Search Results (1,509)

This paper studies spacecraft control under communication congestion, multi-source uncertainties, and input constraints. To reduce communication load, a static event-triggered mechanism is used so that transmissions occur only when necessary. Unknown nonlinearities are estimated online by a radial basis function neural network (RBFNN). To address sensor outliers and external disturbances, an event-triggered stubborn composite disturbance observer (ESCDO) is proposed, and sufficient conditions are derived to ensure its globally uniformly bounded stability. Based on this, an MPC-based composite anti-disturbance controller is designed to satisfy input constraints, and conditions are provided to guarantee the uniform bounded stability of the closed loop. Numerical simulations are conducted to demonstrate the effectiveness of the proposed approach. Full article

Air cycle systems, once largely replaced by vapour-compression technologies due to efficiency concerns, are now re-emerging as a viable and sustainable alternative for highly dynamic thermal applications and excel in ultra-low temperature. By using air as the working fluid, these systems eliminate the need for synthetic refrigerants and comply naturally with evolving environmental regulations. This study presents the conceptual design and simulation-based analysis of a novel air cycle machine developed for advanced automotive testing environments. The system is intended to replicate a wide range of climatic conditions—from deep winter to peak summer—through the use of fast-responding turbomachinery and a flexible control strategy. A central focus is placed on the radial turbine, which is designed and evaluated using a modular, open source framework that integrates geometry generation, off-design CFD simulation, and performance mapping. The study outlines a potential operating strategy based on these simulations and discusses a control architecture combining lookup tables with zone-specific PID tuning. While the results are theoretical, they demonstrate the feasibility and flexibility of the proposed approach, particularly the turbine’s role within the system. Full article

A traumatic elbow dislocation that remains unreduced for more than three weeks is considered a neglected elbow dislocation. We report a case of a patient with a neglected elbow dislocation combined with a terrible triad injury (elbow dislocation with fractures of the coronoid process and radial head). Initially, the patient was managed with three weeks of cast immobilization followed by physiotherapy. However, six months after the trauma, he presented to our clinic with severe heterotopic ossification, significant pain, and nearly complete elbow stiffness. An open surgical intervention was performed, involving excision of the heterotopic bone, reduction in the dislocation, and suturing of the anterior capsule to the coronoid process. Given the irreparable fracture of the radial head, radial head arthroplasty was also performed. At 18-month follow-up, the elbow was stable and pain-free, with flexion–extension of 80°, pronation of 85°, and supination of 80°. This case underscores the critical importance of early diagnosis and intervention to prevent long-term complications in neglected elbow dislocations. Full article

Aluminum, one of the most abundant metals found on our planet, plays a crucial role in manufacturing as it is lightweight and resistant to corrosion and has excellent machinability. Of its numerous alloys, Al6061 is one of the most popular alloys used for CNC machining due to its superior mechanical and processing properties. This paper aims to investigate the impact of machining under dry and wet machining conditions. Correspondingly, the impact of dry machining on the material removal rate (MRR) and surface roughness (Ra) of Al6061 was evaluated. Machining was performed on a CNC Lathe. Two rods of Al6061 were used, and a dynamometer was attached to them to measure the radial, thrust, and tangential forces. In wet machining, the coolant used was a mixture of cutting oil and water. Different RPMs, feed rates, and depths of cut were entered into the machine as parameters. And the optimum parameters where found. This research utilizes particle swarm optimization approaches in order to evaluate optimal parameters, in contrast to traditional measurement methods such as contact profilometry or cutting force measurement. The results indicate that surface roughness rises with the depth of cut and feed rate. Ra rises by about 200% when dry machining is conducted at 0.05 mm/rev with increased depths of cut from 0.5 mm to 2.5 mm. In wet machining, the rise is much smaller, approximately 67% at 0.05 mm/rev and 30% at 0.25 mm/rev. Wet machining always produces more finished surfaces, decreasing Ra by 22–25% over dry machining. Wet machining is therefore better suited for achieving high-quality surface finish in Al6061 machining. Full article

Efficient and accurate prediction of transient temperature fields is critical for thermal management of electronic devices with multiple heat sources. In this study, a reduced-order surrogate modeling approach is developed based on proper orthogonal decomposition (POD) and radial basis function (RBF) neural networks. The method maps time-conditioned modal coefficients in a parameter–time space, enabling robust temporal extrapolation beyond the training horizon. A multi-heat-source conduction model typical of electronic packages is used as the application scenario. Numerical experiments demonstrate that the proposed POD–RBF surrogate achieves high predictive accuracy (global MRE < 3%) with significantly reduced computational cost, offering strong potential for real-time thermal monitoring and management in electronic systems. Full article

This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables (input shaft speed, HST ratio, and load), which were used as the training data. A hybrid prediction model combining deep kernel learning and a residual radial basis function surrogate was developed with hyperparameters optimized via Bayesian optimization. For performance verification, the proposed model was compared with Neural Network (NN), Random Forest, XGBoost, Gaussian Process (GP), and Support Vector Regressor (SVR) models trained using 27 samples. As a result, the proposed model achieved the highest prediction accuracy (R² = 0.93, MAPE = 5.94%, RMSE = 4.05). Process, SVM (Support Vector MA). These findings indicate that the proposed approach can be effectively used to predict the overall HST efficiency using minimal data, particularly in situations where experimental data collection is limited. Full article

Background: Non-transected and partially transected peripheral nerve injuries (neuromas-in-continuity) are relatively common but understudied. Their optimal surgical management and expected outcomes remain unclear. We conducted a literature review of surgical repairs in such lesions and illustrate a case to guide decision-making. Systematic searches of PubMed and Google Scholar identified 70 eligible reports (Level I = 2, Level II = 5, Level III = 37, Level IV = 20, Level V = 4). Across studies, neurolysis of NAP-positive lesions often restored antigravity strength, while direct repair or grafting of nonconductive segments yielded meaningful recovery in ~75%. After neurolysis or reconstruction, ~77–92% of brachial plexus/axillary neuromas-in-continuity reached LSUHSC Grade ≥3. Median/ulnar lesions treated with neurolysis, biologic/vascularized coverage, or reconstruction showed reliable pain relief but variable sensory/motor recovery. Radial/PIN lesions improved in some series irrespective of NAPs. Earlier intervention, shorter gaps, distal sites, and younger age correlated with superior outcomes. Meanwhile, prolonged observation risking end-organ atrophy degraded results. Adjuncts such as electrical stimulation and wraps may aid reinnervation or reduce scarring, though high-quality evidence is limited. Conclusions: For non-transected and partially transected PNIs, a pragmatic approach emerges: Observe low-grade injuries with serial examinations. Explore early if recovery stalls (≈3–6 months). Use NAP-guided neurolysis for conductive lesions. Perform tension-free repair or grafting for nonconductive segments, adding anti-adhesive coverage when appropriate. Standardized reporting and prospective trials are needed to refine timing, technique selection, and patient-reported outcomes. Full article

This paper addresses the simultaneous feeder routing and conductor sizing problem in unbalanced three-phase distribution systems, formulated as a nonconvex mixed-integer nonlinear program (MINLP) that minimizes the equivalent annualized expansion cost—combining investment and loss costs—under voltage, ampacity, and radiality constraints. The model captures nonconvex voltage–current–power couplings, $\Delta /Y$ load asymmetries, and discrete conductor selections, creating a large combinatorial design space that challenges heuristic methods. An exact MINLP formulation in complex variables is implemented in Julia/JuMP and solved with the Basic Open-source Nonlinear Mixed Integer programming (BONMIN) solver, which integrates branch-and-bound for discrete variables and interior-point methods for nonlinear subproblems. The main contributions are: (i) a rigorous, reproducible formulation that jointly optimizes routing and conductor sizing; (ii) a transparent, replicable implementation; and (iii) a benchmark against minimum spanning tree (MST)-based and metaheuristic approaches, clarifying the trade-off between computational time and global optimality. Tests on 10- and 30-node rural feeders show that, although metaheuristics converge faster, they often yield suboptimal solutions. The proposed MINLP achieves globally optimal, technically feasible results, reducing annualized cost by 14.6% versus MST and 2.1% versus metaheuristics in the 10-node system, and by 17.2% and 2.5%, respectively, in the 30-node system. These results highlight the advantages of exact optimization for rural network planning, providing reproducible and verifiable decisions in investment-intensive scenarios. Full article

Existing research has made significant strides in UAV formation control, particularly in active localization and certain passive methods. However, these approaches face substantial limitations in electromagnetically silent environments, often relying on strong assumptions such as fully known and stationary emitter positions. To overcome these challenges, this paper proposes a comprehensive framework for bearing-only passive localization and adjustment of UAV formations under strict electromagnetic silence constraints. We systematically develop three core models: (1) a geometric triangulation model for scenarios with three known emitters, enabling unique target positioning; (2) a hierarchical identification mechanism leveraging an angle database to resolve label ambiguity when some emitters are unknown; and (3) a cyclic cooperative strategy, Perceive-Explore-Judge-Execute (PEJE), optimized via an improved genetic algorithm with adaptive discrete neighborhood search (GA-IADNS), for dynamic formation adjustment. Extensive simulations demonstrate that our proposed methods exhibit strong robustness, rapid convergence, and high adjustment accuracy across varying initial deviations. Specifically, after adjustment, the maximum radial deviation of all UAVs from the desired position is less than 0.0001 m, and the maximum angular deviation is within 0.00013°; even for the $30\%R$ initial deviation scenario, the final positional error remains negligible. Furthermore, comparative experiments with a standard Genetic Algorithm (GA) confirm that GA-IADNS achieves superior performance: it reaches stable peak average fitness at the 6th generation (vs. no obvious convergence of GA even after 20 generations), reduces the convergence time by over $70\%$, and improves the final adjustment accuracy by more than $95\%$ relative to GA. These results significantly enhance the autonomous collaborative control capability of UAV formations in challenging electromagnetic conditions. Full article

Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of intelligent medical diagnostic systems. In this work, we propose a new biomedical image recognition in two stages: the first stage is to introduce a new image feature extraction technique using quaternion Legendre orthogonal moments (QLOMs) to extract features from biomedical images. The second stage is to use radial basis function (RBF) neural networks for image classification to know the type of disease. To evaluate our computer-aided medical diagnosis system, we present a series of experiments were conducted. Based on the results of a comparative study with recent approaches, we conclude that our method is very promising for the detection and recognition of dangerous diseases. Full article

This study investigates stochastic bifurcation in a generalized tristable Rayleigh–Duffing oscillator with fractional inertial force under both additive and multiplicative recycling noises. The system’s dynamic behavior is influenced by its inherent spatial symmetry, represented by the potential function, as well as by temporal symmetry breaking caused by fractional memory effects and recycling noise. First, an approximate integer-order equivalent system is derived from the original fractional-order model using a harmonic balance method, with minimal mean square error (MSE). The steady-state probability density function (sPDF) of the amplitude is then obtained via stochastic averaging. Using singularity theory, the conditions for stochastic P bifurcation (SPB) are identified. For different fractional derivative’s orders, transition set curves are constructed, and the sPDF is qualitatively analyzed within the regions bounded by these curves—especially under tristable conditions. Theoretical results are validated through Monte Carlo simulations and the Radial Basis Function Neural Network (RBFNN) approach. The findings offer insights for designing fractional-order controllers to improve system response control. Full article

Objectives: This study aims to report on radial access for transarterial chemoembolization (TACE), comparing right and left accesses in terms of technical effectiveness, safety, operator radiation exposure, and procedural comfort. Methods: In a single-center prospective design, patients were randomized into two groups according to right (R) or left (L) radial access. Primary endpoints were used to assess the efficacy and safety of radial access to perform liver TACE interventions; secondary endpoints were used to compare procedural comfort and operator radiation exposure. Technical efficacy was intended as procedural accomplishment via sole radial access. Safety was assessed in terms of complication occurrence. Operator radiation exposure was monitored according to dosimeters and beam-on time. Patient and operator procedural comfort was investigated using a visual analog scale. Results: A total of 61 patients (17 women and 44 men; mean age 68.4 years) were enrolled. Group R included 32 patients, and group L had 29; all were affected by hepatocellular carcinoma and treated with palliative or bridge-to-transplant intent. Sixteen (26.2%) had abnormal coagulation function. Technical success did not statistically differ between the two groups (96.8% group R vs. 100% group L). No major complications were recorded. While no differences were detected in terms of radiation exposure values and patient comfort, operators were significantly in favor of the right radial artery. Conclusions: In this sample, both right and left radial access were technically effective and safe, without significant differences in operator radiation exposure and patient comfort; considering significantly higher operator comfort with the right approach, right radial artery could be considered the right radial access for liver TACE interventions. Full article

This paper presents the first-of-its-kind full-crown Detached Eddy Simulation (DES) of a radial turbine designed for operation in a transcritical CO₂-based power cycle. The simulation domain contains not only the main blade passage but also the exhaust diffuser and the rotor disk cavities. To ensure accurate simulation of the turbine, two hybrid RANS/LES models, using the Improved Delayed Detached Eddy Simulation (IDDES) approach, are validated in a flow around a circular cylinder at $\mathrm{Re}=3900$, obtaining excellent agreement with other experimental and numerical studies. The turbine simulation was performed using the k-$\omega$-SST-based IDDES model, which was identified as the most appropriate approach for accurately capturing all relevant flow dynamics. Thermophysical properties of CO₂ are modeled with the Span–Wagner reference equation, which was evaluated by a highly efficient spline-based table look-up method. A preliminary assessment of the grid quality in the context of DES is performed for the full-crown simulation, and characteristic flow features of the main passage and cavity flow are highlighted and discussed. Full article

The growing demand for energy and the environmental impacts of fossil fuels have driven the search for sustainable alternatives such as biodiesel. Castor oil stands out as a promising non-edible feedstock but requires optimization strategies to overcome challenges in its conversion to biodiesel. This study developed a predictive model to determine the optimal parameters for homogeneous alkaline or acid transesterification of castor oil, aiming to maximize fatty acid methyl ester (FAME) yield. A dataset of 406 operating conditions from the literature was used to train and evaluate six models: Multilayer Perceptron with logistic sigmoid activation (MLP-logsig), hyperbolic tangent activation (MLP-tansig), Radial Basis Function network (RBF), hybrid RBF + MLP, Random Forest (RF), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLP-tansig achieved the best performance in training, validation, and testing (R > 0.98). However, when combined with a Genetic Algorithm (GA), it generated infeasible parameters. Conversely, the RBF + GA combination yielded results consistent with the literature: molar ratio 19.35:1, alkaline catalyst 1.13% w/w, temperature 50 °C, reaction time 70 min, and stirring speed 548.32 rpm, achieving 100% FAME yield. This approach reduces the need for extensive experimental testing, offering a cost- and time-efficient solution for optimizing biodiesel production. Full article

Ensuring the resilience and efficiency of modern distribution networks is increasingly critical in the presence of distributed energy resources (DERs). This study presents a multi-objective optimization framework based on a Genetic Algorithm (GA) to improve voltage profiles, minimize active power losses, and enhance resilience in a radial distribution network. A simplified 6-bus radial test system with DERs at buses 2, 3, and 4 is considered as a proof-of-concept case study. The GA optimizes control variables, including DER setpoints and network reconfiguration, under operational and thermal constraints. The optimization employs a weighted objective function combining voltage profile improvement, loss minimization, and a resilience penalty term that accounts for bus voltage collapse and branch overloads during DER contingencies. Simulation results demonstrate that the GA significantly improves network performance: the minimum bus voltage rises from 0.92 pu to 0.97 pu, while the total real power losses decrease by 46% (from 55.3 kW to 29.7 kW). Moreover, in the event of a DER outage, the optimized configuration preserves 100% load delivery, compared to 89% in the base case. These findings confirm that GA is an effective and practical tool for enhancing distribution network operation and resilience under high DER penetration. Future work will extend the approach to larger IEEE benchmark systems and time-series scenarios. Full article