformity. Radiotherapy (RT) is considered the treatment of choice due to anatomical limitations to surgical resection. This study aimed to evaluate clinical outcomes, toxicity, and prognostic factors in dogs with primary malignant intranasal tumors treated with cobalt-60–based megavoltage radiotherapy. Methods: This retrospective study included 40 dogs with histopathologically confirmed primary malignant intranasal tumors treated between September 2018 and February 2025 at a veterinary radiotherapy clinic in Rio de Janeiro, Brazil. Medical records were reviewed for patient demographics, tumor characteristics, treatment protocols, response, toxicity, and survival outcomes. Tumors were staged using modified Adams criteria based on computed tomography. Definitive-intent protocols (n = 32) delivered 48–54 Gy in 10–13 fractions administered three to five times weekly, while palliative protocols consisted of either four fractions of 8 Gy delivered once weekly or five fractions of 4 Gy delivered daily. Results: Adenocarcinoma was the most common histologic subtype (42.5%), and 82.5% of dogs had stage III–IV disease. The objective response rate was 82.5% (CR: 17.5%; PR: 65.0%), with clinical benefit observed in 92.5% of cases. Acute toxicity was frequent but manageable, primarily affecting skin, oral mucosa, and eyes. Overall median progression-free interval (PFI) and survival time (MST) were 382 days and 430 days, respectively. Stage IV disease was significantly associated with shorter survival when compared to stage I-III (MST 345 vs. 1063 days, respectively; p = 0.016). Treatment response was significantly associated with PFI in univariate analysis (p < 0.05). Conclusions: Radiotherapy provided high response rates and meaningful clinical benefit with acceptable toxicity in dogs with malignant intranasal tumors, highlighting the importance of early diagnosis and treatment. Further prospective studies with standardized protocols are warranted. Full article

The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane’s movements. The Neural Network algorithm optimized the crane’s movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane’s structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane’s fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life. Full article

We described tumor response and survival in dogs with modified Adams stage 4 intranasal tumors treated with once-weekly hypofractionated radiation therapy (RT) combined with the radiosensitizer α-sulfoquinovosyl-acylpropanediol (SQAP), and compared linear and volumetric response assessments. Twenty dogs treated with intensity-modulated RT (8 Gy per fraction, once weekly) and concurrent SQAP were included in this retrospective case series. Tumor response was assessed using RECIST-like linear measurements and volumetric analysis on contrast-enhanced computed tomography. Overall survival (OS) was estimated using Kaplan–Meier analysis. Of the 20 dogs, 4 were classified as stage 4a and 16 as stage 4b. The best RECIST-like responses were complete response (CR) in 5 dogs, partial response (PR) in 12, and stable disease (SD) in 4. Volumetric responses were CR in 5 dogs, PR in 11, and SD in 5. No cases demonstrated progressive disease as the best response. The median OS for all dogs was 342 days (95% confidence interval [CI], 206–419 days). Censoring one non-tumor-related death yielded a median OS of 356 days (95% CI, 231–419 days). Exploratory analysis revealed median OS of 393 and 297 days for stage 4a and 4b dogs, respectively. Volumetric assessment appeared more sensitive for detecting tumor regrowth in selected cases. Dermatologic adverse events were limited to alopecia within the radiation field, and no complete vision loss was observed. Seizure activity was documented in eight dogs. In conclusion, once-weekly hypofractionated intensity-modulated RT combined with concurrent SQAP was associated with clinically meaningful survival outcomes in dogs with advanced intranasal tumors. However, because no radiotherapy-alone control group was available, the independent contribution of SQAP to these outcomes could not be determined. Full article

The job shop scheduling problem (JSSP) is a paradigmatic and strongly $\mathcal{NP}$-hard combinatorial optimisation problem that underpins production planning in modern manufacturing systems, and constraint programming (CP) has become one of the leading methodologies for tackling it. However, comparative studies of CP solvers for the JSSP have so far been restricted to a single benchmark family, a single instance-size range, or a single hardware setting, which limits the practical guidance they offer to both researchers and practitioners. This paper presents a controlled empirical evaluation of four state-of-the-art CP solvers—IBM ILOG CP Optimizer, Google OR-Tools (CP-SAT), Hexaly, and OptalCP—on the makespan-minimisation JSSP. The four engines are run with default parameters and a uniform 600 s wall-clock time budget on 332 instances drawn from nine canonical benchmark families (Fisher–Thompson, Lawrence, Adams–Balas–Zawack, Applegate–Cook, Yamada–Nakano, Storer–Wu–Vaccari, Taillard, Demirkol–Mehta–Uzsoy, and Da Col–Teppan), spanning sizes from $6\times 6$ to $1000\times 1000$ operations. OptalCP emerges as the most robust engine overall, certifying optimality on 191 of the 332 instances ($57.5\%$) with the smallest average optimality gap ($3.55\%$), followed by CP Optimizer (166 optima), OR-Tools (144), and Hexaly (116), while Hexaly dominates on industrial-scale problems and produces the bulk of the 22 new best-known upper bounds and one new best-known lower bound reported here. A Friedman test followed by Nemenyi post hoc comparisons confirms that OptalCP attains significantly smaller optimality gaps than the three other engines ($p<0.001$). Solver competitiveness depends sharply on instance size and the $n/m$ ratio, with square instances confirmed as the hardest case. In practical terms, these findings support an instance-aware approach to CP solver selection: OptalCP is the default choice for small to large instances of moderate aspect ratio, whereas Hexaly is preferable for industrial-scale problems with tens of thousands of operations or extreme $n/m$ ratios, where it is the only engine that reliably returns high-quality feasible schedules within the time budget. 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